Public speaking and engagement (since 2020)
A list of major speaking engagements (divided into conferences, workshops/summer schools, and guest lectures) can be found below.
| Type | Venue/organizer | Event name | Event type | Title | Event date | Year | URL | Description | Abstract | Recording | Flag 1 | Flag 2 | Flag 3 | Flag 4 | Flag 5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Tutorial | ICML | Conference | Synthetic Healthcare Data Generation and Assessment: Challenges, Methods, and Impact on Machine Learning | 2021/07/19 | 2021 | https://www.vanderschaar-lab.com/icml-2021-tutorial-synthetic-healthcare-data-generation-and-assessment/ | 1 | ||||||||
| Keynote | University of Oxford | Medical Image Understanding and Analysis (MIUA) | Conference | The Role of Imaging in Machine Learning for Healthcare | 2021/07/13 | 2021 | https://www.miua2021.com/#programme | 1 | |||||||
| Keynote | Cancer and Primary Care Research International Network | Ca-PRI Conference | Conference | Utilising the power of AI to promote earlier detection of cancer in primary care | 2021/06/10 | 2021 | https://www.ed.ac.uk/usher/cancer-primary-care-research-international-network/conferences/online-conference-2021/programme | 1 | |||||||
| Invited talk | Copenhagen Bioscience Cluster | Copenhagen Bioscience Conference | Conference | Why medicine is creating exciting new frontiers for machine learning | 2021/05/04 | 2021 | https://cph-bioscience.com/en/events/cbc21-2may2021 | 1 | |||||||
| Keynote | MedTech UCL | AI in Medicine Series | Conference | Why medicine is creating exciting new frontiers for machine learning | 2021/01/25 | 2021 | https://uclmed.tech/project/prof-mihaela-van-der-schaar-why-medicine-is-creating-exciting-new-frontiers-for-machine-learning/ | 1 | |||||||
| Invited talk | European Society of Intensive Care Medicine | ESICM LIVES Conference | Conference | Transforming Intensive Care Medicine through Artificial Intelligence and Machine Learning | 2020/12/07 | 2020 | https://www.vanderschaar-lab.com/events/esicm-lives-2020-presentation-and-qa/ | 1 | |||||||
| Tutorial | ICML | Conference | Machine Learning for Healthcare: Challenges, Methods, and Frontiers | 2020/07/13 | 2020 | https://www.vanderschaar-lab.com/icml-2020-machine-learning-for-healthcare-challenges-methods-and-frontiers/ | Part 1 Part 2 Part 3 Part 4 | 1 | |||||||
| Keynote | Cambridge Centre for Data-Driven Discovery | C2D3 Virtual Symposium | Conference | AutoML: powering the new human-machine learning ecosystem | 2020/10/21 | 2020 | https://www.c2d3.cam.ac.uk/events/c2d3-virtual-symposium-2020 | 1 | |||||||
| Keynote | ICLR | Conference | Machine learning: changing the future of healthcare | 2020/04/29 | 2020 | https://iclr.cc/virtual_2020/speaker_5.html | View full video | 1 | |||||||
| Keynote | London School of Hygiene & Tropical Medicine | Centre for Statistical Methodology Symposium | Conference | Transforming medicine through Artificial Intelligence-enabled healthcare pathways | 2019/11/12 | 2019 | https://www.lshtm.ac.uk/newsevents/events/centre-statistical-methodology-symposium | 1 | |||||||
| Invited talk | Machine Learning for Healthcare | Conference | Learning Engines for Healthcare: Transforming Medicine through AI-enabled Healthcare Pathways | 2019/08/09 | 2019 | https://www.mlforhc.org/2019-conference | 1 | ||||||||
| Invited talk | The Alan Turing Institute | AI for Social Good | Conference | Machine learning and data science for medicine: a vision, some progress and opportunities | 2018/02/12 | 2018 | https://www.vanderschaar-lab.com/ai-for-social-good-machine-learning-and-data-science-for-medicine/ | 1 | |||||||
| Plenary | International Federation of Automatic Control | System Identification (SYSID) | Conference | Quantitative epistemology: conceiving a new human-machine partnership | 2021/07/16 | 2021 | https://www.sysid2021.org/plenary-speakers | 1 | |||||||
| Tutorial | IEEE Communications Society | International Conference on Communications (ICC) | Conference | Online learning for wireless communications: theory, algorithms, and applications | 2021/07/14 | 2021 | https://icc2021.ieee-icc.org/program/tutorials#tut-16 | 1 | |||||||
| Keynote | HelmHoltz AI | Helmholtz AI Virtual Conference | Conference | Why medicine is creating exciting new frontiers for machine learning | 2021/04/15 | 2021 | https://www.helmholtz.ai/themenmenue/latest/events/helmholtz-ai-virtual-conference-2021/index.html | 1 | |||||||
| Keynote | Open Data Science | ODSC EAST 2021 | Conference | Why Medicine is Creating Exciting New Frontiers for Machine Learning | 2021/04/01 | 2021 | https://www.vanderschaar-lab.com/events/odsc-east-2021/ | 1 | |||||||
| Keynote | ICM Centre for Neuroinformatics | Computational approaches for ageing and age-related diseases (CompAge) | Conference | Transforming healthcare through machine learning | 2020/09/01 | 2020 | https://neuroinformatics.icm-institute.org/conferences/compage-2020/ | 1 | |||||||
| Keynote | IEEE Computer Society/Circuits and Systems/Communications Society/Signal Processing Society | International Conference on Multimedia and Expo (ICME) | Conference | A nationally-implemented AI solution for Covid-19: addressing capacity planning, risk assessment, treatment effects and outcomes | 2020/07/07 | 2020 | https://www.2020.ieeeicme.org/www.2020.ieeeicme.org/index.php/prof-mihaela-van-der-schaar/index.html | 1 | |||||||
| Keynote | ACM | International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) | Conference | Learning Engines for Networks, Healthcare and Beyond | 2019/07/03 | 2019 | https://www.sigmobile.org/mobihoc/2019/keynotes.html | 1 | |||||||
| Keynote | IAPR | International Conference on Pattern Recognition (ICPR) | Conference | AutoML and interpretability: powering the machine learning revolution in healthcare | 2021/01/13 | 2021 | https://www.micc.unifi.it/icpr2020/index.php/mihaela-van-der-schaar/ | 1 | |||||||
| Keynote | International Federation of Operational Research Societies | IFORS 2021 | Conference | Quantitative epistemology: conceiving a new human-machine partnership | 2021/08/26 | 2021 | https://www.ifors2021.kr/sub02/sub08.php | 1 | |||||||
| Plenary | IEEE | International Conference on Image Processing (ICIP) | Conference | From image processing to machine learning in healthcare | 2021/09/20 | 2021 | https://2021.ieeeicip.org/PlenarySpeakers.asp | 1 | |||||||
| Panel | London Tech Week | The AI Summit London | Conference | How is The Ecosystem for AI-ML and Medicine Poised to Advance ‘Scientific Superpower’ Status for The UK? | 2021/09/23 | 2021 | https://app.swapcard.com/event/the-ai-summit-london-1/planning/UGxhbm5pbmdfNDU0NzMw | 1 | |||||||
| Invited talk | GDR | GDR Statistics and Health 2021 | Conference | Transforming Healthcare Delivery through Machine Learning | 2021/10/21 | 2021 | http://gdr-stat-sante.math.cnrs.fr/spip/spip.php?article125 | 1 | |||||||
| Keynote | Technische Universität München | 2021 Munich Digital Healthcare Summit | Conference | Why is medicine creating new frontiers for AI? | 2021/11/12 | 2021 | https://www.digitalhealthsummit.de/ | 1 | |||||||
| Invited talk | British Neuroscience Association | Festive Symposium 2021 | Conference | Quantitative epistemology: how machine learning can help humans become better decision-makers | 2021/12/13 | 2021 | https://www.bna.org.uk/mediacentre/events/festive-symposium-2021/ | 1 | |||||||
| Invited talk | The Alan Turing Institute | Interpretability, safety, and security in AI | Conference | From interpretability to a new human-machine partnership | 2021/12/14 | 2021 | https://www.turing.ac.uk/events/interpretability-safety-and-security-ai | 1 | |||||||
| Keynote | INSTICC | International Conference on Pattern Recognition Applications and Methods (ICPRAM) | Conference | Machine Learning for Medicine and Healthcare | 2022/02/03 | 2022 | https://icpram.scitevents.org/KeynoteSpeakers.aspx#1 | The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives. Contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods are especially encouraged. | Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19! In this talk I will show how machine learning is transforming medicine and how medicine is driving new advances in machine learning, including new methodologies in time-series, causal inference, interpretable and explainable machine learning, as well as the development of new machine learning areas - quantitative epistemology. | View | 1 | ||||
| Tutorial | AAAI | Conference | Time Series in Healthcare: Challenges and Solutions | 2022/02/23 | 2022 | https://aaai.org/Conferences/AAAI-22/aaai22tutorials/#mq4 | The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. AAAI-22 will have a diverse technical track, student abstracts, poster sessions, invited speakers, tutorials, workshops, and exhibit and competition programs, all selected according to the highest reviewing standards. | Time series datasets such as electronic health records (EHR) and registries represent valuable (but imperfect) sources of information spanning a patient’s entire lifetime of care. While learning from temporal data is an established field and has been covered in a number of prior tutorials, the healthcare domain raises unique problems and challenges that require new methodologies and ways of thinking. Perhaps the most common application of time series is forecasting. While we will discuss state-of-the-art approaches for disease forecasting, we will also focus on other important problems in time series, such as time-to-event or survival analysis, personalized monitoring, and treatment effects over time. These topics will be introduced in the context of healthcare, but they have broad applicability to other domains beyond medicine. In addition, we will explore several characteristics that are necessary to make AI and machine learning models as useful as possible in the clinical setting. We will discuss automated machine learning and we will address the challenges of understanding and explaining machine learning models as well as uncertainty estimation, both of which are critical in high-stakes scenarios such as healthcare. We will aim for minimal required prerequisite knowledge. However, we will assume basic knowledge of standard machine learning methods (e.g. MLPs, RNNs). While our tutorial will include some detailed explanations of machine learning techniques, significant focus will be placed on the problem areas, their unique challenges, and ways of thinking to overcome these. | 1 | ||||||
| Keynote | AISTATS | Conference | Using ML to discover the underlying models of medicine | 2022/03/30 | 2022 | http://aistats.org/aistats2022/ | Since its inception in 1985, AISTATS has been an interdisciplinary gathering of researchers at the intersection of artificial intelligence, machine learning, statistics, and related areas. | 1 | |||||||
| Invited talk | Aviesan, ITMO, PMN, TS | 6th Scientific Day on Technological Innovation | Conference | Using ML to discover the underlying models of medicine | 2022/04/01 | 2022 | https://www.insb.cnrs.fr/fr/evenement/symposium-walk-through-uses-ai-experimental-biology-and-bio-medicine | The aim of this joint day will be to inform the French scientific community about advances in the field of artificial intelligence and its use in biomedical research. It intended at all research actors, scientists, clinicians, academics and private researchers. | 1 | ||||||
| Keynote | Pistoia Alliance | Driving Patient Centricity in R&D Conference | Conference | Using ML to discover the governing equations of medicine | 2022/04/06 | 2022 | https://www.pistoiaalliance.org/eventdetails/spring-conference-2022/ | The Pistoia Alliance’s annual spring conference will focus on Patient Centricity in R&D. Through a series of keynote presentations, focused discussions, and breakouts we will look at the challenges and opportunities for embedding patient centricity within the biopharma value chain—from early discovery right through to post commercialization and its potential for real-world data insights. | 1 | ||||||
| Seminar | Cambridge Centre for Data-Driven Discovery | CCBI/C2D3 Annual Computational Biology Symposium 2022 | Conference | Using ML to discover the underlying models of medicine | 2022/05/18 | 2022 | https://www.c2d3.cam.ac.uk/events/ccbic2d3-annual-computational-biology-symposium-2022 | 1 | |||||||
| Invited talk | Nature/Helmholtz | Bioengineering Solutions for Biology and Medicine | Conference | Using machine learning to turn medicine from an art to a science | 2022/07/05 | 2022 | https://bioeng2022.helmholtz-muenchen.de/ | Bioengineering Solutions for Biology and Medicine 2022 will highlight the latest impactful innovations in bioengineering and artificial intelligence, with a focus on technologies that promise tangible solutions for urgent medical needs. | TBD | 1 | |||||
| Keynote | Philips | Global Data & AI Conference | Conference | Machine Learning for Healthcare | 2022/06/16 | 2022 | TBD | OCUPAI+ 2022 is Philips Global Data & AI Conference. A platform to discuss current Data and AI practices and applications at Philips around the globe. This 4th edition of OCUPAI will take place from June 14-16, 2022, hosted by the Data & AI CoE with active participation from Data & AI experts in Research, Businesses, and Functions, as well as business leaders. | TBD | 1 | |||||
| Keynote | (Various) | IJCAI-ECAI 2022 | Conference | Panning for insights in medicine and beyond: New frontiers in machine learning interpretability | 2022/07/28 | 2022 | https://ijcai-22.org/keynote-speakers/ | international gathering of researchers in AI | international gathering of researchers in AI | 1 | |||||
| Keynote | Association for Uncertainty in Artificial Intelligence (AUAI) | The 38th Conference on Uncertainty in Artificial Intelligence (UAI) | Conference | Augmenting human skill using machine learning: Going beyond inverse reinforcement learning | 2022/08/02 | 2022 | https://www.auai.org/uai2022/ | We aim to foster a community and an environment that recognizes and respects the inherent worth of every person. Such an environment is essential for the open exchange of ideas, the freedom of thought and expression, and respectful scientific debate at the conference. | TBD | 1 | |||||
| Keynote | ELLIS Unit Alicante Foundation | ELLIS Doctoral Symposium 2022 | Conference | Panning for insights in medicine and beyond: New frontiers in machine learning interpretability | 2022/09/21 | 2022 | https://ellisalicante.org/eds2022/ | The ELLIS Doctoral Symposium is an annual conference for ELLIS PhD students to meet in person and share knowledge about Machine Learning. | TBD | 1 | |||||
| Invited talk | Romanian Healthcare Conference | Conference | Machine Learning for Healthcare: Current solutions, Opportunities and New frontiers | 2022/10/14 | 2022 | https://patientexperience.ro/#speakers | TBD | TBD | 1 | ||||||
| Keynote | (Various) | AMLD Africa 2022 | Conference | Panning for insights in medicine and beyond: New frontiers in machine learning interpretability | 2022/11/03 | 2022 | https://appliedmldays.org/events/amld-africa-2022 | AMLD Africa 2022 includes 3 days of talks, tutorials & workshops, on Machine Learning and Artificial Intelligence with top speakers from industry and academia. | In this keynote, I describe an extensive new framework for ML interpretability which enables us to turn black-box machine learning methods into white boxes. This framework allows us to unravel underlying governing equations from data, enabling scientists to make new discoveries. Finally, I will introduce our extensive github for ML interpretability: https://github.com/vanderschaarlab/Interpretability | 1 | |||||
| Keynote | Danish Data Science Academy | Danish Data Science 2022 | Conference | TBD | 2022/11/07 | 2022 | https://ddsa.dk/danishdatascience2022/ | Danish Data Science 2022 is a two-day conference with technical talks in the fields of Machine Learning, Data Quality, Generative Models, Algorithms, AI and much more. | TBD | 1 | |||||
| Keynote | (Various) | VCIP2022 | Conference | New frontiers in machine learning interpretability | 2022/12/14 | 2022 | http://www.vcip2022.org/ | VCIP 2022 will carry on this tradition of VCIP in disseminating the state of art of visual communication technology, brainstorming and envisioning the future of visual communication technology and applications. The main theme would be new media, including VR, point cloud capture and playback, and new visual processing tools including deep learning for intelligence distilling in visual information pre- and post-processing such as de-blurring, super resolution, 3D understanding, and content based image enhancement. | Medicine has the potential to be transformed by machine learning (ML) by addressing core hallenges such as time-series forecasts, clustering (phenotyping), and heterogeneous treatment effect estimation. However, to be embraced by clinicians and patients, ML approaches need to be nterpretable. So far though, ML interpretability has been largely confined to explaining the predictions of static classifiers. In this keynote, I describe an extensive new framework for ML interpretability. This framework allows us to 1) interpret ML ethods for time-series forecasting, clustering phenotyping), and heterogeneous treatment effect estimation sing feature and example-based explanations, 2) rovide personalized explanations of ML methods with eference to a set of examples freely selected by the user, and 3) autonomously (re)discover known scientific concepts using concept activation regions, which are generalizations of concept-based explanations. To learn more about our work in this area - see our website dedicated to this topic - https://www.vanderschaar-lab.com/interpretable-machine-learning/ and our github - https://github.com/vanderschaarlab/Interpretability | 1 | |||||
| Keynote | (Various) | IEEE Metaverse-2022and 2022 IEEE Smart World Congress | Conference | The future of healthcare in the metaverse | 2022/12/16 | 2022 | http://www.ieee-smart-world.org/index.php | IEEE Smart World 2022 aims to provide a high-profile, leading-edge platform for researchers and engineers to exchange and explore state-of-art advances and innovations in graceful integrations of Cyber, Physical and Social Worlds with Ubiquitous Intelligence. | In this keynote, I will describe my vision of how the Metaverse will transform healthcare. By applying machine learning and AI on data from a variety of devices and sensors, we can better monitor and treat patients at home, in hospitals and in the clinic, and enable patients and clinicians to interact in completely new ways in the Metaverse on the basis of the derived analytics.The Metaverse will also allow AI-enabled avatars to join multidisciplinary clinical teams, creating more efficient and more advanced health delivery systems. Finally, I will outline a vision of how national and international healthcare systems can interact and be transformed and how clinical trials can be conducted and augmented in the Metaverse. | 1 | |||||
| Keynote | UiT Machine Learning Group and Visual Intelligence | Northern Lights Deep Learning Conference 2023 | Conference | AI for Science: Discovering diverse classes of equations in medicine and beyond | 2023/01/10 | 2023 | https://www.nldl.org/ | Deep learning is an emerging subfield in machine learning that has in recent years achieved state-of-the-art performance in image classification, object detection, segmentation, time series prediction and speech recognition to name a few. This conference will gather researchers both on a national and international level to exchange ideas, encourage collaborations and present cutting-edge research. | Artificial Intelligence (AI) offers the promise of revolutionizing the way scientific discoveries are made and significantly accelerating their pace. This is important for numerous fields of study, including medicine. In this talk, I will present our research on AI for science over the past few years. I will start by briefly showing how we can discover closed-form prediction functions from cross-sectional data using symbolic metamodels. Then, I will introduce a new method, called D-CODE, which discovers closed-form ordinary differential equations (ODEs) from observed trajectories (longitudinal data).This method can only describe observable variables, yet many important variables in medical settings are often not observable. Hence, I will subsequently present the latent hybridisation model (LHM) that integrates a system of ODEs with machine-learned neural ODEs to fully describe the dynamics of the complex systems. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities. To solve these challenges, I will then present Neural Laplace, with which we can learn diverse classes of differential equations in the Laplace domain. I will conclude by presenting next research frontiers, including recent work on discovering partial differential questions from data (D-CIPHER). While these works are applicable in numerous scientific domains, in this talk I will illustrate the various works with examples from medicine, ranging from understanding cancer evolution to treating Covid-19. This work is joint work with Zhaozhi Qian, Krzysztof Kacprzyk and Sam Holt. | 1 | |||||
| Keynote | Helmholtz Munich | International Symposium on AI for Health | Conference | AI for Science: Discovering diverse classes of equations in medicine and beyond | 2023/01/16 | 2023 | https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fevents.hifis.net%2Fevent%2F605%2F&data=05%7C01%7Cpt374%40universityofcambridgecloud.onmicrosoft.com%7C9e1364f4422546d8767608daf3131cd2%7C49a50445bdfa4b79ade3547b4f3986e9%7C1%7C0%7C638089560659190413%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=P3iNAj6HUxBCgdVCgohqJft8Tj%2FiIPj1QbzwzexKdSE%3D&reserved=0 | TBD | TBD | 1 | |||||
| Keynote | Statlearn conference in Montpellier | Conference | Adaptive Clinical Trials using sequential decision making | 2023/04/06 | 2023 | https://statlearn.sciencesconf.org/ | Statlearn is a scientific workshop held every year, which focuses on current and upcoming trends in Statistical Learning. | TBD | 1 | ||||||
| Keynote | (Various) | Colloquium on 'Fundamental challenges in causality' | Conference | Causal Deep Learning | 2023/05/11 | 2023 | https://quarter-on-causality.github.io/challenges/#speakers | The colloquium on Fundamental Challenges on Causality (FunCausal) aims to bring together researchers interested in causality and willing to discuss novel approaches to causal discovery and causal inference. | TBD | 1 | |||||
| Keynote | (Various) | XAI-Healthcare 2023 | Conference | New frontiers in machine learning interpretability | 2023/06/15 | 2023 | https://www.um.es/medailab/events/XAI-Healthcare/ | The purpose of XAI-Healthcare 2023 event is to provide a place for intensive discussion on all aspects of eXplainable Artificial Intelligence (XAI) in the medical and healthcare field. | TBD | 1 | |||||
| Keynote | University of Bonn | Industry Symposium on "AI in the Life Sciences" | Conference | Transforming Healthcare: A Journey Through 5 Groundbreaking ML Innovations | 2023/06/26 | 2023 | https://www.scai.fraunhofer.de/en/events/industry-symposium-ai-life-sciences.html | AI in the Life Sciences is one of the hottest topics in translational biomedical informatics. We therefore want to put a spotlight onto various “applied” AI research themes with high relevance for innovation in the industry. | TBD | 1 | |||||
| Keynote | DSSV-ECDA 2023 conference | Conference | Time: The next frontier in machine learning | 2023/07/07 | 2023 | https://iasc-isi.org/dssv-ecda2023/speakers/ | Data Science, Statistics & Visualisation (DSSV) and the European Conference on Data Analysis (ECDA) are a joint conference aimed at bringing together researchers and practitioners interested in the interplay of statistics, computer science, and visualization, and to build bridges between these fields for interdisciplinary research. | TBD | 1 | ||||||
| Keynote | KDD 2023 | Conference | Time: The next frontier for machine learning | 2023/08/10 | 2023 | https://kdd.org/kdd2023/ | TBD | TBD | 1 | ||||||
| Keynote | University of Milano Bicocca | 44th Annual Conference of the International Society for Clinical Biostatistics | Conference | Automated machine learning as a partner in predictive modelling | 2023/08/31 | 2023 | https://www.iscb2023.info/ | The ISCB Conference focuses both on methods and practical applications of biostatistics in medical, pharmaceutical and health-related research. Its aim is also to stimulate the use and development of new methods to further increase the value of scientific research in medicine. | In this talk, I aim to illuminate the underemphasized yet critical dimension in machine learning for healthcare as well as biostatistics: time. I contend that time harbors the potential to revolutionize machine learning methodologies, particularly within healthcare. This presentation underscores the opportunities and challenges that emerge from integrating temporal dynamics into machine learning models, enriching prediction accuracy, inference robustness, causal inference and conceptual understanding. | 1 | |||||
| Keynote | Royal Statistical Society | RSS International Conference 2023 | Conference | My Enemy's Enemy is My Friend: How Statisticians and Machine Learners Should Work Together to Build a Better World | 2023/09/06 | 2023 | https://rss.org.uk/news-publication/news-publications/2023/general-news/significance-lecturer-confirmed-for-rss-2023-confe/ | The RSS International Conference regularly attracts more than 500 attendees from over 30 countries providing one of the best opportunities for anyone interested in statistics and data science to come together to share knowledge and network. | TBD | 1 | |||||
| Keynote | (Various) | AutoML 2023 | Conference | Patterns, Prognosis, and Pitfalls: AutoML's Unique Challenges in Healthcare | 2023/09/13 | 2023 | https://2023.automl.cc/ | The international conference on automated machine learning (AutoML) is the premier gathering of professionals focussed on the progressive automation of machine learning (ML), aiming to develop automated methods for making ML methods more efficient, robust, trustworthy, and available to everyone. | TBD | 1 | |||||
| Keynote | AutoML 2023 | Conference | Data-Centric AI and the importance of AutoML | 2023/09/12 | 2023 | https://2023.automl.cc/ | The international conference on automated machine learning (AutoML) is the premier gathering of professionals focussed on the progressive automation of machine learning (ML), aiming to develop automated methods for making ML methods more efficient, robust, trustworthy, and available to everyone. | TBD | 1 | ||||||
| Keynote | iwCLL2023 Conference | Conference | Anticipated Role of AI/Machine Learning in Research and Treatment of CLL and Other Malignancies | 2023/10/09 | 2023 | https://iwcll2023.org/program/iwcll-program/# | The 2023 assembly will present the most up-to-date results emerging from clinic- and laboratory-based research about the pathobiology and treatment of this still incurable disease. The information presented will focus on the development, progression, treatment, and evolution of CLL. | TBD | 1 | ||||||
| Keynote | (Various) | MICCAI 2023 | Conference | Synthetic Data: Powerful creation not second rate copy | 2023/10/09 | 2023 | https://conferences.miccai.org/2023/en/ | The annual MICCAI conference attracts the world’s leading biomedical scientists, engineers, and clinicians from a wide range of disciplines associated with medical imaging and computer assisted intervention. | TBD | 1 | |||||
| Keynote | (Various) | 26th International Conference on Discovery Science | Conference | New Frontiers in Machine Learning for Discovery Science | 2023/10/10 | 2023 | https://ds2023.inesctec.pt/?page_id=255 | Discovery Science 2023 conference provides an open forum for intensive discussions and exchange of new ideas among researchers working in the area of Discovery Science. | TBD | 1 | |||||
| Keynote | Breaking Through | Conference | Crafting the Future: The Decisive Influence of Women in AI and Data | 2023/11/16 | 2023 | https://rsvp.withgoogle.com/events/breaking-through-a-women-in-data-initiative-conference | ‘Breaking Through’ is an event Google is organising to inspire and support women in Data Science and related fields. | TBD | 1 | ||||||
| Keynote | ATEaM founding conference | Conference | Machine learning in healthcare and AutoPognosis 2.0. What are the opportunities for the clinician and informatician? | 2023/11/11 | 2023 | https://a4transmed.blogspot.com/p/founding-conference-on-nov-11.html | The main topic is "Translational Science & AI/Emerging Technology in Medicine." | TBD | 1 | ||||||
| Keynote | Artificial Intelligence in Healthcare: Shaping the Future of Science | Conference | Pushing Medical Frontiers: AI-Driven Breakthroughs in Medicine | 2024/03/21 | 2024 | https://ai4h.unipd.it/home/speakers/ | This two-days event aims to explore the role of AI in healthcare and to discuss opportunities and challenges that AI offers to the present and future of medicine. The event will see the participation of maximum experts in the field of global research in medicine, information technology and bioengineering, who will present the latest news and ongoing research on the use of artificial intelligence and machine learning in medical applications | This presentation explores the transformative impacts of machine learning on personalized medicine and clinical trials, highlighting advancements in personalized screening, diagnosis, treatments, and monitoring. I will delve into automated machine learning for risk prediction and screening, causal effect inference for understanding treatment impacts, ODE and PDE discovery from data for pharmacology, time-series forecasting for patient monitoring, and continuous time control for optimizing interventions. By integrating these methods, we are not only enhancing the precision of medical care but also revolutionizing the efficiency of clinical trials. The talk will showcase real-world examples of how AI can redefine the entire healthcare landscape, providing insights into the current applications, challenges, and future directions of AI in medicine. Come discuss how AI can make these transformations in healthcare and join forces with us in this endeavour! | 1 | ||||||
| Invited talk | Danish cancer conference | Conference | TBD | 2023/11/10 | 2023 | TBD | At the conference we would like an expert that could provide us with an international perspective and in particular address issues regarding the use of AI and machine learning in the cancer pathway. With a focus on cancer, the purpose is to provide new perspectives of the potentials for the use of AI and machine learning in the future, but also the related challenges. | TBD | 1 | ||||||
| Invited talk | Cancer Crosslinks 2024 | Conference | Revolutionizing Oncology: AI-Driven Innovations on Cancer Pathways | 2024/01/25 | 2024 | https://www.cancercrosslinks.com/2024.html | Cancer Crosslinks is an educational meeting series which promotes interactions between researchers and clinicians, and encourages translational and clinical research to support collaborations to advance the development of innovative cancer treatments. | TBD | 1 | ||||||
| Invited talk | EMBL | EMBO Symposium “AI in Biology” | Conference | Beyond Causality: Discovering and Analyzing the Governing Equations of Medicine | 2024/03/12 | 2024 | https://www.embl.org/about/info/course-and-conference-office/events/ees24-01/#vf-tabs__section-speakers | This newly established symposium will bring together AI researchers who are active in different domains of biology including genomics/multi-omics, cell and tissue imaging, molecular imaging (structural biology), to discuss topics of common interest to make progress. | In this talk, I will present several cutting-edge machine learning methods developed in our lab which enable us to discover and analyze the governing equations of medicine, moving beyond traditional causal discovery methods. The focus will be on how this new way of modeling medical and biological processes as dynamical systems offers unprecedented insights into disease progression and the efficacy of treatment strategies over time. Through real-world examples, I will illustrate the transformative impact which I believe this new strand of machine learning can play in deciphering complex medical phenomena and improving patient care. | 1 | ||||||
| Keynote | UniBern-SDSC workshop on Data Science for the Sciences | Conference | Time: The next frontier in Machine Learning | 2024/04/11 | 2024 | https://www.ds4s.ch/ | The first Swiss conference on Data Science for the Sciences is jointly organized by the Swiss Data Science Center and the University of Bern. | In this talk, I aim to illuminate the underemphasized yet critical dimension in machine learning: time. I contend that time harbors the potential to revolutionize machine learning methodologies and their applications in numerous domains from healthcare to engineering to finance. This presentation underscores the opportunities and challenges that emerge from integrating temporal dynamics into machine learning models, enriching prediction accuracy, inference robustness, causality, and conceptual understanding. | 1 | ||||||
| Invited talk | LifeArc Translational Science Summit | Conference | Revolutionizing Healthcare: AI-Driven Breakthroughs in Medicine and Healthcare Delivery | 2024/04/23 | 2024 | https://www.translationalsciencesummit.org/home | The summit will highlight and encourage discussion on how translational science can bridge the gap between basic research and clinical applications, featuring presentations and insights from leading scientists, experts, policy makers and innovators. | This presentation explores the transformative impacts of machine learning and AI on personalized medicine, clinical trials and healthcare delivery. I will introduce several breakthrough machine learning methods developed in our lab aimed to address some of the hardest and most complex challenges in medicine and healthcare . By integrating these methods in clinical practice together with clinicians, we are not only enhancing the precision of medical care but also revolutionizing the efficiency of clinical trials. The talk will showcase real-world examples of how machine learning and AI can redefine the entire healthcare landscape, providing insights into the current applications, challenges, and future directions of AI in medicine. Come discuss how AI can make these transformations in healthcare and join forces with us in this endeavour! | 1 | ||||||
| Plenary | LifeArc Translational Science Summit | Conference | Our data and responsible AI | 2024/04/23 | 2024 | https://www.translationalsciencesummit.org/home | The summit will highlight and encourage discussion on how translational science can bridge the gap between basic research and clinical applications, featuring presentations and insights from leading scientists, experts, policy makers and innovators. | TBD | 1 | ||||||
| Panel | ABPI Conference | Conference | Perspectives on AI | 2024/04/25 | 2024 | https://events.abpi.org.uk/website/13571/speakers/ | Under the theme ‘Delivering the future of UK life sciences,’ we aim to address the sector's challenges and opportunities head-on with an agenda filled with discussions on driving investment and innovation through the new VPAG scheme, streamlined access to medicines, AI's role in pharmaceuticals, and strategies for excelling in medicines manufacturing. | TBD | 1 | ||||||
| Keynote | The Danish Engineering Society (IDA) and the Danish Society for Healthcare Quality | Conference on AI in Healthcare | Conference | Revolutionizing Healthcare: AI-Driven Breakthroughs in Medicine and Healthcare Delivery | 2024/05/21 | 2024 | https://ida.dk/arrangementer-og-kurser/konferencer/artificial-intelligence-in-healthcare-advancing-patient-outcomes-and-responsible-governance/speakers | Join the discussion about the future of healthcare at our conference, where AI's power to transform patient care through swift, accurate data processing and task automation takes center stage. | This presentation explores the transformative impacts of machine learning and AI on personalized medicine, clinical trials and healthcare delivery. I will introduce several breakthrough machine learning methods developed in our lab aimed to address some of the hardest and most complex challenges in medicine and healthcare. By integrating these methods in clinical practice together with clinicians, we are not only enhancing the precision of medical care for each patient, but also revolutionizing the efficiency of healthcare systems and of clinical trials. The talk will showcase real-world examples of how machine learning and AI can redefine the entire healthcare landscape, providing insights into the current applications, challenges, and future directions of AI in medicine. Come discuss how AI can make these transformations in healthcare and join forces with us in this endeavour! | 1 | |||||
| Panel | The Danish Engineering Society (IDA) and the Danish Society for Healthcare Quality | Conference on AI in Healthcare | Conference | How to accelerate innovative and trustworthy AI-enabled healthcare in Denmark and Europe? | 2024/05/21 | 2024 | https://ida.dk/arrangementer-og-kurser/konferencer/artificial-intelligence-in-healthcare-advancing-patient-outcomes-and-responsible-governance/speakers | Join the discussion about the future of healthcare at our conference, where AI's power to transform patient care through swift, accurate data processing and task automation takes center stage. | TBD | 1 | |||||
| Keynote | University Medical Center of Groningen, the Netherlands | ISCOMS 2024 | Conference | TBD | 2024/06/04 | 2024 | https://iscoms.com/keynote-lectures/ | ISCOMS is Europe's largest biomedical student congress and is growing bigger every year. The primary objectives of the congress are to facilitate student research exchange and support student research initiatives. | TBD | 1 | |||||
| Engagement session | ICML 2024 | Conference | TBD | 2024/07/21 | 2024 | https://icml.cc/ | When the Forty-first International Conference on Machine Learning (ICML 2024), one of the leading international academic conferences in machine learning, takes place from 21 – 27 July, the van der Schaar lab will be well-represented with 7 accepted papers. | TBD | 1 | ||||||
| Keynote | Munich AI Day | Conference | Revolutionizing Medicine & Healthcare: 5 Key Ingredients and the Role of AI | 2024/07/04 | 2024 | https://mcml.ai/events/2024-07-04-munich-ai-day/ | The aim of the summit, organized by the Munich Center for Machine Learning, is to discuss the potentials of AI in different fields. In addition to panels and discussions, the Munich AI Day offers numerous keynotes from science and industry, presenting the latest developments and innovative solutions. | In today's rapidly evolving world, technology is profoundly transforming healthcare. This talk will explore five key elements driving this change, focusing on how Artificial Intelligence (AI) can shape the future of medicine and ealthcare. 1. Proactive and Personalized Care: We are moving away from traditional reactive models to a proactive approach. AI-driven analytics and predictive modeling enable us to anticipate health issues before they occur. This shift not only improves patient outcomes but also revolutionizes our approach to wellness and disease prevention. 2. Tailored Treatments for individuals: The era of one-size-fits-all is ending. By using AI to analyze vast clinical records, we candetermine the best treatment options for each patient, considering their unique characteristics. This personalized approach introduces a new age of precision medicine. 3. Streamlining Healthcare Systems: Efficiency and resource allocation in healthcare systems need to undergo significant improvements to better support an aging population. AI can help optimize clinical esources, ensuring that the right care is delivered at the right time, reducing costs, and improving patient satisfaction. 4. Enhanced Healthcare Data: The quality, continuity, and accessibility of data are crucial in order for AI to be able to transform healthcare. AI can enhance data quality, integration and management, enabling seamless information flow across different platforms, systems and even countries. 5. Educating Clinicians on AI: For AI to reach its full potential, clinicians must understand its applications and implications. This talk will highlight the importance of AI education for healthcare professionals, ensuring not only that they are prepared to use these technologies effectively and ethically, but also that they are able to drive innovation in healthcare using AI. | 1 | ||||||
| Keynote | Curious2024 – Future Insight Conference | Conference | TBD | 2024/07/11 | 2024 | https://www.curiousfutureinsight.org/ | The Curious – Future Insight™ Conference brings together some of the world’s brightest scientists and most accomplished innovators to present their work and explore the future of science and technology, solving the challenges of today and enabling the dreams of a better tomorrow. | TBD | 1 | ||||||
| Keynote | 11th World Congress in Probability and Statistics | Conference | My Enemy's Enemy is My Friend: How Statisticians and Machine Learners Should Work Together to Build a Better World | 2024/08/12 | 2024 | https://www.bernoulli-ims-worldcongress2024.org/ | The Department of Mathematics at Ruhr-University Bochum, Germany, is honored to host the Bernoulli-IMS 11th World Congress in Probability and Statistics. Participants can expect superb meeting facilities on an open campus that is easily accessible from around the world by public transportation, in an excellent scientific environment with strong research groups in Probability and Statistics. | TBD | 1 | ||||||
| Keynote | Data Makers Fest | Conference | Reality-Centric AI | 2024/09/24 | 2024 | https://www.datamakersfest.com/agenda#sz-tab-45559 | Data Makers Fest is a festival dedicated to all data makers. For 3 days, we’ll gather data professionals and enthusiasts from all fields to learn, network, and make things happen. | For machine learning to add value to human-centric or human-impacted domains, we must embrace the complexity and the error-prone and constantly changing nature of these domains and not pretend the world is simple and then hope to sort real-world complexity afterward. We believe that real-world domains pose significantly harder challenges for machine learning than solving games or straightforward-to-formalize science problems. | 1 | ||||||
| Keynote | Causality Symposium | Conference | The Causal Discovery Ladder: Unravelling Governing Equations and Beyond Using Machine Learning | 2024/09/26 | 2024 | https://datascience.unifi.it/eccellenzadisia/events/simposio-sulla-causalita-26-27-settembre-2024/ | The Symposium will be a collection of contributions on the state of the art and future challenges in causality and causal inference for the new era of Data Science. | TBD | 1 | ||||||
| Keynote | Royal College of Physicians | What is our Future? AI is Coming for Gastroenterology | Conference | Pushing Medical Frontiers: AI-Driven Breakthroughs in Medicine | 2024/09/27 | 2024 | https://www.eventbrite.co.uk/e/what-is-our-future-ai-is-coming-for-gastroenterology-tickets-891813226367?aff=oddtdtcreator | Discover how artificial intelligence is set to transform the field of gastrointestinal medicine. Indeed, what is our future? AI is coming... | TBD | 1 | |||||
| Invited talk | ESICM LIVES 2024 Barcelona - Debate | Conference | Role of AI in the ICU in 2030 | 2024/10/08 | 2024 | https://www.esicm.org/events/37th-annual-congress/ | Our annual congress attracts over 5,000 intensive care physicians, anaesthetists, trainees and nursing and allied health professionals from +100 countries worlwide. Such extraordinary diversity makes LIVES the place where you can absorb the latest evidence and advances in patient care through multidisciplinary discussions and talks, interactive courses, demonstrations of the newest technology and simulation exercises. | TBD | 1 | ||||||
| Invited talk | ESICM LIVES 2024 Barcelona - Thematic session | Conference | Artificial Intelligence in the ICU - How large language models will change your clinical practice | 2024/10/09 | 2024 | https://www.esicm.org/events/37th-annual-congress/ | Our annual congress attracts over 5,000 intensive care physicians, anaesthetists, trainees and nursing and allied health professionals from +100 countries worlwide. Such extraordinary diversity makes LIVES the place where you can absorb the latest evidence and advances in patient care through multidisciplinary discussions and talks, interactive courses, demonstrations of the newest technology and simulation exercises. | TBD | 1 | ||||||
| Keynote | Westminster Health Forum policy conference: AI in healthcare - next steps for development, regulation and adoption | Conference | Next steps for utilising AI in healthcare | 2024/11/14 | 2024 | https://www.westminsterforumprojects.co.uk/conference/AI-in-Health-24 | The conference is being organised as an opportunity for stakeholders and policymakers to assess the way forward for utilising AI safely to meet the needs of healthcare, ensuring the readiness of healthcare systems to integrate AI effectively, including infrastructure and data management. Further discussion is expected on the priorities for developing regulation and legislation for AI-based technologies, next steps for research, development and innovation of new technologies, and key challenges for healthcare workers using AI technologies and developing workforce confidence. | TBD | 1 | ||||||
| Keynote | EGC 2025 | Conference | Scientific Discovery using AI: From Discovering Equations to Digital Twins | 2025/01/31 | 2025 | https://www.egc2025.cnrs.fr/ | TBD | In this keynote, we will explore the transformative role of AI in scientific discovery, from uncovering fundamental equations that govern natural phenomena to creating dynamic, high-fidelity digital twins of complex systems. By harnessing the power of AI to analyze vast datasets, we can accelerate breakthroughs across diverse fields—from physics to biology, from healthcare to environmental science. This talk will highlight cutting-edge AI methods that not only aid in discovering new scientific principles but also enable the simulation and optimization of real-world systems, ushering in a new era of predictive modeling and transformative innovation. | 1 | ||||||
| Keynote | Humboldt-Universität zu Berlin | DAGStat 2025 | Conference | LLM’s Role in Advancing Science | 2025/03/25 | 2025 | https://dagstat2025.de/#invited-speakers | TBD | In this talk, I will delve into how we are pushing the boundaries of machine learning to revolutionize scientific discovery and drive transformative real-world applications. By harnessing the power of Machine Learning and Large Language Models (LLMs), we are achieving breakthroughs in uncovering intricate patterns, generating novel hypotheses, and advancing causal inference over time. This research agenda pioneers innovative methods for scientific exploration, from harmonizing diverse datasets to uncovering complex dynamical systems. At its core, this vision positions LLMs as a pivotal force in advancing science that is not only impactful but also profoundly human-centered. Join me to explore this transformative journey and how we can collaborate to shape the future of discovery together. | 1 | |||||
| Keynote | The Institute of Digital Technologies for Personalized Healthcare (MeDiTech), Dalle Molle Institute of Artificial Intelligence (IDSIA USI-SUPSI), Institute of Information Systems and Networking (ISIN) and the Ente Ospedaliero Cantonale (EOC) | Precision Health Day | Conference | TBD | 2025/03/28 | 2025 | https://www.supsi.ch/en/giornata-della-salute-di-precisione | Event organized by the Institute of Digital Technologies for Personalized Healthcare (MeDiTech), Dalle Molle Institute of Artificial Intelligence (IDSIA USI-SUPSI), Institute of Information Systems and Networking (ISIN) and the Ente Ospedaliero Cantonale (EOC) to share experiences and develop collaborations in precision health care with experts from academia, industry and institutions. | TBD | 1 | |||||
| Keynote | Association of Physicians of Great Britain and Ireland | The 118th Association of Physicians of Great Britain and Ireland Annual Meeting | Conference | Translating Big Data and AI: Building Digital Twins using Machine Learning | 2025/04/04 | 2025 | https://apam2025.org.uk/ | This prestigious event will bring together leading clinicians, researchers, and healthcare professionals. It is a unique opportunity to collaborate, share insights, and advance groundbreaking research. | TBD | 1 | |||||
| Keynote | HQA | HQA’s 21st annual Industry Results Presentation and Clinical Quality Conference | Conference | Clinicians in the Driver’s Seat: How ML Copilots Can Help You Design the Future of Healthcare | 2025/08/15 | 2025 | https://www.hqa.co.za/ | TBD | TBD | 1 | |||||
| Keynote | Wearables Innovation Forum | Conference | Temporal Intelligence - How Time Warps the Boundaries of Wearable Innovation | 2025/09/29 | 2025 | https://www.precisionhealth.cam.ac.uk/wp-content/uploads/2025/03/Wearables_advert_final_logo_small.png | Hosted at Pembroke College on Monday September 29th, our launch event will include a full day of keynote lectures, short talks, innovation pitching and poster sessions, discussion panels, interactive workshops, and plenty of opportunities for networking, ending with a College dinner. Our aim is to catalyse efficient knowledge exchange, stimulate joint ventures, and provide a roadmap for technology transfer endeavours enabling sustained growth and impact in precision health. | TBD | 1 | ||||||
| Keynote | AstraZeneca | AstraZeneca’s R&D Science Day | Conference | TBD | 2025/09/30 | 2025 | TBD | This AstraZeneca event presents a valuable opportunity for our R&D community to foster cross-disciplinary collaboration, exchange knowledge, and showcase outstanding science communication. We are also welcoming local innovators and collaborators who help shape Cambridge’s vibrant scientific ecosystem. The event will feature networking sessions for AstraZeneca R&D colleagues and distinguished guests. | TBD | 1 | |||||
| Invited talk | AstraZeneca | Real‑World Data & Analytics Summit: FORWD | Conference | Lightning partners cases breakout session | 2026/03/16 | 2026 | TBD | The RWD Summit is a two-day, invitation-only event that brings together global leaders from healthcare, academia, government, technology, and pharma. This summit will feature keynote presentations, interactive workshops, and networking opportunities. Our goal is to unlock the power of real-world data to transform care and improve patient outcomes through collaboration, innovation, and actionable insights. | TBD | 1 | |||||
| Panel | Goldman Sachs | In the Lead 2026 – London | Conference | AI in Action: The Women Driving Innovation | 2026/03/27 | 2026 | TBD | "In the Lead" is Goldman Sachs’ global flagship platform, established in 2021, designed to bring together top women private wealth management clients for unparalleled content, networking, and unique insights. It aims to empower women to maximize their impact in their wealth, philanthropy, legacy, and beyond, fostering a powerful community of change-makers. | TBD | 1 | |||||
| Invited talk | ICLR 2026 | Principled Design for Trustworthy AI - Interpretability, Robustness, and Safety across Modalities workshop | Conference | Stop Forecasting! Start Understanding Time Series Dynamics and Causality! | 2026/04/27 | 2026 | https://trustworthy-ai-workshop.github.io/iclr2026/ | This workshop focuses on developing trustworthy AI systems by principled design: models that are interpretable, robust, and aligned across the full lifecycle – from training and evaluation to inference-time behavior and deployment. We aim to unify efforts across modalities (language, vision, audio, and time series) and across technical areas of trustworthiness spanning interpretability, robustness, uncertainty, and safety. | TBD | 1 | |||||
| Tutorial | European Association for the Study of the Liver (EASL) | EASL Congress 2026 | Conference | The Next Genomics Frontier: From Risk Prediction to Digital Twins | 2026/05/27 | 2026 | https://pag.virtual-meeting.org/easl/easl2026/en-GB/pag/session/104334 | The EASL 2026 Annual Congress of the European Association for the Study of the Liver will take place from May 27 to 30 at Fira Barcelona Gran Via, a renowned venue in Barcelona, Spain. As one of the largest gatherings of its kind, it is expected to draw more than 10,000 delegates globally, including clinicians, researchers, industry professionals, and policymakers. The congress will operate in a hybrid format, offering options for attendees to join either in person in Barcelona or virtually online. | In this talk, I will chart a path that connects these two powerful but currently disjoint paradigms. I will begin by revisiting our foundational work on self-supervised learning for genomic risk estimation and multi-view supervised learning for multi-omics integration—methods that learn rich, structured representations of biological data. I will then turn to our more recent efforts in developing AI agents for digital twins for simulating individual disease trajectories and personalized effects of therapeutic interventions. | 1 | |||||
| Invited talk | Ray Dolby Centre, Cavendish Laboratory | Dolby Symposium: AI in Science | Conference | AI in Healthcare | 2026/06/18 | 2026 | https://www.tickettailor.com/events/cambridgephysicsoutreach/2177184?trackingConsent=0 | The Dolby Symposium is a one-day meeting focused on a single, unifying theme designed to foster engaging dialogue and provide fresh insights on the topic. The theme for 2026 is “AI in Science”. The programme of talks and panel discussions will explore how AI can be used across the sciences from computational biology, integrated into autonomous laboratories and the wider impact on society and policy. | TBD | 1 | |||||
| Keynote | PRISM Institute | AI in Oncology Conference | Conference | Cross-Domain AI Applications: Predictive and Methodological Insights Beyond Oncology | 2026/06/23 | 2026 | https://www.aiinoncology.com/programme-2026 | In 2026, we will build on this momentum by broadening our scope to include the latest developments and real-world applications of AI that are transforming oncology today. Beyond the scientific content, AI in Oncology Conference by IHU PRISM will once again serve as a unique platform to exchange ideas, share experiences, and foster collaborations among clinicians, researchers, data scientists, and industry experts shaping the future of personalized cancer care. | TBD | 1 | |||||
| Keynote | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2026) | Conference | Rethinking the Role of Scientific Discovery in the Era of AI | 2026/09/07 | 2626 | https://ecmlpkdd.org/2026/keynotes/ | ECML-PKDD is widely recognized as the flagship European conference in machine learning and data mining, and one of the most prestigious venues worldwide, bringing together approximately 1,500 participants from academia and industry. The conference serves as a premier forum for advancing the theoretical foundations, methodologies, and impactful applications of data-driven intelligence. | In this talk, I present a framework that rethinks discovery as an agentic process, in which networks of interacting AI agents and human experts jointly explore, critique, and refine hypotheses under shared norms of evaluation. I will discuss how heterogeneous, LLM-based research agents can self-organize into evolving networks that generate, review, and extend findings—trading off novelty, quality, and diversity over time. Drawing on results from biomedical discovery, including cancer cohort analyses, I will argue that such socially structured agent networks can sustain large-scale exploratory discovery, complementing experimental science while reshaping how knowledge is generated, accumulated, and validated in the era of AI. | 1 | ||||||
| Invited talk | Pistoia Alliance | ClinSilico Showcase - Al in Clinical Trials: From Promise to Practice | Conference | Building the Next Generation of Digital Twins: Introducing ClinSilicoTwins™: Patient, Population, Trial and Development Twins for AI-enabled Clinical Development | 2026/09/15 | 2026 | https://pistoiaalliance.org/eventdetails/ai-in-clinical-trials-from-promise-to-practice/ | AI is rapidly moving from promise to practical application in clinical development. This session will explore how advances in AI, patient engagement, in-silico approaches and digital twins are reshaping the design and delivery of clinical trials. Bringing together perspectives from across the research and innovation ecosystem, speakers will share emerging approaches to AI-enabled clinical development, from systemic transformation in clinical research to regulatory-aligned in-silico tools and the next generation of patient, population, trial and development digital twins. | TBD | 1 | |||||
| Invited talk | wellcome Sanger institute | Target Validation – In the Era of Genomics, Big Data, and AI | Conference | Rethinking scientific discovery in the era of AI | 2026/09/22 | 2026 | https://coursesandconferences.wellcomeconnectingscience.org/event/target-validation-in-the-era-of-genomics-big-data-and-ai-20260921/ | This conference will bring together leading researchers and thought experts from academia, biotech, and pharma. The meeting will focus on the critical elements that power therapeutic hypothesis building, including large-scale genomics data generation and target selection strategies across modalities and therapeutic areas—with AI and emerging technologies taking centre stage. The meeting will explore how these innovations are reshaping the path from discovery to the clinic in both common and rare diseases. | TBD | 1 | |||||
| Seminar | The Francis Crick Institute | Computational Biology Symposium @ The Crick | Conference | TBD | 2026/09/29 | 2026 | https://www.eventbrite.co.uk/e/computational-biology-symposium-the-crick-tickets-1998225980699 | This event brings together computational researchers from across London's leading institutions, including the Francis Crick Institute, UCL, Imperial, and King's, alongside the AI and biotech community around King's Cross, to spark new collaborations between computational and experimental science. | TBD | 1 | |||||
| Invited talk | British Medical Association | International Conference on Physician Health™ (ICPH) 2026 | Conference | From Clinician to Super-Clinician: How AI Can Make Exceptional Care the Standard | 2026/09/30 | 2026 | https://www.international-conference-physician-health.org/conference-speakers | The International Conference on Physician Health 2026 (ICPH 2026) will provide a forum for practitioners and researchers to present innovative methods and support systems, educational programmes and recent research findings in the area of physician and medical student health. The friendly and informal conference environment will promote networking, exchange of experience and information and leisure activity focused on staying healthy. | The discussion about AI and physician wellbeing is often too defensive. We ask whether AI can reduce documentation, remove administrative tasks and protect clinicians from burnout. These are worthwhile goals, but they are far too modest. The greater opportunity is to use AI to make every physician a super-clinician: able to deliver exceptional, bespoke care to every patient, at every stage of their journey. Today, the care a patient receives can depend too heavily on where they are treated, which clinician they see and whether the right information is noticed at the right moment. This variation rarely reflects a lack of commitment. It reflects the limits of human attention, fragmented data, rapidly expanding medical knowledge and the difficulty of reasoning about an individual patient over many years. AI can change this. It can help clinicians anticipate disease before symptoms appear, detect subtle early signs of deterioration, identify the diagnosis that best explains a patient’s evolving history, estimate which treatment is most likely to benefit that particular person, and continually adapt care as the patient responds. It can bring together longitudinal records, biomarkers, imaging, genomics, clinical evidence and the experience of similar patients—while making uncertainty, alternatives and missing evidence visible to the physician. This is not a future in which AI replaces clinical judgment or standardises every patient into the same pathway. It is one in which AI reduces unwarranted variation while enabling more individualised medicine. The clinician remains responsible for understanding the person, interpreting context and making decisions, but is no longer constrained by what one human can remember, integrate and calculate alone. Physician wellbeing should include being able to practise medicine at a level that was previously impossible: preventing more disease, diagnosing earlier, selecting better treatments and learning from every patient. AI’s greatest contribution may be not to make physicians less busy, but to make every physician far more capable—and medicine far more precise, proactive and personal. | 1 |
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| Type | Venue/organizer | Event name | Event type | Title | Event date | Year | URL | Description | Abstract | Recording | Flag 1 | Flag 2 | Flag 3 | Flag 4 | Flag 5 |
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| Invited talk | ICML | Time Series Workshop | Workshop | Time-series in healthcare: challenges and solutions | 2021/07/24 | 2021 | https://www.vanderschaar-lab.com/icml-2021-time-series-workshop-invited-talk/ | 2 | |||||||
| Keynote | ICML | Interpretable ML in Healthcare workshop | Workshop | Quantitative epistemology – conceiving a new human-machine partnership | 2021/07/23 | 2021 | https://www.vanderschaar-lab.com/icml-2021-imlh-keynote-on-quantitative-epistemology/ | 2 | |||||||
| Keynote | Eric and Wendy Schmidt Center | Inaugural workshop | Workshop | How we set up machine learning for clinical medicine at Cambridge/Turing | 2021/06/03 | 2021 | https://www.broadinstitute.org/eric-and-wendy-schmidt-center/eric-and-wendy-schmidt-center-workshop-opportunities-interface-machine | 2 | |||||||
| Invited talk | ICLR | Synthetic Data Workshop | Workshop | Can Machine Learning Revolutionize Healthcare? Synthetic Data may be the Answer | 2021/05/07 | 2021 | https://sdg-quality-privacy-bias.github.io/ | View full video | 2 | ||||||
| Invited talk | ICLR | MLPCP workshop | Workshop | How AI and machine learning can help healthcare systems respond to pandemics | 2021/05/07 | 2021 | https://mlpcp21.github.io/pages/speakers.html | View full video | 2 | ||||||
| Invited talk | NeurIPS | NeurIPS Europe meetup on Bayesian Deep Learning | Workshop | Bayesian Uncertainty Estimation under Covariate Shift: Application to Cross-population Clinical Prognosis | 2020/12/10 | 2020 | http://bayesiandeeplearning.org/#schedule | 2 | |||||||
| Keynote | NeurIPS | Women in ML (WiML) workshop | Workshop | Interpretable AutoML: Powering the machine learning revolution in healthcare in the era of Covid-19 and beyond | 2020/12/09 | 2020 | https://wimlworkshop.org/neurips2020/ | View full video | 2 | ||||||
| Invited talk | Health Data Research UK | Synthetic Data Special Interest Group workshop | Workshop | How technically close are we to a vision for synthetic healthcare datasets? | 2020/12/09 | 2020 | https://www.vanderschaar-lab.com/events/synthetic-data-special-interest-group-workshop/ | 2 | |||||||
| Keynote | ICML | Workshop on Automated Machine Learning | Workshop | Automated ML and its transformative impact on medicine and healthcare | 2020/07/18 | 2020 | https://www.vanderschaar-lab.com/icml-2020-automated-ml-and-its-transformative-impact-on-medicine-and-healthcare/ | View full video | 2 | ||||||
| Invited talk | ICML | ARTEMISS Workshop | Workshop | Learning despite the unknown – missing data imputation in healthcare | 2020/07/17 | 2020 | https://www.vanderschaar-lab.com/icml-2020-learning-despite-the-unknown-missing-data-imputation-in-healthcare/ | View full video | 2 | ||||||
| Tutorial | Max Planck Institute for Intelligent Systems | Machine Learning Summer School (MLSS) | Summer school | Machine Learning for Healthcare | 2020/07/08 | 2020 | https://www.vanderschaar-lab.com/mihaela-van-der-schaar-to-give-tutorial-at-mlss-2020/ | 2 | |||||||
| Keynote | IEEE Signal Processing Society | Data Science and Learning Workshop (DSLW) | Workshop | Why Medicine is Creating Exciting New Frontiers for Machine Learning | 2021/06/06 | 2021 | http://conferences.ece.ubc.ca/dslw2021/#/speakers | 2 | |||||||
| Invited talk | AAAI | Spring Symposium survival prediction workshop | Workshop | Survival Analysis in the Era of Machine Learning: Is there life after Cox? | 2021/03/23 | 2021 | https://spaca.weebly.com/program.html | 2 | |||||||
| Keynote | IEEE Signal Processing Society | International Workshop on Machine Learning for Signal Processing (MLSP) | Workshop | Machine learning: Changing the future of healthcare | 2020/09/23 | 2020 | https://ieeemlsp.cc/keynote-lectures#mihaela-van-der-schaar | 2 | |||||||
| Keynote | KDD | Workshop on Mining and Learning from Time Series | Workshop | Machine Learning for Healthcare in the COVID-19 Era | 2020/08/24 | 2020 | https://kdd-milets.github.io/milets2020/ | 2 | |||||||
| Invited talk | DeepMind/Wigner Institute | Eastern European Machine Learning (EEML) Summer School | Summer school | Causality with applications to medicine | 2021/07/12 | 2021 | https://www.eeml.eu/home | 2 | |||||||
| Invited talk | AI for Global Goals/University of Oxford/CIFAR | Oxford Machine Learning (OxML) Summer School | Summer school | Why is machine learning for healthcare different? | 2020/08/20 | 2020 | https://www.oxfordml.school/oxml2020 | 2 | |||||||
| Keynote | KDD | Workshop on Applied Data Science for Healthcare | Workshop | Quantitative epistemology: conceiving a new human-machine partnership | 2021/08/23 | 2021 | https://dshealthkdd.github.io/dshealth-2021 | 2 | |||||||
| Keynote | ACM | 2021 ACM Europe Summer School | Summer school | Machine Learning for Healthcare | 2021/09/01 | 2021 | https://europe.acm.org/hpc-summer-school/invited-talks | 2 | |||||||
| Keynote | EMA/FDA | Good Machine Language Procedure workshop | Workshop | Machine Learning in Healthcare: Interpretability, Explainability and Trustworthiness | 2021/09/14 | 2021 | https://www.vanderschaar-lab.com/events/ema-fda-good-machine-language-procedure-workshop-keynote/ | 2 | |||||||
| Invited talk | AVIVA/University of Cambridge | AVIVA-Cambridge Mathematics of Information Workshop | Workshop | Conceiving a new human-machine partnership | 2021/09/22 | 2021 | 2 | ||||||||
| Keynote | MICCAI | Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) | Workshop | Quantitative Epistemology: Conceiving a new human-machine partnership | 2021/09/27 | 2021 | http://imimic-workshop.com/index.html | 2 | |||||||
| Invited talk | University of Freiburg | AutoML Fall School 2021 | Summer school | AutoML for healthcare | 2021/11/10 | 2021 | https://sites.google.com/view/automlschool21/schedule | 2 | |||||||
| Invited talk | Royal Society of Medicine | Virtual education day (AI & Genomics) | Workshop | Self-supervised learning for genomics | 2021/11/24 | 2021 | https://www.rsm.ac.uk/events/medical-genetics/2021-22/mgq50/ | 2 | |||||||
| Invited talk | NeurIPS | Deep Generative Models and Downstream Applications Workshop | Workshop | Synthetic Data Generation and Assessment: Challenges, Methods, Impact | 2021/12/14 | 2021 | https://dgms-and-applications.github.io/2021/#invited-speakers | 2 | |||||||
| Invited talk | NeurIPS | Self-supervised Learning Workshop | Workshop | Self-supervised learning for genomics | 2021/12/14 | 2021 | https://sslneurips21.github.io/pages/schedule.html | 2 | |||||||
| Invited talk | NeurIPS | Workshop on Meta-Learning (MetaLearn) | Workshop | Quantitative epistemology – empowering human meta-learning using machine learning | 2021/12/13 | 2021 | https://meta-learn.github.io/2021/#schedule | 2 | |||||||
| Keynote | AI4Med | AI4Med Talk Series | Workshop | 2022/02/19 | 2022 | https://www.ai4medtalkseries.com/ | AI4MED is an organization that aims to bring knowledge about current and future Artificial Intelligence (AI) technologies closer to medical students and clinicians. Its purpose is to spread information about AI in the healthcare industry and demystify many of the myths that surround it. AI4MED will accomplish this through a series of talks in which renowned experts will explain AI technologies in simple terms and resolve common doubts. | 2 | |||||||
| Invited talk | AAAI | Information-Theoretic Methods for Causal Inference and Discovery (ITCI'22) | Workshop | Machine Learning for Healthcare: Challenges and Research Opportunities for Information Theory | 2022/03/01 | 2022 | https://sites.google.com/view/itci22 | The goal of ITCI’22 is to bring together researchers working at the intersection of information theory, causal inference and machine learning in order to foster new collaborations and provide a venue to brainstorm new ideas, exemplify to the information theory community causal inference and discovery as an application area and highlight important technical challenges motivated by practical ML problems, draw the attention of the wider machine learning community to the problems at the intersection of causal inference and information theory, demonstrate to the community the utility of information-theoretic tools to tackle causal ML problems. | 2 | ||||||
| Invited talk | Accademia Nazionale dei Lincei | The research-industry system in Italy | Workshop | Revolutionizing Healthcare using AI | 2022/03/18 | 2022 | https://www.lincei.it/en/node/9469 | The 1-day Workshop is aimed at discussing the context in which Italian public institutions and industry operate in science and technology. The Workshop’s goal is to identify strengths and weaknesses of the current system and discuss a strategy for improving the efficiency of the exchange between public and private sectors to foster scientific and technological research. The Workshop is meant to be the first of a series of meetings (Forum on Research-Industry in Italy) on focused exchanges between the public and private research sectors in Italy. | 2 | ||||||
| Keynote | AWS | Modern Statistics and Statistical Machine Learning (StatML) | Workshop | Using ML to discover the underlying models of medicine | 2022/04/06 | 2022 | https://statml.io/ | A workshop on Machine Learning, Computational Statistics and their Applications that brings together academics and PhD students from several academic institutions, and Applied Scientists from Amazon and other companies. The workshop will cover several areas of Machine Learning in order to give PhD students a good feeling of the research done within and outside Amazon. | 2 | ||||||
| Keynote | Various | Artificial Intelligence for the Fight Against COVID-19 | Workshop | Using ML to discover the underlying models of medicine | 2022/04/07 | 2022 | https://events.bcamath.org/ai4facovid-19/ | The workshop will bring together multiple international researchers working on applications of Artificial Intelligence for the COVID-19 pandemic, as well as representatives from the Health Care system including physicians, biomedicine researchers, and managers of health centers. | 2 | ||||||
| Keynote | Ulster University/IEEE | Trustworthy AI for the Future of Risk Management | Workshop | Machine learning for discovery - The new frontier | 2022/06/14 | 2022 | https://computing.ulster.ac.uk/TAI-RM2022/ | TAI-RM 2022 aims to bring together academic researchers, industry practitioners, regulators and stakeholders to share their latest results, gather new problems, explore the deep understanding of trustworthy AI, understand the implications of trustworthy AI for existing risk management practices and the broader regulatory context, discuss the foundations in the design of every AI system in terms of those 5 pillars, the practical challenges associated with the implementation of the state-of-art trustworthy AI methods, as well as discuss the key opportunities and focus areas within trustworthy AI to face the unique challenges in the future of risk management in different areas, especially in IoT area. | TBD | 2 | |||||
| Invited talk | Cambridge ELLIS unit | 2022 Cambridge Ellis Summer School on Machine Learning | Summer school | "New frontiers in machine learning interpretability" (first hour) and "New Frontiers in Causal Inference" (second hour) | 2022/07/13 | 2022 | http://www.ellis.eng.cam.ac.uk/summerschool/ | The Cambridge Ellis Machine Learning Summer School is a distinguished course offered to graduate students, researchers and professionals, featuring engaging experts in their respective field and/or world-recognized professionals speaking about advanced machine learning concepts. | TBD | 2 | |||||
| Keynote | (Various) | ICML 2022 Workshop - The 1st Workshop on Healthcare AI and COVID-19 | Workshop | TBD | 2022/07/22 | 2022 | https://healthcare-ai-covid19.github.io/#organizers | In recent two years, the COVID-19 pandemic continues to disrupt the world, and has changed most aspects of human life. Healthcare AI has a mission to help humans to tackle the issues that are caused by COVID-19, e.g., COVID-19 vaccine related prediction, COVID-19 medical imaging diagnosis. With the development of the epidemic, the virus keeps mutating, and meanwhile the related research is also evolving. As a result, more and more understanding, observation, and policy are emerging. All of these factors bring new challenges and opportunities to scientific research, including Healthcare AI. The goal of this workshop is to bring together perspectives from multiple disciplines (e.g., Healthcare AI, Machine Learning, Medical Image ML, Bioinformatics, Genomics, Epidemiology, Public Health, Health Policy, Computer Vision, Deep Learning, Cognitive Science) to highlight major open questions and to identify collaboration opportunities to address outstanding challenges in the domain of COVID-19 related Healthcare AI. | TBD | 2 | |||||
| Panel | (Various) | ICML 2022 -- Panel Discussion | Workshop | Challenges and opportunities in the drug discovery pipeline. | 2022/07/17 | 2022 | We would like the panel to consist of established researchers in ML and in-house experts spanning different departments within the organization. The goal is to have an objective discussion with agreements and disagreements between ML researchers and domain experts. | 2 | |||||||
| Invited talk | TU Delf | Explainable AI Summer School | Workshop | New frontiers in machine learning interpretability | 2022/08/29 | 2022 | https://xaiss.eu/ | The Explainable AI summer school aims to cover some of the most important and highly researched topics in explainable AI (e.g. post-hoc interpretability in ML, interpretable representation learning in language and vision tasks, counterfactuals, human-centric explainability, and others) and their applications in important subject areas (e.g. language, vision, search and recommendation systems). | TBD | 2 | |||||
| Invited talk | AI Education Foundation | The Mediterranean Machine Learning (M2L) summer school | Summer school | ML for healthcare | 2022/09/12 | 2022 | https://www.m2lschool.org/home | The Mediterranean Machine Learning (M2L) summer school will be structured around 5 days of keynotes, lectures and practical sessions. The program will include social or cultural activities to foster networking. | TBD | 2 | |||||
| Panel | WiML | Women in Machine Learning (WiML) Ph.D. Admissions Workshop | Workshop | 2022/10/06 | 2022 | TBD | This Ph.D. admissions workshop brings together established researchers who identify as a woman and/or nonbinary from both academia and industry with students and junior researchers who are looking to further their machine learning research careers in a graduate Ph.D. program. The goal of the workshop is to provide concrete, actionable guidance to students from underrepresented groups to help them succeed in applying to graduate school. | 2 | |||||||
| Invited talk | ICAIF2022 | Workshop on Explainable AI in Finance | Workshop | Interpretable Machine Learning for Time-Series Forecasting | 2022/11/02 | 2022 | https://sites.google.com/view/2022-workshop-explainable-ai/speakers-and-panelists?authuser=0 | This workshop aims to bring together academic researchers, industry practitioners and financial experts to discuss the key opportunities and focus areas within XAI – both in general and to face the unique challenges in the financial sector. | In this talk I will present two approaches for making machine learning for time-series forecasting interpretable and actionable. The first approach uses post-hoc interpretability to turn black-box machine learning into interpretable insights. I will describe here the first methods for feature-based and for example-based interpretability of machine learning for time-series forecasting - Dynamask (ICML 2021) and Simplex (NeurIPS 2021), respectively.The second approach directly discovers interpretable closed-form ordinary differential equations (ODEs) from data using machine learning. I will describe here the first automated tool to distill closed-form ODEs from observed trajectories, D-CODE (ICLR 2022), which we believe will accelerate the modeling process of dynamical systems in finance, in healthcare, and beyond. | 2 | |||||
| Keynote | Turing Post-Doctoral Enrichment Awards (PDEA) and the Turing Interest Group Meta-Learning for Multimodal Data | Turing Workshop on Open-Source AI Software in Healthcare | Summer school | TBD | 2022/11/21 | 2022 | https://sites.google.com/sheffield.ac.uk/ai-software4health | The "Open-Source AI Software in Healthcare" workshop aims to bring together the research communities of open-source software and healthcare. The objective is to discuss the major bottlenecks and standards in the field. There will be a series of high-profile speakers from industry and academia, with engineering or medical backgrounds. Recent advances, challenges, and efforts of open-source AI software for healthcare applications will be discussed. | TBD | 2 | |||||
| Keynote | Ethox Centre in Oxford, Nuffield Department of Population Health | Workshop on ethical AI | Workshop | “Sunlight is said to be the best of disinfectants”: Transparency is key to ethical AI in healthcare | 2023 | https://workshopedaim.sciencesconf.org/ | Through this workshop, we hope to facilitate an interdisciplinary dialogue between technologists, medical practitioners and ethicists. | TBD | 2 | ||||||
| Invited talk | ICU Talk | Workshop | TBD | 2023/04/03 | 2023 | https://www.traumabase.eu/fr_FR | TBD | TBD | 2 | ||||||
| Panel | (Various) | NCI workshop on Cancer AI Research | Workshop | How can we build digital twins from complex, messy, incomplete and private real-world clinical data | 2023/04/04 | 2023 | https://events.cancer.gov/aiwg/ai_imperfect_data_workshop | The goals of this workshop are to (1) examine the state of the science for AI methods designed to operate on noisy, complex, or low-dimensional data, (2) explore how these methods may be applied to key areas of cancer research, and (3) discuss processes for identifying the biological questions that will motivate further advances in machine learning. This workshop will highlight the importance of leveraging advances across fields to accelerate cancer research and discovery through AI. | TBD | 2 | |||||
| Panel | (Various) | Financial Times Digital Dialogue | Workshop | Digitalising Drug Discovery | 2023/04/25 | 2023 | https://digitisingdrugdiscovery.live.ft.com/ | This webinar discussion, hosted by the Financial Times and BIOVIA, Dassault Systèmes, will explore how adapting AI and ML can revolutionise early stage drug development. There will also be discussions around how pharma and software companies can work together to generate success on both sides. | TBD | 2 | |||||
| Invited talk | Machine Learning and Artificial Intelligence for Personalized Medicine | Workshop | Improving Clinical trials with Machine Learning: Discovering Governing Equations in Medicine & Beyond | 2023/04/18 | 2023 | https://www.imsi.institute/activities/machine-learning-and-ai-for-personalized-medicine/#schedule | This workshop will focus on cutting-edge advances in ML and AI applied to personalized medicine and prognostic care for treatments of diseases like cancer, cardiovascular conditions and diabetes. | TBD | 2 | ||||||
| Panel | 15th annual McKinsey Cancer Congress Series Symposium during ASCO 2023 | Workshop | From chip to bedside – Oncology in silico R&D | 2023/06/02 | 2023 | https://web.cvent.com/event/85302b76-5b89-4923-8af0-20bd794a2407/summary?RefId=Summary | TBD | TBD | 2 | ||||||
| Keynote | AI4H Paris Summer School | Summer school | The future of personalized medicine and its implications on the healthcare systems: A machine learning perspective | 2023/07/03 | 2023 | https://ai4healthschool.org/speakers/ | With three days of plenary lectures (July 3rd-5th) led by international experts accompanied by two days of hands-on practical sessions (July 6th and 7th), the summer school will cover the latest advances in the field of artificial intelligence applied to health. | Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of clinical data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Cancer! Think Covid 19! In this talk I will show how, by working closely with clinicians, we are developing cutting-edge machine learning methods to create tangible clinical impact and how in turn, medicine is driving new advances in machine learning, including discovery of laws and equations from data, causal inference, time-series forecasting, reinforcement learning, generative models etc. | 2 | ||||||
| Invited talk | (Various) | DMLR@ICML'23 | Workshop | Prescription for Perfect Data: Four Machine Learning Antidotes for Improving Data | 2023/07/29 | 2023 | https://dmlr.ai/ | The goal of this workshop is to facilitate these essential topics in what we call Data-centric Machine Learning Research, which includes not only datasets and benchmarks, but tooling and governance, as well as fundamental research on topics such as data quality and data acquisition for dataset creation and optimization. | TBD | 2 | |||||
| Invited talk | Counterfactuals in Minds and Machines Workshop@ICML'23 | Workshop | Causal Deep Learning | 2023/07/29 | 2023 | https://sites.google.com/view/counterfactuals-icml/ | In this workshop, we aim to fill that space by facilitating interdisciplinary interactions that will shed light onto the three following questions: What insights can causal machine learning take from the latest advances in cognitive science? In what use cases is each causal modeling framework most appropriate for modeling counterfactuals? What barriers need to be lifted for the wider adoption of counterfactual-based machine learning applications, like personalized healthcare? | TBD | 2 | ||||||
| Panel | Counterfactuals in Minds and Machines Panel@ICML'23 | Workshop | The role of counterfactuals in explainable AI and ML for healthcare. | 2023/07/29 | 2023 | https://sites.google.com/view/counterfactuals-icml/ | Therein, our diverse set of speakers across computer science, cognitive psychology, and philosophy will discuss similarities and differences between humans and machines in counterfactual reasoning, as well as ways to make counterfactual-based methods more broadly adopted in relevant applications. Audience questions will be encouraged to make the discussions more interactive. | TBD | 2 | ||||||
| Keynote | (Various) | IEEE MLSP 2023 | Workshop | From theory to bedside: Clinician-centric machine learning for tangible impact | 2023/09/19 | 2023 | https://2023.ieeemlsp.org/ | The 33rd MLSP Workshop will be held at Roma Eventi Fontana di Trevi Conference Centre in the heart of Rome, Italy. The conference will present the most recent and exciting research advances through keynote talks, tutorials, special and regular single-track sessions, panels, and demonstration sessions. | TBD | 2 | |||||
| Keynote | ECML-PKDD on Explainable AI | Workshop | Turning the lights on inside the black box: New frontiers in machine learning interpretability from time-series to causal inference to unsupervised learning | 2023/09/18 | 2023 | https://project.inria.fr/aimlai/ | The AIMLAI workshop aims at gathering researchers, experts and professionals, from inside and outside the domain of AI, interested in the topic of interpretable or explainable AI. | TBD | 2 | ||||||
| Invited talk | Causal XAI Workshop | Workshop | Benchmarking Heterogeneous Treatment Effect Models through the Lens of XAI | 2023/10/26 | 2023 | https://minds.qmul.ac.uk/index.php/causal-xai-workshop/ | This workshop is aimed at PhD students, researchers, and academics. The audience will have the chance to network and hear invited speakers who are experts on XAI. | TBD | 2 | ||||||
| Invited talk | Royal Society & UK Government workshop on AI safety | Workshop | TBD | 2023/10/25 | 2023 | TBD | The workshop will convene senior scientists from a wide range of disciplines to identify potential AI safety risks across scientific fields, with a particular focus on understanding views on short-to-medium term risks. | TBD | 2 | ||||||
| Panel | The Royal Statistical Society | RSS AI Fringe event | Workshop | Evaluating artificial intelligence: How data science and statistics can make sense of AI models | 2023/10/31 | 2023 | https://rss.org.uk/training-events/events/events-2023/rss-events/evaluating-artificial-intelligence-how-data-scienc/#fulleventinfo | In early November the UK government hosts an AI Safety Summit. Ahead of that event, we ask: What should AI evaluation look like? How will it work in practice? What metrics are most important, and -- crucially -- who gets to decide this? Join us for a special panel debate at the RSS, where these questions, and more, will be discussed. | TBD | 2 | |||||
| Tutorial | Northern Lights Deep Learning Conference 2024 | Summer school | Innovative Uses of Synthetic Data Tutorial | 2024/01/08 | 2024 | https://www.nldl.org/program/winter-school | This 5-day course is build upon tutorials on specific topics on deep learning from perspectives such as Synthetic data, Generative Models, and Explainability. The course further encompasses among others keynote talks as well as special sessions on industry and diversity in AI as part of the NLDL conference program. | One of the biggest barriers to AI adoption is the difficulty to access high quality training data. Synthetic data has been widely recognised as a viable solution to this problem. It allows sharing, augmenting and de-biasing data for building performant and socially responsible AI algorithms.However, despite the significant progress in the theory and algorithm, the community still lacks a unified software that enables practical data sharing and access with synthetic data. This lab aims to bridge this gap by introducing synthcity, an open source Python library that implements an array of cutting edge synthetic data generators to address the problems of data generation due to its commonality in various applications. | 2 | ||||||
| Invited talk | TU Delft Aula | Workshop series on Values and Value Conflicts: Navigating the Interplay of Explainability and Privacy in AI | Workshop | The Road to Transparent AI: Latest Breakthroughs | 2024/02/08 | 2024 | https://www.aanmelder.nl/150497/home | In the ever-evolving landscape of artificial intelligence, where ethical considerations intersect with innovation, we aim to develop a workshop series on values and value conflicts, with a specific focus on their application in real-world domains. | In this keynote, I describe new breakthroughs on ML interpretability. This will include 1) powerful ways to interpret ML methods for time-series forecasting, clustering (phenotyping), and heterogeneous treatment effect estimation, 2) provide personalized explanations of ML methods which refer to the unique experience of the user of the ML method, and 3) autonomously discover scientific concepts using concept activation regions, which are generalizations of concept-based explanations. To learn more about our work in this area - see our website dedicated to this topic - https://www.vanderschaar-lab.com/interpretable-machine-learning/ and our github - https://github.com/vanderschaarlab/Interpretability | 2 | |||||
| Tutorial | TU Delft Aula | Workshop series on Values and Value Conflicts: Navigating the Interplay of Explainability and Privacy in AI | Workshop | Innovative Uses of Synthetic Data | 2024/02/09 | 2024 | https://www.aanmelder.nl/150497/home | In the ever-evolving landscape of artificial intelligence, where ethical considerations intersect with innovation, we aim to develop a workshop series on values and value conflicts, with a specific focus on their application in real-world domains. | TBD | 2 | |||||
| Keynote | AI4TS workshop @ AAAI 2024 | Workshop | Time - The next frontier in AI: From scientific discovery to causality | 2024/02/26 | 2024 | https://ai4ts.github.io/aaai2024 | The goal of this workshop is to provide a platform for researchers and AI practitioners from both academia and industry to discuss potential research directions, key technical issues, and present solutions to tackle related challenges in practical applications. The workshop will focus on both the theoretical and practical aspects of time series data analysis and aims to trigger research innovations in theories, algorithms, and applications. | TBD | 2 | ||||||
| Invited talk | ICLR TS4H Workshop | Workshop | Learning from Time Series for Health | 2024/05/11 | 2024 | https://timeseriesforhealth.github.io/ | Time series data are ubiquitous in healthcare, from medical time series to wearable data, and present an exciting opportunity for machine learning methods to extract actionable insights about human health. However, huge gap remain between the existing time series literature and what is needed to make machine learning systems practical and deployable for healthcare. This is because learning from time series for health is notoriously challenging: labels are often noisy or missing, data can be multimodal and extremely high dimensional, missing values are pervasive, measurements are irregular, data distributions shift rapidly over time, explaining model outcomes is challenging, and deployed models require careful maintenance over time. These challenges introduce interesting research problems that the community has been actively working on for the last few years, with significant room for contribution still remaining. Learning from time series for health is a uniquely challenging and important area with increasing application. Significant advancements are required to realize the societal benefits of these systems for healthcare. This workshop will bring together machine learning researchers dedicated to advancing the field of time series modeling in healthcare to bring these models closer to deployment. | TBD | 2 | ||||||
| Keynote | ELLIS summer school on Machine Learning for Healthcare and Biology | Summer school | Machine learning for healthcare and biology | 2024/06/11 | 2024 | www.manchester.ac.uk/ellis2024 | Following the success of last year, Manchester’s European Laboratory for Learning and Intelligent Systems (ELLIS) unit is hosting its second Summer School during 11-13 June 2024, which will bring participants up-to-speed on the latest methods and technologies in machine learning with the focus on healthcare and biology. The school includes a set of Lectures by renowned researchers at the intersection of ML and Healthcare and Biology, and with an excellent track record of delivering educational content. | TBD | 2 | ||||||
| Keynote | EEML (Eastern European Machine Learning) Summer School | Summer school | Causality | 2024/07/20 | 2024 | https://www.eeml.eu/home | TBD | TBD | 2 | ||||||
| Tutorial | AI for Global Goals | OxML 2024 | Summer school | Synthetic Data - Powerful Creation, Not Second Rate Copy | 2024/07/07 | 2024 | https://www.oxfordml.school/ | At AI for Global Goals, we aim to provide our global participants with best-in-class training on a broad range of advanced topics and developments in machine learning (ML) -- including deep learning (DL). Building on 4 successful years of previous programs, our 2024 initiative will delve into some of the most vital and emerging themes in ML and DL, areas of significant interest within the research community. These encompass statistical and probabilistic ML, representation learning, reinforcement learning, causal inference, computer vision, natural language processing (NLP), geometrical DL, and more. We are excited to explore their diverse applications in the context of sustainable development goals (SDGs). | TBD | 2 | |||||
| Invited talk | CCAIM | CCAIM AI and Machine Learning Summer School | Summer school | Summer School Welcome | 2024/09/02 | 2024 | https://www.vanderschaar-lab.com/ccaim-ai-and-machine-learning-summer-school/ | From 2 to 6 September 2024, the Cambridge Centre for AI in Medicine invites you to the third iteration of the world’s first Online Summer School exclusively focused on AI and Machine Learning for Healthcare – technical, cutting-edge, and reality-centric. | TBD | 2 | |||||
| Invited talk | CCAIM | The Cambridge AI in Medicine Summer School | Summer school | Introduction to summer school / Welcome | 2024/09/09 | 2024 | https://www.vanderschaar-lab.com/cambridge-ai-in-medicine-summer-school/ | This school is tailored to physicians, medical students, and other clinicians without or little prior knowledge of AI and machine learning. All you need is a passion to engage with AI for Medicine in a fast-paced learning environment where you will build expertise from the ground up. | TBD | 2 | |||||
| Tutorial | MICCAI 2024 | Workshop | Clinical AI in the Real-World: From Data-Centric AI to Dynamic Learning | 2024/10/06 | 2024 | https://continualmedai.github.io/dcdl2024/ | Our integrated tutorial will equip participants with a comprehensive understanding and practical skills around two important real-world medical AI challenges. The tutorial aims to provide an interactive and hands-on experience via software tools and interactive coding sessions, thereby enabling practical engagement for participants. | TBD | 2 | ||||||
| Invited talk | NeurIPS 2024 TSALM Workshop | Workshop | From Data to Discovery: LLM’s Role in Advancing Science | 2024/12/15 | 2024 | https://neurips-time-series-workshop.github.io/ | This workshop aims to provide a forum for researchers and practitioners to understand the progress made and push the frontier of time series research in the era of large models. | TBD | 2 | ||||||
| Invited talk | JHH AI & Healthcare Workshop | Workshop | CliMB: An AI-enabled Partner for Clinical Predictive Modeling | 2025/02/21 | 2025 | TBD | TBD | In this talk, I will introduce CliMB, a no-code AI-enabled partner designed to empower clinician scientists to create predictive models using natural language. CliMB streamlines the entire medical data science pipeline, enabling users to build robust models from real-world data within a single conversation. It also generates structured reports, interpretable visuals, and automated performance evaluations, ensuring transparency and usability for clinical decision-making. I will present findings from systematic evaluations demonstrating CliMB's superior performance over GPT-4, particularly in planning, error prevention, and model execution. Additionally, I will discuss results from a blinded study involving 45 clinicians across specialties and career stages, where over 80% preferred CliMB for its clarity, ease of use, and reliability. By integrating advances in data-centric AI, AutoML, and interpretable ML, CliMB lowers the barrier to AI adoption in medicine, offering clinician scientists a powerful, intuitive tool to harness AI for real-world impact. | 2 | ||||||
| Invited talk | Data Science 4 Health and Biology (DS4HB) Workshop 2025 | Workshop | Reality-Centric AI | 2025/04/09 | 2025 | https://www.ds4hb.polimi.it/index.html | DS4HB is designed to be a comprehensive three-day workshop focusing on the application of data science to (multi-)omics data (Day 1), medical imaging (Day 2), and Electronic Health Records (Day 3). | TBD | 2 | ||||||
| Keynote | ICLR 2025 | SynthData@ICLR'25 workshop | Workshop | From Synthetic Data to Digital Twins: The Next Frontier in Machine Learning | 2025/04/27 | 2025 | https://synthetic-data-iclr.github.io/#speakers | This workshop seeks to address this question by highlighting the limitations and opportunities of synthetic data. It aims to bring together researchers working on algorithms and applications of synthetic data, general data access for machine learning, privacy-preserving methods such as federated learning and differential privacy, and large model training experts to discuss lessons learned and chart important future directions. | TBD | 2 | |||||
| Invited talk | ICLR 2025 | "Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI": Workshop on ICLR 2025 in Singapore | Workshop | TBD | 2025/04/27 | 2025 | https://uncertainty-foundation-models.github.io/ | This workshop seeks to address the gap by defining, evaluating, and understanding the implications of uncertainty quantification for autoregressive models and large-scale foundation models. | TBD | 2 | |||||
| Invited talk | ICLR 2025 | Machine Learning for Genomics Exploration (MLGenX) workshop | Workshop | The Next Genomics Frontier: From Risk Prediction to Digital Twins | 2025/04/27 | 2025 | https://mlgenx.github.io/ | This year, the workshop will feature three distinct tracks designed to welcome a diverse array of researchers in the field of machine learning and biology: the Main Track including application and ML topics, the Special Track on LLMs and Agentic AI, and the Tiny Papers Track. | As machine learning increasingly intersects with biology and medicine, we find ourselves at a critical inflection point: predictive models built on genomic and multi-omics data have achieved remarkable success in estimating disease risk and stratifying patients, yet they remain fundamentally static—offering snapshots rather than simulations, associations rather than actions. In parallel, the rise of AI agents and digital twins promises a new paradigm: dynamic, interactive systems that use longitudinal clinical biomarkers to reason about interventions, simulate outcomes, and adapt to change based on newly available clinical data about the patient. In this talk, I will chart a path that connects these two powerful but currently disjoint paradigms. I will begin by revisiting our foundational work on self-supervised learning for genomic risk estimation and multi-view supervised learning for multi-omics integration—methods that learn rich, structured representations of biological data. I will then turn to our more recent efforts in developing AI agents for digital twins for simulating individual disease trajectories and personalized effects of therapeutic interventions. I will finish the talk by discussing what I believe to be the next frontier: the urgent need to build a new kind of digital twin—one that does not merely learn from clinical biomarkers, but is grounded in the rich molecular fabric of each individual patient. By integrating time-series clinical data with genomic and multi-omics signals, we can create AI agents capable of reasoning not only over the dynamic progression of disease, but also over the biological predispositions that shape it. These AI agents can give rise to truly personalized digital twins which hold the potential to transform clinical care by enabling earlier diagnosis, more precise prognosis, and tailored therapeutic strategies—ultimately improving outcomes for every patient based on their unique biological blueprint. | 2 | |||||
| Invited talk | CVPR 2025 | 4th XAI4CV workshop | Workshop | TBD | 2025/06/11 | 2025 | https://xai4cv.github.io/workshop_cvpr25 | Explainability of computer vision systems is critical for people to effectively use and interact with them. This workshop seeks to contribute to the development of more explainable CV systems by: (1) initiating discussions across researchers and practitioners in academia and industry to identify successes, failures, and priorities in current XAI work; (2) examining the strengths, weaknesses, and underlying assumptions of proposed XAI methods and establish best practices in evaluation of these methods; and (3) discussing the various nuances of explainability and brainstorm ways to build explainable CV systems that benefit all involved stakeholders. | TBD | 2 | |||||
| Keynote | 2025 Deep Learning School@UniCA | Summer school | The Rise of AI Agents: Code, Science, Systems, Networks | 2025/06/26 | 2025 | https://univ-cotedazur.eu/efelia-cote-dazur/summer-schools/deep-learning-school/deep-learning-school-2025 | The Deep Learning School at Université Côte d'Azur is a major scientific event for the research and engineering communities working at the frontline of AI. Since 2017, we have provided our extended ecosystem with the opportunity to learn from prestigious AI researchers and practicing the latest techniques in hands-on sessions with local expert supervisors. | AI agents are redefining autonomy in artificial intelligence—evolving from passive predictors into dynamic systems that can plan, act, adapt, and collaborate. This talk provides a structured introduction to the emerging field of AI agents, aimed at researchers and practitioners in machine learning, systems, and science. We will define what constitutes an AI agent, outline a taxonomy of current approaches, and illustrate their capabilities through diverse examples: agents that write and debug code, agents that discover scientific laws, agents that solve complex planning tasks, and agents that power digital twins in healthcare and infrastructure. Throughout, we will highlight key architectural components, modes of interaction, and open research challenges shaping the future of agentic AI. | 2 | ||||||
| Invited talk | ICML 2025 | “Foundation Models for Structured Data” workshop | Workshop | The Consistent Reasoning Paradox and Why Saying 'I Don’t Know’ is Essential for AGI | 2025/07/18 | 2025 | https://icml-structured-fm-workshop.github.io/ | The workshop on Foundation Models for Structured Data (FMSD) offers a place to jointly discuss foundation models for structured data, effectively addressing the gap and enabling the communities to capitalize on their synergies. We aim for advancements in foundation models that unify structured data modalities, addressing challenges of scalability and generalization across real-world applications. | The workshop on Foundation Models for Structured Data (FMSD) offers a place to jointly discuss foundation models for structured data, effectively addressing the gap and enabling the communities to capitalize on their synergies. We aim for advancements in foundation models that unify structured data modalities, addressing challenges of scalability and generalization across real-world applications. | 2 | |||||
| Tutorial | NORA as part of the NORA Research School. | NLDL 2016 Winter School | Summer school | Large Language Models-Based Copilots and AI Agents - Facilitating the use of AI/ML tools with CLiMB/DC | 2026/01/09 | 2026 | https://www.nldl.org/program/winter-school | The NLDL Winter School consists of tutorials by experts in the field and is co-hosted by NORA as part of the NORA Research School. | Even though predictive modelling with can provide crucial insights for prevention, diagnosis, and therapy in medicine, clinical domain experts are often unable to build such models using their data. The advent of large-language models (LLM) has ushered in interactive tools that can provide advice on statistical analysis, help researchers code in various programming languages, and interpret statistical output in natural language. The most commonly used interactive tools do not have the necessary reasoning agents adapted for the statistical analysis pipeline. Such limitations could result in improper interpretation of the data, syntax errors in statistical code, unproductive loops, etc., all of which can stymie productive use. Furthermore, these tools do not provide interfaces that allow data to be incorporated, processed and analyzed in a single framework. In this seminar, we present several advances that have overcome these issues and describe a new set of LLM-based co-pilots. These co-pilots have broad applications across healthcare disciplines and can support both novel data-centric and data-analytic tools in AI/ML. | 2 | |||||
| Keynote | UCL | IGNITE: Health Innovation Summit | Workshop | The future of health and emerging health innovation | 2026/01/22 | 2026 | https://www.ucl.ac.uk/health/academic-careers-office/training-portfolios/ignite-health-innovation-summit-series | IGNITE is a summit series that brings together national and international leaders from across the ecosystem to debate, connect, and accelerate meaningful health innovation, and push the boundaries of the art of the possible. | TBD | 2 | |||||
| Panel | UCL | IGNITE: Health Innovation Summit Panel Discussion | Workshop | Clinical Intelligence | 2026/01/22 | 2026 | https://www.ucl.ac.uk/health/academic-careers-office/training-portfolios/ignite-health-innovation-summit-series | IGNITE is a summit series that brings together national and international leaders from across the ecosystem to debate, connect, and accelerate meaningful health innovation, and push the boundaries of the art of the possible. | TBD | 2 | |||||
| Invited talk | UK Parliament and APPG AI | AI Horizon Scanning: The Next Wave of AI Technologies and Their Impact on Our World, Work, and Play | Workshop | Emerging AI Horizon Scanning | 2026/01/26 | 2026 | TBD | This session will focus on emerging AI Horizon Scanning, including agentic AI systems, AGI-orientated models, synthetic data, autonomous systems, and other AI frontiers, and how they may enable new ways of achieving outcomes, what they signal for the future, and their implications for the UK’s economic resilience, public services/business/society, scientific leadership, and governance frameworks. | TBD | 2 | |||||
| Invited talk | AISTATS 2026 | Causality in the Age of AI Scaling Workshop | Workshop | Scaling Causal Reasoning | 2026/05/05 | 2026 | https://virtual.aistats.org/virtual/2026/workshop/10059 | Reasoning about interventions, the core of causality, is fundamental to solving many of modern AI's most pressing challenges, including trustworthiness, reliability, explainability, and out-of-distribution generalization. Yet, recent AI breakthroughs have been overwhelmingly driven by scaling models on simple predictive objectives without explicit causal modeling, such as next-word prediction for Large Language Models or denoising prediction for diffusion models. This success raises a critical question for the community: Can causal abilities emerge from scale alone, and if not, what can explicit causal modeling bring that scale cannot? This workshop aims to understand this question and explore the potential synergy between scaling predictive methods and formal causal modeling to build the next generation of AI. | Causal reasoning requires answering interventional and counterfactual questions about the mechanisms that generate data—not just predicting what comes next. Yet today’s large-scale machine learning systems struggle precisely where this capability matters most: in complex, real-world settings where causal structure is large, only partially specified, and rarely accompanied by ground-truth supervision. This creates a fundamental bottleneck: how can we scale causal reasoning when both the underlying systems and the signals needed to learn them are inherently incomplete? In this talk, I will argue that the core challenge is not simply one of model capacity, but of representation and supervision. Real-world causal systems must be formalized, queried, and stress-tested in ways that current learning paradigms do not naturally support. I will outline a new perspective on scaling causal reasoning—one that reframes the problem as constructing and leveraging causal simulators capable of supporting interventional and counterfactual queries at scale. This perspective opens the door to new forms of supervision, evaluation, and generalization for causal reasoning in machine learning. This is joint work with my students Anita Kriz and Nicolas Astorga. | 2 | |||||
| Invited talk | Cambridge Public Health | Cambridge Public Health Seminar | Workshop | Scaling Causal Reasoning | 2026/06/08 | 2026 | https://www.cph.cam.ac.uk/events/cambridge-public-health-seminar-scaling-causal-reasoning | The seminar will showcase the breadth of public health research across the University of Cambridge, highlighting how different disciplines are addressing complex health challenges – from local priorities in the East of England to global issues. | Causal reasoning requires answering interventional and counterfactual questions about the mechanisms that generate data—not just predicting what comes next. Yet today’s large-scale machine learning systems struggle precisely where this capability matters most: in complex, real-world settings where causal structure is large, only partially specified, and rarely accompanied by ground-truth supervision. This creates a fundamental bottleneck: how can we scale causal reasoning when both the underlying systems and the signals needed to learn them are inherently incomplete? In this talk, I will argue that the core challenge is not simply one of model capacity, but of representation and supervision. Real-world causal systems must be formalized, queried, and stress-tested in ways that current learning paradigms do not naturally support. I will outline a new perspective on scaling causal reasoning—one that reframes the problem as constructing and leveraging causal simulators capable of supporting interventional and counterfactual queries at scale. This perspective opens the door to new forms of supervision, evaluation, and generalization for causal reasoning in machine learning. This is joint work with my students Anita Kriz and Nicolas Astorga. | 2 | |||||
| Panel | Royal Academy of Engineering | Agentic AI Workshop | Workshop | TBD | 2026/06/08 | 2026 | TBD | The event designs around three elements: a discussion on what is new in the current wave of agentic AI; a set of practical case studies from people building and deploying these systems; and a hands-on workshop focused on where the most promising near-term opportunities lie, what the main blockers are, what good looks like in applied settings, and where government intervention may be most useful. | TBD | 2 | |||||
| Panel | BBC Studios | A Question of Science with Professor Brian Cox | Workshop | How is AI transforming medicine? | 2026/07/01 | 2026 | https://lostintv.com/tv-show?id=1428 | From interpreting scans to predicting disease, artificial intelligence is already transforming medicine. But how far can it go — and what does it mean for our health? The panel explores AI's role in drug discovery, patient care and beyond. | TBD | 2 |
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| Type | Venue/organizer | Event name | Event type | Title | Event date | Year | URL | Description | Abstract | Recording | Flag 1 | Flag 2 | Flag 3 | Flag 4 | Flag 5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Invited talk | Cambridge Mathematics of Information in Healthcare Hub | Launch event | Guest lecture | Why medicine is creating exciting new frontiers for machine learning | 2021/05/05 | 2021 | https://gateway.newton.ac.uk/event/tgmw93/programme | View or download | 3 | ||||||
| Seminar | MIT Operations Research Center | Guest lecture | Causal Effects and Counterfactuals: A Machine Learning Approach | 2021/04/15 | 2021 | https://orc.mit.edu/events/causal-effects-and-counterfactuals-machine-learning-approach | 3 | ||||||||
| Seminar | University of Oxford Nuffield Department of Population Health | Richard Doll Seminar | Guest lecture | Revolutionizing Healthcare: Turning the practice of medicine into a quantitative science | 2021/02/16 | 2021 | https://talks.ox.ac.uk/talks/id/f7cf7369-ecb0-459a-b699-6bd35ffec01e/ | 3 | |||||||
| Seminar | DeepMind/ELLIS/UCL | CSML Seminar | Guest lecture | Why medicine is creating exciting new frontiers for machine learning | 2021/02/05 | 2021 | https://ucl-ellis.github.io/dm_csml_seminars/2021-02-05-Schaar/ | 3 | |||||||
| Seminar | The Alan Turing Institute | Turing Lecture | Guest lecture | Machine learning: from black boxes to white boxes | 2020/03/11 | 2020 | https://www.vanderschaar-lab.com/turing-lecture-machine-learning-from-black-boxes-to-white-boxes/ | 3 | |||||||
| Seminar | University of Oxford | Alan Taylor Lecture | Guest lecture | Transforming medicine through machine learning and artificial intelligence | 2019/11/11 | 2019 | https://www.vanderschaar-lab.com/events/university-of-oxford-2019-alan-taylor-lecture/ | 3 | |||||||
| Seminar | The Alan Turing Institute | Turing Lecture | Guest lecture | Transforming medicine through AI-enabled healthcare pathways | 2019/06/03 | 2019 | https://www.vanderschaar-lab.com/turing-lecture-transforming-medicine-through-ai-enabled-healthcare-pathways/ | 3 | |||||||
| Seminar | University of Leicester | Very Reverend Derek Hole Lecture | Guest lecture | 2019/04/25 | 2019 | https://www.vanderschaar-lab.com/events/university-of-leicester-very-reverend-derek-hole-2019-lecture/ | 3 | ||||||||
| Seminar | University of Cambridge | Oon Lecture | Guest lecture | Medicine 2.0: Transforming Clinical Practice and Discovery Through Machine Learning and Learning Engines | 2018/11/20 | 2018 | https://www.vanderschaar-lab.com/2018-oon-lecture-medicine-2-0-transforming-clinical-practice-and-discovery-through-machine-learning-and-learning-engines/ | 3 | |||||||
| Seminar | The Alan Turing Institute | Turing Lecture | Guest lecture | Using Machine Learning to Transform Medical Practice and Discovery | 2017/05/04 | 2017 | https://www.vanderschaar-lab.com/turing-lecture-medicine-2-0-using-machine-learning-to-transform-medical-practice-and-discovery/ | 3 | |||||||
| Seminar | The Alan Turing Institute | Turing Fellow Short Talk | Guest lecture | Machine learning, data science and decisions for a better planet | 2016/10/24 | 2016 | https://www.vanderschaar-lab.com/turing-short-talk-machine-learning-data-science-and-decisions-for-a-better-planet/ | 3 | |||||||
| Invited talk | Rice University | ECE Distinguished Speaker Series | Guest lecture | Why medicine is creating exciting new frontiers for machine learning | 2021/03/09 | 2021 | https://events.rice.edu/#!view/event/event_id/175547 | 3 | |||||||
| Invited talk | International AI Doctoral Academy (AIDA) | AIDA AI Excellence Lecture | Guest lecture | Machine learning for medicine and healthcare | 2021/09/21 | 2021 | https://www.i-aida.org/events/machine-learning-for-medicine-and-healthcare/# | 3 | |||||||
| Invited talk | University of Potsdam | Kálmán Lecture | Guest lecture | Quantitative epistemology: conceiving a new human-machine partnership | 2021/10/04 | 2021 | https://www.math.uni-potsdam.de/en/institute/events/details/veranstaltungsdetails/4th-kalman-lecture-with-mihaela-van-der-schaar | 3 | |||||||
| Invited talk | Cambridge University Hospitals | CUH consultant forum | Guest lecture | Why clinicians need to drive the machine learning revolution in healthcare | 2022/01/27 | 2022 | 3 | ||||||||
| Seminar | Piscopia | PiWORKS Seminar | Guest lecture | Machine learning in healthcare: from interpretability to a new human-machine partnership | 2022/02/01 | 2022 | https://piscopia.co.uk/piworks-seminar-series/ | Piscopia's monthly seminar series features women and non-binary researchers from UK universities working in diverse research areas in mathematics and related disciplines. The aim of PiWORKS (Piscopia Initiative – Women and Other Researchers Keynote Series) is threefold; we want to: 1. Showcase the work of women and non-binary researchers. 2. Provide a taste of different areas of mathematics research to undergraduate and MSc students. 3. Build an UK-wide community of women and non-binary researchers and students. The target audience for the talks are final year undergraduate and MSc students, but PhD students or academic staff are very welcome to attend. | 3 | ||||||
| Invited talk | University of Oxford | Sherrington Society lecture | Guest lecture | Envisioning the NHS of the future - How machine learning can drive the transformation of healthcare delivery | 2022/02/03 | 2022 | The Sherrington Society is a University-wide medical society based at Magdalen College. Named in honour of the 1932 Nobel laureate Sir Charles Sherrington, a former Waynflete Professor of Physiology and tutor at Magdalen, the society regularly hosts distinguished speakers in the medical sciences to encourage debate on topical medical and life-science issues. The society’s roots stretch back to the start of the 20th century, making it one of the College’s oldest and most venerable student-led institutions. | 3 | |||||||
| Invited talk | Cambridge Medical Society | Cambridge Medical Society Annual Talk | Guest lecture | What can Machine Learning do for Early Diagnosis and Detection (ED&D)? | 2022/02/24 | 2022 | 3 | ||||||||
| Seminar | University of Minnesota | UMN Machine Learning Seminar Series | Guest lecture | Using ML to discover the underlying models of medicine | 2022/03/23 | 2022 | https://sites.google.com/umn.edu/machine-learning/ | This seminar series brings together faculty, students, and industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. | 3 | ||||||
| Seminar | University of Wisconsin-Madison | Systems, Information, Learning and Optimization (SILO) seminar | Guest lecture | Using ML to discover the underlying models of medicine | 2022/04/13 | 2022 | https://silo.wisc.edu/ | SILO is about breaking down the “silos” of research created by academic department boundaries. Recent advances in information science are allowing scientists and researchers to sense, process and share data in ways and scales previously impossible. These developments have the potential to benefit work happening in a wide range of disciplines. SILO’s purpose is to help realize such potential by providing the time and space for researchers to present and interact to find common threads. | 3 | ||||||
| Invited talk | Department of Artificial Intelligence, Technical University of Madrid | Guest lecture | Using ML to discover the underlying models of medivine | 2022/05/23 | 2022 | 3 | |||||||||
| Seminar | Baidu | Learning Structured Models of the World | Guest lecture | Machine learning for medicine and healthcare | 2022/06/08 | 2022 | TBD | TBD | Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19! | 3 | |||||
| Invited talk | CeMM | CeMM Special Seminar | Guest lecture | Panning for insights in medicine and beyond - New frontiers in machine learning interpretability | 2022/07/25 | 2022 | TBD | TBD | TBD | 3 | |||||
| Invited talk | ITU | ITU Journal on Future and Evolving Technologies (ITUJ-FET) webinar series | Guest lecture | The future of healthcare in the metaverse | 2022/10/18 | 2022 | https://www.itu.int/en/journal/j-fet/webinars/20220927/Pages/default.aspx | The webinars, open to anyone and free of charge, aim at presenting insights and forward-looking research on future and evolving technologies. | In this webinar, the speaker will describe her vision of how the metaverse will transform healthcare. By applying machine learning and AI on data from a variety of devices and sensors, we can better monitor and treat patients at home, in hospitals and in the clinic, and enable patients and clinicians to interact in completely new ways in the metaverse on the basis of the derived analytics. The metaverse will also allow AI-enabled avatars to join multidisciplinary clinical teams, creating more efficient and more advanced health delivery systems. Finally, the speaker will outline a vision of how national and international healthcare systems can interact and be transformed and how clinical trials can be conducted and augmented in the metaverse. | 3 | |||||
| Seminar | AP-HP (Greater Paris University hospitals) | Bernoulli Lab Seminar | Guest lecture | Panning for insights in medicine and beyond: New frontiers in machine learning interpretability | 2022/10/07 | 2022 | https://www.bernoulli-lab.fr/en/home/ | The Bernoulli Lab is in fact a “virtual laboratory” aimed at facilitating collaborations between APHP clinicians and Inria researchers on digital health. The audience is expected to mostly consist of APHP clinicians and Inria researchers, but the seminar will be publicly announced and open in the usual fashion of an academic seminar. | TBD | 3 | |||||
| Seminar | University of Cape Town | Faculty of Sciences seminar series | Guest lecture | Why Medicine is Creating New Frontiers in AI: From Data-Centric AI to AI for Scientific Discovery | 2022/10/17 | 2022 | http://www.stats.uct.ac.za/ | TBD | TBD | 3 | |||||
| Invited talk | Boston Children's Hospital Computational Health Informatics Program (CHIP) | Landmark Ideas Series | Guest lecture | Machine Learning and Revolutionizing Healthcare. | 2022/10/03 | 2022 | http://www.chip.org/events | The Landmark Ideas Series is an event series led by Boston Children's Hospital Computational Health Informatics Program (CHIP) that features thought leaders across health care, informatics, IT, astrophysics, science, and more. | TBD | 3 | |||||
| Invited talk | Universitat Politècnica de Catalunya (UPC) | Talk by Prof Mihaela van der Schaar (Univ. of Cambridge) | Guest lecture | Panning for insight: Discovering governing equations from data using Machine Learning | 2022/10/27 | 2022 | https://telecos.upc.edu/ca/noticies/conferencia-panning-for-insight-discovering-governing-equations-from-data-using-machine-learning-27-doctubre | TBD | TBD | 3 | |||||
| Seminar | UCLA | CS 201 Seminar Fall 22 | Guest lecture | AI for Science Discovering Diverse Classes of Equations in Medicineand Beyond | 2022 | TBD | TBD | Artificial Intelligence (AI) offers the promise of revolutionizing The way scientific discoveries are made and significantly accelerating their pace. This is important for numerous fields of study, including medicine. In this talk, I will present our esearch on AI for science over the past few years. I will start by briefly showing how we can discover closed-form prediction functions from crosssectional data using symbolic metamodels. Then, I will introduce a new method, called D-CODE, which discovers closed-form ordinary differential equations (ODEs) from observed trajectories (longitudinal data).This method can only describe observable variables, yet many important variables in medical settings are often not observable. Hence, I will subsequently present the latent hybridisation model (LHM) that integrates a system of ODEs with machine-learned neural ODEs to fully describe the dynamics of the complex systems. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities. To solve these challenges, I will then present Neural Laplace, with which we can learn diverse classes of differential equations in the Laplace domain. I will conclude by presenting next research frontiers, including recent work on discovering partial differential questions from data (D-CIPHER). While these works are applicable in numerous scientific domains, in this talk I will illustrate the various works with examples from medicine, ranging from understanding cancer evolution to treating Covid-19. This work is joint work with Zhaozhi Qian, Krzysztof Kacprzyk and Sam Holt. | 3 | ||||||
| Seminar | USC - Viterbi school of engineering | Ming Hsieh Institute + Center for Cyber-Physical Systems and the Internet of Things + Center for Autonomy and AI Joint Seminar | Guest lecture | AI for Science: Discovering Diverse Classes of Equations in Medicine and Beyond | 2022/11/22 | 2022 | https://viterbi.usc.edu/calendar/?date=11/22/2022& | TBD | Artificial Intelligence (AI) offers the promise of revolutionizing the way scientific discoveries are made and significantly accelerating their pace. This is important for numerous fields of study, including medicine. In this talk, I will present our research on AI for science over the past few years. I will start by briefly showing how we can discover closed-form prediction functions from cross-sectional data using symbolic metamodels. Then, I will introduce a new method, called D-CODE, which discovers closed-form ordinary differential equations (ODEs) from observed trajectories (longitudinal data).This method can only describe observable variables, yet many important variables in medical settings are often not observable. Hence, I will subsequently present the latent hybridisation model (LHM) that integrates a system of ODEs with machine-learned neural ODEs to fully describe the dynamics of the complex systems. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities. To solve these challenges, I will then present Neural Laplace, with which we can learn diverse classes of differential equations in the Laplace domain. I will conclude by presenting next research frontiers, including recent work on discovering partial differential questions from data (D-CIPHER). While these works are applicable in numerous scientific domains, in this talk I will illustrate the various works with examples from medicine, ranging from understanding cancer evolution to treating Covid-19. This work is joint work with Zhaozhi Qian, Krzysztof Kacprzyk and Sam Holt. | 3 | |||||
| Invited talk | Fondazione Policlinico Universitario | Maters Lecture | Guest lecture | Machine learning for healthcare: challenges, opportunities and current status | 2022/12/19 | 2022 | TBD | TBD | TBD | 3 | |||||
| Invited talk | Alan Turing Institute | Bridging machine learning and behaviour models talk | Guest lecture | Quantitative Epistemology: A machine learning field aimed at creating a new human-machine partnership | 2023/01/19 | 2023 | https://www.turing.ac.uk/research/interest-groups/bridging-machine-learning-and-behaviour-models | TBD | TBD | 3 | |||||
| Invited talk | UCL | CMIC/WEISS Joint Seminar Series | Guest lecture | New Frontiers in Machine Learning Interpretabilit | 2023/01/25 | 2023 | https://ucl.zoom.us/j/99464005163?pwd=ZFdURkJ4TjJIeGVhbXpTclhuNE9WUT09 | TBD | TBD | 3 | |||||
| Seminar | UCL | Machine Learning MSc Seminar | Guest lecture | Discovering diverse classes of equations in medicine and beyond | 2023/01/30 | 2023 | TBD | TBD | Artificial Intelligence (AI) offers the promise of revolutionizing the way scientific discoveries are made and significantly accelerating their pace. This is important for numerous fields of study, including medicine. In this talk, I will present our research on AI for science over the past few years. I will start by briefly showing how we can discover closed-form prediction functions from cross-sectional data using symbolic metamodels. Then, I will introduce a new method, called D-CODE, which discovers closed-form ordinary differential equations (ODEs) from observed trajectories (longitudinal data).This method can only describe observable variables, yet many important variables in medical settings are often not observable. Hence, I will subsequently present the latent hybridisation model (LHM) that integrates a system of ODEs with machine-learned neural ODEs to fully describe the dynamics of the complex systems. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities. To solve these challenges, I will then present Neural Laplace, with which we can learn diverse classes of differential equations in the Laplace domain. I will conclude by presenting next research frontiers, including recent work on discovering partial differential questions from data (D-CIPHER). While these works are applicable in numerous scientific domains, in this talk I will illustrate the various works with examples from medicine, ranging from understanding cancer evolution to treating Covid-19. This work is joint work with Zhaozhi Qian, Krzysztof Kacprzyk and Sam Holt. | 3 | |||||
| Invited talk | Oxford | Exploring human and machine intelligence (Oxford) | Guest lecture | TBD | 2023/01/31 | 2023 | TBD | TBD | TBD | 3 | |||||
| Seminar | Imperial | XAI Seminar | Guest lecture | TBD | 2023 | http://xaiseminars.doc.ic.ac.uk/ | XAI has witnessed unprecedented growth in both academia and industry in recent years (alongside AI itself), given its crucial role in supporting human-AI partnerships whereby (potentially opaque) data-driven AI methods can be intelligibly and safely deployed by humans in a variety of settings, such as finance, healthcare and law. XAI is positioned at the intersection of AI, human-computer interaction, the social sciences (and in particular psychology) and applications. | TBD | 3 | ||||||
| Invited talk | Chat to top 200 Executives in Accenture | Guest lecture | TBD | 2023/03/06 | 2023 | TBD | TBD | TBD | 3 | ||||||
| Seminar | School of Mathematics, University of Leeds | Seminar in University of Leeds | Guest lecture | TBD | 2023/03/17 | 2023 | TBD | TBD | TBD | 3 | |||||
| Seminar | Wellcome Sanger Institute | HDR UK Cambridge seminar series | Guest lecture | ML in healthcare: New role not eye roll | 2023/03/23 | 2023 | https://www.eventbrite.co.uk/e/hdr-uk-cambridge-seminar-series-guest-speaker-mihaela-van-der-schaar-tickets-566659392497 | Seminars are aimed at a general scientific audience and are open to all. | Mihaela will describe her work in the field of machine learning for healthcare which involved developing improved methods for forecasting individual risks and for identifying covariates that are most important for forecasting risk. | 3 | |||||
| Invited talk | J.P. Morgan | J.P. Morgan workshop | Guest lecture | Facilitating innovative use cases of synthetic data in different data modalities | 2023/05/11 | 2023 | TBD | TBD | TBD | 3 | |||||
| Invited talk | ELLIS | ELLIS summer school | Guest lecture | Machine learning for healthcare and biology | 2023/06/14 | 2023 | https://www.idsai.manchester.ac.uk/connect/events/ellis-summer-school-2023/schedule/ | Manchester’s European Laboratory for Learning and Intelligent Systems (ELLIS) unit is hosting a Summer School in June 2023 which will bring participants up-to-speed on the latest methods and technologies in machine learning with a focus on healthcare and biology. | TBD | 3 | |||||
| Panel | Moonfire Pulse 2023 | Guest lecture | Can AI transform healthcare? | 2023/06/14 | 2023 | TBD | TBD | TBD | 3 | ||||||
| Invited talk | (Various) | 16th International Symposium on Signals, Circuits, and Systems (ISSCS 2023) | Guest lecture | Transforming Healthcare using Machine Learning | 2023/07/14 | 2023 | http://scs.etti.tuiasi.ro:81/isscs2023/ | TBD | Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of clinical data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. In this talk I will show how, by working closely with clinicians, we are developing cutting-edge machine learning methods to create tangible clinical impact and how in turn, medicine is driving new advances in machine learning. | 3 | |||||
| Invited talk | i-sense | i-sense 10-year anniversary meeting | Guest lecture | The Future of Early Disease Detection and Diagnosis | 2023/07/07 | 2023 | https://www.eventbrite.co.uk/e/i-sense-10-year-anniversary-diagnostics-conference-tickets-588399818697 | TBD | TBD | 3 | |||||
| Seminar | Seminar at the Health Data Science Centre of Human Technopole | Guest lecture | From Theory to Bedside: Clinician-Centric Machine Learning for Tangible Impact | 2023/09/01 | 2023 | TBD | TBD | TBD | 3 | ||||||
| Seminar | Synthetic data generation for health Seminar | Guest lecture | Synthetic Data: Powerful creation not second rate copy | 2023/10/26 | 2023 | TBD | TBD | TBD | 3 | ||||||
| Invited talk | Bosch Center for Artificial Intelligence | Bosch Distinguished Lecture Series on Machine Learning | Guest lecture | Synthetic Data: Powerful creation not second rate copy | 2023/10/05 | 2023 | https://www.bosch-ai.com/research/distinguished-lecture-series/ | Our Distinguished Lecture series serves as a gateway to a diverse and dynamic realm, where people, backgrounds, ideas and knowledge converge. From pioneering innovators to visionary leaders, our Distinguished Lecture series features an exceptional lineup of experts presenting their findings and work to the BCAI community. | TBD | 3 | |||||
| Invited talk | AI in Medicine joint Cambridge-Singapore Symposium | Guest lecture | TBD | 2023/10/17 | 2023 | https://www.eventbrite.co.uk/e/machine-learning-artificial-intelligence-medicine-tickets-704693245467 | This exciting one-day event is brought to you in partnership between the University of Cambridge and the National University of Singapore | TBD | 3 | ||||||
| Invited talk | UCL AI Society's Journal Club Event | Guest lecture | Next Frontiers on Reality-centric Machine Learning: From theory to algorithms | 2023/11/13 | 2023 | https://uclaisociety.co.uk/ | The goal of this event is to present research in a manner that's both approachable and insightful, showing students various paths of AI research and inspiring them to pursue a more research-oriented path in their future. | TBD | 3 | ||||||
| Invited talk | The Institute for Medical Data Science (IMDS) | UW's Medical Data Science Seminar | Guest lecture | Pushing Medical Frontiers: AI-Driven Breakthroughs in Medicine | 2023/11/21 | 2023 | https://myemail.constantcontact.com/Medical-Data-Science-Seminar-NEXT-Tuesday--November-21st-at-1pm-PT.html?soid=1140027809300&aid=bAiFD26vOfk | Established in December of 2021, the Institute for Medical Data Science (IMDS) organizes a seminar series that includes key experts in the field with a focus on clinical implementation and the study of AI and data science in the practice of patient health and healthcare. We are inviting a select number of eminent speakers chosen through a committee involving UW Medicine leadership. We involve faculty and students in this initiative and students will be hosting the seminar events. | TBD | 3 | |||||
| Invited talk | French consortium DIGPHAT | Guest lecture | TBD | 2023/11/22 | 2023 | TBD | TBD | TBD | 3 | ||||||
| Invited talk | BU Talk Artificial Intelligence Research (AIR) Distinguished Speaker Series | Guest lecture | Time: The next frontier in Machine Learning | 2024/01/24 | 2024 | https://www.bu.edu/hic/centers-initiatives-labs/air/ | The Artificial Intelligence Research (AIR) initiative at BU is a cross-disciplinary research initiative focused on machine intelligence. It brings together researchers whose work aims to create intelligent systems that reliably make decisions, reason about data, and communicate with humans. | In this talk, I aim to illuminate the underemphasized yet critical dimension in machine learning: time. I contend that time harbors the potential to revolutionize machine learning methodologies and their applications in numerous domains from healthcare to engineering to finance. This presentation underscores the opportunities and challenges that emerge from integrating temporal dynamics into machine learning models, enriching prediction accuracy, inference robustness, causality, and conceptual understanding. | 3 | ||||||
| Invited talk | Apple | Machine Learning Research Seminar | Guest lecture | Synthetic Data: Powerful creation not second rate copy | 2024/01/18 | 2024 | TBD | TBD | TBD | 3 | |||||
| Invited talk | Distinguished Speakers: Oxford Women in Computer Science | Guest lecture | Time: The next frontier in Machine Learning | 2024/01/22 | 2024 | https://www.cs.ox.ac.uk/seminars/distinguishedspeakers/previous.html | Oxford Women In Computer Science Society organises talks by distinguished speakers from academia or industry who - through talking about their career paths and experiences - could become role models and inspirations to attendees. | In this talk, I aim to illuminate the underemphasized yet critical dimension in machine learning: time. I contend that time harbors the potential to revolutionize machine learning methodologies and their applications in numerous domains from healthcare to engineering to finance. This presentation underscores the opportunities and challenges that emerge from integrating temporal dynamics into machine learning models, enriching prediction accuracy, inference robustness, causality, and conceptual understanding. | 3 | ||||||
| Invited talk | Faculty of Applied Science, TU Delft | ImPhys Colloquium | Guest lecture | Time - The next frontier in Machine Learning | 2024/02/08 | 2024 | https://www.tudelft.nl/evenementen/2023/tnw/tnw-imphys/imphys-colloquium-mihaela-van-der-schaar | TBD | In this talk, I aim to illuminate the underemphasized yet critical dimension in machine learning: time. I contend that time harbors the potential to revolutionize machine learning methodologies and their applications in numerous domains from healthcare to engineering to finance. This presentation underscores the opportunities and challenges that emerge from integrating temporal dynamics into machine learning models, enriching prediction accuracy, inference robustness, causality, and conceptual understanding. | 3 | |||||
| Invited talk | Imperial | XAI Seminar @ Imperial | Guest lecture | The Road to Transparent AI: Latest Breakthroughs in Time-Series Interpretability | 2024/02/19 | 2024 | https://xaiseminars.doc.ic.ac.uk/ | This seminar series focuses on all aspects of XAI, ranging from methods to applications. | In this talk, I will describe several new breakthroughs on ML interpretability. This will include 1) powerful ways to interpret ML methods for time-series forecasting, clustering (phenotyping), and heterogeneous treatment effect estimation, 2) provide personalized explanations of ML methods which refer to the unique experience of the user of the ML method, and 3) autonomously discover scientific concepts using concept activation regions, which are generalizations of concept-based explanations. To learn more about our work in this area - see our website dedicated to this topic - https://www.vanderschaar-lab.com/interpretable-machine-learning/ and our github - https://github.com/vanderschaarlab/Interpretability | 3 | |||||
| Seminar | WINDSMATH | WINDSMATH seminar series: Women in Data Science and Mathematics | Guest lecture | AI for Science: Discovering Diverse Classes of Governing Equations in Medicine and Beyond | 2024/02/26 | 2024 | https://www.windsmath.com/ | The goal of our seminar series "Women in Data Science and Mathematics" is twofold: on the one hand, it aims to show recent advancements in interdisciplinary research in mathematics and data science, and on the other hand, it should increase the visibility of underrepresented groups in the field by featuring high-quality research talks delivered by female and non-binary researchers. This approach will help to address structural bias and create role models, encouraging women to pursue research in this area. | Artificial Intelligence (AI) offers the promise of revolutionizing the way scientific discoveries are made and significantly accelerating their pace. This is important for numerous fields of study, including medicine. In this talk, I will present our research on AI for science over the past few years. I will start by briefly showing how we can discover closed-form prediction functions from cross-sectional data using symbolic metamodels. Then, I will introduce a new method, called D-CODE, which discovers closed-form ordinary differential equations (ODEs) from observed trajectories (longitudinal data).This method can only describe observable variables, yet many important variables in medical settings are often not observable. Hence, I will subsequently present the latent hybridisation model (LHM) that integrates a system of ODEs with machine-learned neural ODEs to fully describe the dynamics of the complex systems. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities. To solve these challenges, I will then present Neural Laplace, with which we can learn diverse classes of differential equations in the Laplace domain. I will conclude by presenting next research frontiers, including recent work on discovering partial differential questions from data (D-CIPHER). While these works are applicable in numerous scientific domains, in this talk I will illustrate the various works with examples from medicine, ranging from understanding cancer evolution to treating Covid-19. This work is joint work with Zhaozhi Qian, Krzysztof Kacprzyk and Sam Holt. | 3 | |||||
| Invited talk | Stanford University | 2024 IBIIS & AIMI Seminar Series | Guest lecture | TBD | 2024/04/03 | 2024 | https://aimi.stanford.edu/events/ibiis-aimi-seminar-mihaela-van-der-schaar-phd | The goal of this seminar is to educate scientists from different disciplines about the rapidly evolving field of medical AI applications and/or the latest in applied computational and biomedical imaging research by hearing an overview of your work. | TBD | 3 | |||||
| Invited talk | ETH CSNOW (network of women in computer science) celebration | Guest lecture | TBD | 2024/04/09 | 2024 | https://csnow.inf.ethz.ch/en/ | CSNOW was founded in 1993 and is part of the Department of Computer Science. The goal of the network is to abolish gender-based barriers and prejudices, and to create a study and work environment that is comfortable for all. | TBD | 3 | ||||||
| Invited talk | ETH Zurich | Distinguished Computer Science Colloquium | Guest lecture | The next frontier in Machine Learning | 2024/04/10 | 2024 | https://inf.ethz.ch/news-and-events/events/colloquium.html | Renowned international computer scientists take the floor at our distinguished colloquium series, to present topics across all areas of computer science. | In this talk, I aim to illuminate the underemphasized yet critical dimension in machine learning: time. I contend that time harbors the potential to revolutionize machine learning methodologies and their applications in numerous domains from healthcare to engineering to finance. This presentation underscores the opportunities and challenges that emerge from integrating temporal dynamics into machine learning models, enriching prediction accuracy, inference robustness, causality, and conceptual understanding. | 3 | |||||
| Invited talk | Online Causal Inference Seminar | Guest lecture | The (Causal) Discovery Ladder: Unravelling Governing Equations and Beyond Using Machine Learning | 2024/04/16 | 2024 | https://sites.google.com/view/ocis/ | We bring in speakers providing a variety of perspectives on causal inference, from theory to applications. | TBD | 3 | ||||||
| Invited talk | I-X Seminar | Guest lecture | Pushing the boundaries of what is possible with ML: The Reality-Centric AI Agenda | 2024/11/05 | 2024 | https://ix.imperial.ac.uk/event/i-x-seminar-pushing-the-boundaries-of-what-is-possible-with-ml-the-reality-centric-ai-agenda-with-professor-mihaela-van-der-schaar/ | I-X is an Imperial initiative focused on interdisciplinary research in Artificial Intelligence with close connections to industry. | This talk introduces the Reality-Centric AI agenda, an approach that tackles the complexity and challenges of the real world through machine learning (ML). I will explore how we are expanding the boundaries of ML to advance this vision—ranging from using AI to drive scientific discovery, to pioneering new directions in causal inference over time, to making AI effective with real-world data, and to empowering individuals with AI. Join me to dive into this emerging research agenda and explore how we can join forces to shape the future. | 3 | ||||||
| Invited talk | Spinoza | Technical AI Masterclass | Guest lecture | AI with medical data | 2025/03/12 | 2025 | https://www.amsterdamumc.org/en/research/events/lecture-series-spinoza-guest-professorship-mihaela-van-der-schaar.htm | Amsterdam UMC is excited to announce that Professor Mihaela van der Schaar has been appointed as Spinoza Guest Professor at Amsterdam UMC. In March, she will deliver an exclusive lecture series that will explore the latest advancements in AI and its transformative impact on clinical decision-making, patient outcomes, and healthcare innovation. | TBD | 3 | |||||
| Invited talk | Spinoza | Medical AI Masterclass | Guest lecture | How to handle big data | 2025/03/13 | 2025 | https://www.amsterdamumc.org/en/research/events/lecture-series-spinoza-guest-professorship-mihaela-van-der-schaar.htm | Amsterdam UMC is excited to announce that Professor Mihaela van der Schaar has been appointed as Spinoza Guest Professor at Amsterdam UMC. In March, she will deliver an exclusive lecture series that will explore the latest advancements in AI and its transformative impact on clinical decision-making, patient outcomes, and healthcare innovation. | TBD | 3 | |||||
| Invited talk | Spinoza | Spinoza Lecture | Guest lecture | Medical AI: the future is now | 2025/03/13 | 2025 | https://www.amsterdamumc.org/en/research/events/lecture-series-spinoza-guest-professorship-mihaela-van-der-schaar.htm | Amsterdam UMC is excited to announce that Professor Mihaela van der Schaar has been appointed as Spinoza Guest Professor at Amsterdam UMC. In March, she will deliver an exclusive lecture series that will explore the latest advancements in AI and its transformative impact on clinical decision-making, patient outcomes, and healthcare innovation. | TBD | 3 | |||||
| Seminar | MOX Colloquia | Guest lecture | Can we discover fundamental laws from data using AI? | 2025/04/10 | 2025 | https://mox.polimi.it/mox-colloquia-seminars-list/mox-seminars/?id_evento=2557 | TBD | Discovering fundamental laws governing systems from observational data has long been a hallmark of scientific inquiry. In this talk, I will discuss how recent advances in AI and machine learning enable the automated discovery of scientific laws and governing equations directly from data, revolutionizing the way we unravel system dynamics in numerous domains, including medicine and pharmacology. I will highlight how AI-driven methods uncover underlying principles, from classical physics to biological systems to medicine, and offer insights into future possibilities—transforming data-driven observations into interpretable and actionable scientific knowledge. Yet, can we push this boundary further—going beyond equations entirely? I will introduce direct semantic modeling, a novel paradigm where AI learns the behavior of dynamical systems directly from data without relying on closed-form equations. This semantic approach offers intuitive, human-interpretable insights into system evolution, marking a transformative leap in scientific discovery. (This talk is based on recent research with Krzysztof Kacprzyk, Tennison Liu and Sam Holt.) | 3 | ||||||
| Invited talk | KCL | KCL Maths Colloquium | Guest lecture | Uncovering the Laws of Nature with AI: Discovering Dynamical Systems and Beyond | 2025/09/26 | 2025 | https://www.kcl.ac.uk/events/mathematical-colloquium-prof.-mihaela-van-der-schaar | The King's Mathematical Colloquium is the main periodic mathematical event at King's College London. Internationally prestigious speakers from all areas of Pure and Applied Mathematics provide an overview of their research topics, introduce their current investigations and give their perspective on future advancements in the field. The Colloquium is aimed at a general mathematical audience: students and researchers from all fields of mathematics and physics have the opportunity to hear about new stimulating ideas and relevant research problems. | TBD | 3 | |||||
| Invited talk | King's College London | Informatics Annual Lecture 2026: inspiring excellence in computer science | Guest lecture | Rethinking the Role of Scientific Discovery in the Era of AI | 2026/06/25 | 2026 | https://www.kcl.ac.uk/events/informatics-annual-lecture-2026-inspiring-excellence-in-computer-science | Established in 2024, the Department of Informatics flagship event provides a platform for distinguished academics in the field of computer science, AI and data science to showcase their groundbreaking contributions that are inspiring the future of technology and innovation. | In this talk, I present a framework that rethinks discovery as an agentic process, in which networks of interacting AI agents and human experts jointly explore, critique, and refine hypotheses under shared norms of evaluation. I will discuss how heterogeneous, LLM-based research agents can self-organize into evolving networks that generate, review, and extend findings—trading off novelty, quality, and diversity over time. Drawing on results from biomedical discovery, including cancer cohort analyses, I will argue that such socially structured agent networks can sustain large-scale exploratory discovery, complementing experimental science while reshaping how knowledge is generated, accumulated, and validated in the era of AI. | 3 |
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