Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).
Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.
Publications and research impact
Mihaela has published more than 600 papers, including 280 journal articles and over 300 conference papers. She has also authored several books and book chapters.
In terms of recent academic output in top machine learning venues, Mihaela ranked among the 10 researchers with the most accepted papers at ICML 2020 (and was the only female researcher on the list); she was only one of two women among the 40 researchers with the most accepted papers at NeurIPS 2019. In 2019, Mihaela was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the U.K. She was also elected a 2019 “Star in Computer Networking and Communications” by N²Women.
While her current research is firmly centered around machine learning for healthcare, Mihaela’s previous work is exceptionally diverse and impactful, spanning a wide range of fields including multimedia compression, processing and transmission; multi-user wireless networking; applications of game-theoretic ideas in engineering contexts; and multi-agent learning in engineering systems.
Impact: machine learning for healthcare
Mihaela’s ground-breaking work on machine learning for healthcare includes the development of improved methods for forecasting individual risks and for identifying covariates that are most important for forecasting risk. Her work has identified better treatment options for patients with heart failure, cystic fibrosis, breast cancer, and Alzheimer’s disease.
Mihaela has also developed a state-of-the-art predictive model (already implemented in a number of hospitals) to manage hospitalised patients at risk of sudden deterioration, in addition to a framework (currently undergoing trials in the UK, in collaboration with NHS Digital and Public Health England) for more efficient allocation of limited resources across hospitals during the current COVID-19 pandemic.
Mihaela has also contributed to a number of discussions regarding policies and guidelines at the highest levels. As part of the 2019 NHS Topol Review, she co-chaired the Expert Advisory Panel on Artificial Intelligence and Robotics. Additionally, Mihaela contributed a chapter to the U.K.’s 2018 Annual Report of the Chief Medical Officer, discussing how machine learning can transform medicine and healthcare.
Impact: previous research
While Mihaela’s research focus is now firmly on machine learning, AI, and operations research for healthcare and medicine, her previous research achieved substantial impact in the areas of multimedia communications, compression and processing, and real-time stream mining.
In her early career, while working at Philips (and simultaneously completing her Ph.D.), Mihaela developed both the theoretical foundations and the first practical algorithm for streaming video. Her contributions remain highly visible, thanks to their inclusion in commercial products (including an award-winning Philips webcam) and the 35 U.S. patents that she has been granted. Between 1999 and 2003, she was Philips’ representative to the International Standards Organization (ISO), leading several of the working groups that developed and wrote the MPEG-4 standards for streaming video, to which she contributed more than 40 papers.
The majority of Mihaela’s 35 patents can be found on the U.S. PTO’s website.
Mihaela has developed new methods for detecting, characterising, and forecasting complex events (including road traffic collisions, popularity of videos on social networks, and energy supply and demand in smart-grids, among others) based on a novel machine learning and real-time stream mining paradigm. These methods have been implemented as part of the IBM InfoSpheres Platform for a Smarter Planet.
Professional contributions, leadership, and mentorship
Mihaela served as Editor-in-Chief of IEEE Transactions on Multimedia between 2011 and 2013, and has held Guest Editor, Associate Editor, and Senior Editorial Board Member roles for numerous other journals (including IEEE Journal on Selected Topics in Signal Processing). As part of the 2018 NHS Topol Review, she co-chaired the Expert Advisory Panel on Artificial Intelligence and Robotics.
Mihaela was Founder and Director of the UCLA Center for Engineering Economics, Learning, and Networks (2011-2016). She is the Founder and Director of the Cambridge Centre for AI in Medicine (2020) and Co-Director of the European Laboratory for Learning and Intelligent Systems (ELLIS) (2019).
Mihaela is a leader and mentor in both science and in science communication. Her Ph.D. students and postdocs have gone on to excellent academic positions internationally (across 4 continents!) and are becoming recognised as leaders in their own right.
Mihaela has also organised numerous outreach activities, several of which are dedicated to empowering women in engineering and computer science. She recently launched Inspiration Exchange, an online series of engagement sessions aiming to share ideas with young researchers in machine learning for healthcare. To build partnerships with clinicians, she created Revolutionizing Healthcare, a regular online engagement series which now has roughly 400 clinicians from around the world registered to participate.
Public speaking and engagement (since 2020)
Mihaela has given plenary and keynote talks at more than thirty international conferences (including, most recently, ICLR 2020 and ICME 2020), tutorials at more than thirty venues (including, most recently, ICML 2021), and summer schools (including at MLSS 2020). She has also delivered invited lectures all over the world (including the Oon International Award Lecture at the University of Cambridge in 2018, the Very Reverend Derek Hole Lecture in 2019 at the University of Leicester and the Alan Tyler Lecture in 2019 at the University of Oxford, as well as Distinguished Seminars at MIT in 2019 and 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|
|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|
|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||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|
|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|
Prizes, awards, and elected positions
Mihaela has received numerous awards and honours for her work. While at Philips, she was awarded three ISO awards for her work leading several MPEG Working Groups, and also received the Philips “Make a Difference” Award. In the course of a single academic year, she received an NSF CAREER award, an Okawa Foundation Award, and an IBM Watson Exploratory Stream Analytics Innovation Award as well as three IBM Faculty Awards.
Mihaela was elected an IEEE Fellow in 2009 and a Distinguished Lecturer for IEEE for 2011-2012, despite being substantially younger than others to have received those honours. She won an IEEE Circuits and Systems Society Darlington Award in 2011, and a Royal Society Wolfson Research Merit Award in 2016 (which she declined, in favour of the endowed Man Chair at Oxford).
Mihaela was awarded the Oon Prize on Preventative Medicine from the University of Cambridge in 2018. In 2019 she was named a Star in Computer Networking and Communications by N2Women (a community of researchers in the fields of networking and communications), and was identified by the National Endowment for Science Technology and the Arts (NESTA) as the most-cited female AI researcher in the UK.
List of key prizes, awards, and elected positions
2020 Received Journal of the American College of Radiology “Best of 2020” award for article on machine learning for mammography
2019 Identified by the National Endowment for Science Technology and the Arts (NESTA) as the most-cited female AI researcher in the U.K.
2019 Named “Star in Computer Networking and Communications” by N2Women
2018 Received Oon Award for Preventative Medicine (University of Cambridge)
2018 Received IJCAI International Workshop on Biomedical informatics with Optimization and Machine learning (BOOM) Best Paper Award
2016 Accepted MAN Professorship (statutory professorship) at Oxford University
2016 Royal Society Wolfson Research Merit Award (declined in favour of Oxford professorship)
2014 – 2016 Senior editorial board member of IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS)
2011 – 2013 Editor-in-Chief of IEEE Transactions On Multimedia
2011 – 2012 Senior editorial board member of IEEE Journal on Selected Topics on Signal Processing
2011 – 2012 Distinguished Speaker for IEEE Communications Society
2011 Elected Chancellor’s Professor at University of California Los Angeles (endowed position)
2011 IEEE Circuits and Systems Society Darlington Award
2009 Elected IEEE Fellow
2008 Exploratory Stream Analytics Innovation Award from IBM Research Watson
2006 Elected to Image and Multidimensional Signal Processing Technical Committee, IEEE Signal Processing Society
2006 IEEE Transactions on Circuits and Systems for Video Technology Best Paper Award
2006 Okawa Foundation Award
2005 2007 2008 IBM Faculty Awards (x3)
2005 ISO Award for technical contributions to the MPEG-21 Multimedia framework
2004 National Science Foundation CAREER Award
2003 2004 ISO Awards (x2) for technical contributions to the MPEG-4 Visual standard
2002 Philips “Make a Difference” Award
2002 Elected to Multimedia Signal Processing Technical Committee, IEEE Signal Processing Society
1996 Awarded one of the first five European Union TEMPUS scholarships
Funding and clinical partnerships
The groundbreaking research conducted by Mihaela and our lab is made possible thanks to strong support from a range of funding partners across the public and private sectors.
We are also grateful for the support and guidance of extensive network of clinical colleagues, who have expertly framed real-world problems for us to solve and helped us understand the complexities of healthcare systems.
Eindhoven University of Technology
BSc. and MSc. (Electrical and Computer Engineering)
Ph.D. (Electrical and Computer Engineering)
Mihaela received one of the first five European Union TEMPUS scholarships, enabling her to continue her education at the Eindhoven University of Technology, The Netherlands, where she received a B.Sc. and M.Sc. in 1996, and a Ph.D. in 2001, all in Electrical and Computer Engineering.
Senior Research Staff, Wireless Communication and Networking Department
Aged 24, Mihaela started her independent research career with Philips Research, first in the Netherlands (1996-1998, as Research Scientist) and then in the U.S. (1998-2003, as Senior Research Scientist and Project Leader).
Assistant Professor, Electrical and Computer Engineering Department
Mihaela then embarked on her career as an academic, starting as Assistant Professor at the University of California, Davis in 2003 and subsequently taking an equivalent role at the University of California, Los Angeles (UCLA) in 2005.
Assistant Professor, Electrical Engineering Department
Associate Professor, Electrical Engineering Department
Professor, Electrical and Computer Engineering Department
At UCLA, Mihaela advanced rapidly from Assistant Professor to Associate Professor (2007), Professor (2010), and Chancellor’s Professor (2011) in the Department of Electrical and Computer Engineering.
(The University of California describes Chancellor’s Professor as a distinguished title “designed for persons who have earned the title of Full Professor and who have demonstrated unusual academic merit and whose continued promise for scholarly achievement is unusually high.” Only 3% of all Full Professors in the University of California system hold this title.)
University of Oxford
MAN Professor, Department of Engineering Sciences
Mihaela was Man Professor in the Department of Engineering Sciences at the University of Oxford between 2016 and 2018.
The Alan Turing Institute
Mihaela has been a Turing Fellow at the Alan Turing Institute since 2016.
University of Cambridge
John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine
Since October 2018, Mihaela has been John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine in the Department of Applied Mathematics and Theoretical Physics, the Department of Engineering, and the Department of Public Health at the University of Cambridge.