van der Schaar Lab

Research Team

Prof Mihaela van der Schaar

Head of the van der Schaar lab

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge. 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 and Fellow of the Royal Society in 2024. She has received numerous awards, including the Johann Anton Merck Award (2024), 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. She was a Turing Fellow at The Alan Turing Institute in London between 2016 and 2024.

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.

More about Mihaela and her impact here.

PhD Students

Anita Kriz

PhD Student – joined the lab in 2025

Anita Kriz obtained her M.Sc. in Electrical Engineering at McGill University, supervised by Professor Tal Arbel in the Probabilistic Vision Group, where her research lay at the intersection of machine learning and medical imaging. Her thesis focused on advancing the trustworthiness of AI in healthcare, developing methods to mitigate spurious correlations in medical images and calibrate the overconfidence of large language models. Her work has been published at venues including ICCV (Workshop), CVPR (Workshop), and MIDL.

Prior to her graduate studies, Anita earned her B.Eng. in Bioengineering with a minor in Applied Artificial Intelligence at McGill, where she pursued interdisciplinary research spanning AI in healthcare and biomaterials. She also co-founded elleFA, a startup developing non-invasive diagnostic tools for women’s health, which won McGill’s Capstone Design Prize among all final-year engineering students. Anita has been recognized with several honors, including the FRQNT Master’s Scholarship (ranked 3rd in her category), the Mila Women in AI Excellence Scholarship, and multiple NSERC research awards.

Looking ahead to her PhD, Anita aims to contribute to the lab’s reality-centric research agenda by advancing the safe and meaningful use of foundation models in healthcare and scientific discovery. Her research interests broadly center on trustworthy AI, with a focus on ensuring that large-scale models are reliable, interpretable, and aligned with the needs of clinical decision-making.

Anita’s research is funded by Sanofi.

Antonin Berthon

PhD Student – joined the lab in 2024

Antonin holds a Diplome d’Ingénieur from Ecole des Ponts ParisTech and the Master MVA (Mathematics, Vision, Learning) at ENS Paris-Saclay. He conducted his Master’s thesis as a research intern at the RIKEN Center for Advanced Intelligence Project in Tokyo, where he worked on Machine Learning methods for noisy label data, resulting in a paper published as a long oral at ICML 2021.

He then worked for four years in the Cambridge-based neuroscience start-up BIOS Health, which focuses on decoding signals in the autonomous nervous system to develop novel neuromodulation therapies. Working on complex neuro and physiological time series datasets has been for him a fascinating engineering and research challenge, and collaborating with cross-functional teams strengthened his interest in working with expert from various fields.

This experience highlighted the significant gap between controlled ML research environments and the complexity of real-world data and challenges. For this reason, he is excited to join the van der Schaar lab and contribute to its reality-centric agenda. He is particularly interested in designing interpretable ML models for real-world applications, enabling wider use of AI in domains like healthcare and education while fostering safe and beneficial human-machine collaborations.

Outside work, he can be found running on trails, climbing on walls, or playing and making music.

Benjamin Lapostolle

PhD Student – joined the lab in 2025

Benjamin holds a Diplôme d’Ingénieur from École Polytechnique in Paris, where he specialized in machine learning and applied mathematics. He later pursued the MVA master’s program at École Normale Supérieure Paris-Saclay, further deepening his expertise in statistical learning and data-driven modelling. 

Throughout his studies, Benjamin engaged in various research projects and internships, exploring applications of machine learning in different domains. At the Czech Technical University, he developed computer vision tools to enhance the analysis of histological images. Later, at the MIND Inria lab, he worked on generative models, focusing on inverse problems to improve the reconstruction of MRI data. 

In the lab, Benjamin is particularly interested in the intersection of mathematical modelling and data science. He aims to explore ways to improve our understanding of the most advanced machine learning architectures to enhance interpretability and develop better intuitions for refining them. 

Curious by nature, he also enjoys exploring other fields, particularly stochastic processes and probability theory, and seeks to connect this knowledge to side projects.

Claudio Fanconi

PhD Student – joined the lab in 2024

Before joining the van der Schaar lab, Claudio completed his BSc and MSc in Electrical Engineering and Information Technology from ETH Zürich, focusing on computer vision, natural language processing (NLP), and uncertainty quantification. He conducted his Master’s thesis as a visiting research student at Stanford University in Biomedical Informatics and has published his research in various conferences and journals such as The Lancet eBioMedicine, EMNLP, ICML and AMIA.

Professionally, Claudio previously worked as a research scientist intern at Sony AI, focusing on robotic perception and computer vision algorithms. Additionally, he interned as a management consultant at McKinsey & Company, working on data science projects for the retail industry, and at IBM as a machine learning engineer, researching near-duplicate matching with NLP.

During his PhD, Claudio aims to explore a wide range of topics in machine learning models applied to high-stakes environments, such as medicine. He is particularly interested in advancing research on multi-modal neural network interpretability, robustness, and uncertainty estimation. He is excited to join the lab to learn and acquire lots of new skills and knowledge from exceptional individuals.

In his leisure time, Claudio can often be found on a tennis court, on a snowboard slope, or in an ice hockey rink.

Claudio’s research is funded by Canon Medical.

Chris Chiu

PhD Student – joined the lab in 2025

Prior to joining the lab, Chris worked as a Clinical AI Engineer at Harrison.ai, developing AI co-pilots for radiologists. Previously, he was a medical doctor, co-founder of two healthcare startups, and an angel investor specializing in healthtech. He holds an MD from the University of New South Wales and an MS in Computer Science from the Georgia Institute of Technology.

Chris’s previous research experience covered neuroimmunology, MRI signal processing, and the application of AI in medicine. He transitioned from medical to machine learning research to further explore replicating biological intelligence in machines. His current work focuses on investigating how multi-agent systems coordinate and form shared representations through communication and game-theoretic reasoning. He aims to develop a theoretical framework for continual learning and metacognition, enabling agents to build internal world models that incorporate causal understanding and a capacity for self-reference.

Outside of research, Chris enjoys playing board games, reading manga and science fiction, and building Gunpla models. He occasionally goes to the gym and plays badminton.

Chris’ research is funded by Apple

Harry Amad

PhD Student – joined the lab in 2024

Harry holds a BSc in Data Science from the University of Melbourne, where he was a Chancellor’s Scholar. He has also recently completed an MSc in Statistical Science from the University of Oxford. For his master’s thesis, he investigated diffusion models, and how different algorithms guide conditional generation of tabular and image data. Harry also has professional experience as an intern at EY in the tax, technology, and transformation team. In this role he helped develop software for the analysis and automation of tax functions for businesses. 

For his PhD, Harry hopes to explore a range of topics which he is passionate about, including synthetic data, simulations, and causality. He is particularly excited to engage in reality-centric research, and push the real-world potential of machine learning in healthcare. 

Outside of the lab, Harry is an avid sports fan, and he can often be found watching Australian rules football in the early UK hours. He also has a passion for rowing, having competed for Oxford in the men’s reserve boat race.

Harry’s research is funded by Canon Medical. 

Julianna Piskorz

PhD Student – joined the lab in 2023

Julianna is a graduate of the BSc in Mathematics and Statistics at Imperial College London. Her desire to study the foundations of Machine Learning motivated her to undertake the MSc in Statistical Science course at the University of Oxford, which she has recently completed. In her Master Thesis, supervised by Prof. Patrick Rebeschini, she explored and compared different supervised learning methods derived from the infinite-width limits of neural networks.

Prior to joining the lab, Julianna has also completed an internship at Apple, during which she designed a solution which combined large volumes of diverse data from across different systems and departments to provide the Search Engine Optimisation team with smart and informed insights. She has also worked with a Venture Capital startup to create a tool allowing to calculate the similarity in the investing patterns and the dependencies between different VC firms, and visualise these in a clear and informative way.

Julianna’s studentship is founded by Astra Zeneca.

Kasia Kobalczyk

PhD Student – joined the lab in 2023

Kasia joined our lab after completing her MASt in Mathematical Statistics (Part III) at the University of Cambridge. Prior to this, she earned her BSc in Mathematics and Statistics from the University of Warwick, where she was recognised for her top-of-class performance and outstanding results in mathematics-oriented subjects with the Warwick Statistics Prize and the Institute of Mathematics and its Applications (IMA) Prize.

During her bachelors, Kasia cultivated her passion for machine learning and statistics by coordinating and engaging in multiple research projects as the Research and Education Lead of Warwick Data Science Society. In her second year of undergraduate, she received a research grant to study Graphical Models, leading to her first publication in a scientific journal. Kasia has also gained a comprehensive professional experience through several internships in data science and quantitative research roles.

Equipped with the blend of academic and practical experiences, Kasia is eager to contribute to the research endeavours at the van der Schaar Lab. She is especially keen to work on data- and reality-centric AI. Her research is dedicated to understanding the process of knowledge acquisition, belief formation, and human decision-making, ultimately contributing to the advancement of personalised education and healthcare. She aspires to develop AI models that empower individuals’ creativity, thus fostering innovation. Within her research pursuits, Kasia particularly enjoys working with Bayesian methods, imitation and representation learning.

Kasia’s studentship is sponsored by Eedi, where she collaborates with industry experts to enhance the effectiveness of studying and teaching mathematics among school-age children. This involves the development of novel machine learning models aimed at understanding students’ misconceptions and guiding their learning paths.

Luca Muscarnera

PhD Student – joined the lab in 2025

Luca joined the Van Der Schaar Lab in October 2025. Before that, he was in the first cohort of High Performance Computing Engineering graduates at Politecnico di Milano, and in Italy, obtaining a cum laude Master’s degree with a thesis on GPU-based stochastic optimization for non-convex problems. Professionally, he worked in Italy as a research scientist during his Master’s, studying a transformer-based architecture for the prediction of gene interaction, in one of the earliest Italian start-ups of this kind.

After his Master’s, he worked as a research fellow at the MOX Laboratory in Milan, where he conducted research on the application of methods from statistical mechanics to the analysis of neural networks and on the development of efficient numerical algorithms for loss landscape exploration, along with novel high-performance neural network pruning techniques and high-dimensional statistics.

Luca considers his area of interest “at the intersection between Probability, Optimization, and High Performance Computing”: he is passionate about theory, but he firmly believes that theoretical results should always be supported by practical intuition in order to make research truly impactful — especially in the field of Artificial Intelligence. Recently, he has shown interest in the mechanistic interpretability of neural networks and reasoning in large language models. Understanding large neural networks, he says, “will help us in understanding reality.”

Luca’s research is supported by funding from Boehringer Ingelheim.

Max Ruiz Luyten

PhD student – joined the lab in 2023

Max graduated from the Interdisciplinary Higher Education Centre at the Polytechnic University of Catalonia with a Bachelor’s degree in Mathematics and another in Physics Engineering. He was a graduate visiting student in AI at MIT, and his work resulted in a publication in Nature Communications that was highlighted in the Editors’ Highlights webpage of recent research for “Applied Physics and Mathematics,” which showcases the 50 best papers recently published in an area.

He then worked at Meta in Instagram’s recommender systems before joining the van der Schaar Lab. In his own words, he was drawn by the “impact-centered culture in the group and our common interest in seeking the breakthroughs that currently separate ML and unsolved societal problems, commonplace in healthcare.” He feels that in academic research, it is “unfortunately too easy to detach from the test of reality,” which he wants to avoid and was a critical factor in choosing the van der Schaar lab with its reality-centric agenda.

Max aims to tackle temporal control tasks effectively by integrating multiple domain data while handling different resolution scales and uncertainty. This would apply to help clinicians effectively provide personalised medicine from a holistic view of the patient’s history but would also support economic or environmental policies, traffic control, and many other critical tasks. He thus seeks to focus his research around the components needed to that end, from modelling and learning the system’s structure to control-like strategies such as RL.

Max’ work is funded by AstraZeneca.

Nicolás Astorga

PhD Student

Nicolás graduated from the University of Chile with a BSc in Electrical Engineering, Mechanical Engineering, and Computer Science as well as a MSc in Computer Science and Electrical Engineering from the same institution.

Previously, Nicolás specialised in variational and information-theoretic research, making notable contributions such as “Matching priors and conditional for clustering” (ECCV2020) during his Harvard University internship. Professionally, he deployed Machine Learning models at ALeRCE, primarily focusing on classifying time series and tabular data for astronomical discoveries. Now, fuelled by his passion, Nicolás joins the Van der Schaar Lab to advance ML in medical research.

As a PhD student, he intends to explore various aspects of uncertainty quantification in ML. He is impressed by how uncertainty naturally appears in many ML formulations, e.g., as exogenous noise in Structural Causal Models or as epistemic uncertainty in Active Learning methods. He is convinced that uncertainty quantification is fundamental for creating more general artificial intelligence methods that, at the same time, can be used in real-world applications.

When Nicolás is not doing research, he enjoys playing video games or playing guitar.

W.D. Armstrong Trust Fund and Cystic Fibrosis Fund fund Nicolás’s research.

Nicolas Huynh

PhD student – joined the lab in 2022

Nicolas joins us freshly graduated with a Diplôme d’Ingénieur from Ecole Polytechnique (France) and an MSc in Machine Learning from Ecole Normale Supérieure Paris-Saclay.

During his studies, Nicolas focussed on a wide array of ML topics, such as cost-aware Bayesian optimisation, reward learning, and combining natural language processing with graphs.

Equipped with experience in a broad range of topics and a cemented passion for his field of research, Nicolas is excited to join the van der Schaar lab. In his words, “the exceptional combination of brilliant minds making up the lab, the immense impact of the lab’s research, and its expositions” make the lab the ideal place to pursue a meaningful PhD.

For his PhD, Nicolas aspires to work on various topics such as representation learning, causality, and data-centric ML to further translate their huge potential into the challenging domain of healthcare.

When he is not working on his research, he enjoys watching and playing football with friends, be it on the pitch or with a controller in his hands.

Nicolas’ research is supported by funding from Illumina.

Paulius Rauba

PhD student – joined the lab in 2022

Paulius’ path to the van der Schaar Lab is a particularly interesting one.

Although he only recently graduated as Shirley Scholar with an MSc in Social Data Science from Oxford University, he comes with years of work experience at the intersection of academia, business, and international organisations.

Paulius has previously worked very practically as an AI expert for the European Commission and the National Education Agency in Lithuania, evaluating proposals on the implementation of AI and advising on how to best build robust AI systems.

However, he also gathered experience in teaching as visiting lecturer at ISM University, instructing on econometrics and statistical learning, and as a lecturer at private coding academies introducing newcomers to Python for data science and statistics.

While working as a data scientist in a large corporate bank, Paulius acquired further know-how by building end-to-end data science and machine learning pipelines, implementing causal inference models, and developing propensity models. Before that, he worked as a data analyst in a company offering big data predictive analytics solutions and as an analyst in a management consulting firm.

Paulius joins us for his Ph.D. hoping to contribute his statistical and deep learning knowledge and to help build a new generation of causal deep learning tools to transparently and quantifiably solve practical problems faced by clinicians.

During his free time, you might find Paulius scuba diving with manta rays, snowboarding, kitesurfing, jogging, playing tennis, or (occasionally) running from orangutans in the jungle.

Paulius’ research is supported by funding from Aviva.

Qiyao Wei

PhD student – joined the lab in 2023

Qiyao Wei graduated from University of Toronto with a BSc in Computer Engineering before joining the van der Schaar lab. Now, he is a PhD student in the Department of Applied Mathematics and Theoretical Physics.

He is excited about the huge potential machine learning carries. On the theoretical side, he believes improvements to machine learning methods should come from a principled and analytical angle. On the empirical side, he is also keen on using machine learning to transform healthcare and medicine in combination with clinical experts.

In particular, Qiyao works at the intersection of deep learning and dynamical systems, with the research of Neural Ordinary Differential Equations as a prime example. He also aims to apply machine learning to improve clinical treatment decisions in a healthcare setting.

Besides research, Qiyao enjoys playing tennis, swimming, and cycling.

Qiyao’s studentship is gracefully sponsored by the Cambridge Center for AI in Medicine and GSK. 

Tennison Liu

PhD student – joined the lab in 2021

Tennison is a Ph.D. student in the Cambridge Centre for AI in Medicine as well as the van der Schaar Lab.

Tennison graduated from the University of Sydney with a B.Eng in Electrical Engineering, receiving the University Medal (highest mark), and then continued to an M.Phil. in Machine Learning and Machine Intelligence at the University of Cambridge, where he first worked with Prof. van der Schaar and was awarded the John CB Chau Prize for highest M.Phil. mark.

Tennison has held research data scientist roles at Cochlear, IBM, and Macquarie Group in Australia, but felt drawn to the intellectual stimulation of machine learning research. In his own words, he opted to join the lab for its “great resources that are rarely found in other institutions, in terms of research collaborations, faculty, and brilliant students.”

Tennison currently expects his work with the van der Schaar lab and the Cambridge Centre for AI in Medicine to focus on areas such as synthetic data, discovery using machine learning, and deep self-supervision.

In his spare time, Tennison enjoys rowing with the St Edmund’s College Boat Club, playing basketball, and working out in the gym. He also enjoys photography and (in the right mood) will play the piano and trumpet.

Tennison’s research is supported by funding from AstraZeneca.

Thomas Pouplin

PhD student – joined the lab in 2024

Thomas embarks on his PhD journey at the van der Schaar Lab with a rich background in applied artificial intelligence and machine learning. He holds a Diplôme d’Ingénieur from CentraleSupélec (France) and recently completed an MPhil in Machine Learning and Machine Intelligence at the University of Cambridge.

Before joining the lab, Thomas gained practical experience as an Applied AI Scientist at Thales Digital Solution in Montreal, Canada. There, he developed innovative methods for image registration and trained generative models to bridge the gap between different modalities.

During his MPhil, Thomas explored a wide range of fields, from computer vision to natural language processing. He specialised in conformal prediction for time series during his thesis, working under the guidance of Professor Mihaela van der Schaar for the first time. His work reflects a deep engagement with cutting-edge time series models, quantile regression, and confidence interval forecasting.

In the lab, Thomas aims to leverage his diverse expertise to delve into various machine learning areas. He is enthusiastic about applying his knowledge in new and challenging domains, demonstrating a true “jack of all trades” spirit.

Away from research, Thomas is a fervent video game aficionado and a die-hard Formula 1 enthusiast, proudly owning a personal F1 simulator where he races virtual laps at home.  He is also a keen snowboarder who might just surprise you with an impromptu backflip!

Thomas’s research is supported by funding from AstraZeneca.

Research Student

Silas Ruhrberg Estévez

Research Student

Silas is currently studying for an MB BChir in Medicine at King’s College. He previously completed an MEng in Information and Computer Engineering and a BA in Preclinical Medicine, also at Cambridge. With a strong foundation in both medicine and engineering, Silas is particularly interested in the application of Artificial Intelligence to support and improve clinical practice. He has gained research experience in Germany, the UK, and the USA, working on projects at the intersection of healthcare and technology.

Post-Doctoral Researcher

Krzysztof Kacprzyk

Post-Doctoral researcher

Krzysztof completed his PhD at the van der Schaar Lab in 2026. Prior to this, he graduated with an M.Sc. in Mathematical Sciences at the University of Oxford, where he studied statistics, geometry and quantum computing. His dissertation was devoted to optimization algorithms for the generalized multi-armed bandit problem. Before moving to Oxford, he completed his B.Sc. in Mathematics at University College London.

In his second year of undergraduate study, Krzysztof was awarded funding from EPSRC to conduct research in mathematical modelling, during which he designed a biomechanical model for rice seedling emergence. Later, as an Oxford AI Society member, he investigated bias, fairness and privacy issues in computer vision algorithms and presented his findings at the ICLR workshop on Synthetic Data Generation.

Krzysztof is driven to explore ways for machine learning to aid scientific discovery, especially in pharmacology. He is particularly interested in combining data from clinical trials with data from electronic health records to suggest better treatments for patients.

In his leisure time, Krzysztof enjoys playing the piano, building robots, juggling and performing yo-yo tricks.

Krzysztof’s research is supported by funding from Roche.

Communications

Dr Marika Niihori

Technical Communications Manager – Joined the lab in 2025

Marika is our communications manager since joining in 2025. Marika is a trained physicist with a PhD in NanoPhotonics from the University of Cambridge.

Alongside her scientific background, she has extensive experience in science communication through content creation, outreach, and public engagement. She has also gained industry experience in biotech, further broadening her perspective on how research translates into real-world applications.

Marika works to share the group’s cutting-edge AI and machine learning research with both scientific and wider audiences, making complex ideas clear, engaging, and impactful.

Research Engineers

Evgeny Saveliev

Research engineer & part-time PhD student – joined the lab in 2020

Evgeny is one of the lab’s research engineers, and has been a part-time PhD student since 2021. His educational background is Natural Sciences at the University of Cambridge, followed by postgraduate study in Computer Science at University of Southampton.

Evgeny was an AI Resident at Microsoft Research Cambridge before joining the lab, where he worked on projects covering meta-learning and reinforcement learning as applied to recommender systems. He also has experience in computational finance, having worked in a fintech start-up and commodities trading.

Evgeny facilitates turning the lab’s research code into robust production quality code, making it more scalable, applying software engineering best practices; he also collaborates with our PhD students on some research topics.

He is particularly interested in working on AutoML and time-series modelling, as well as machine learning for time series, and synthetic data.

Rob Davis

Rob Davis

Research engineer – joined in 2022

Rob is one of our research engineers since joining in 2022. His educational background is Physical Natural Sciences at the University of Cambridge.

Rob worked for five years at a medical software company before joining us. In his roles of Senior Data Scientist and Data Engineer, he focussed on data extraction from scientific literature, and it was here he became excited by applying Machine Learning methods in medical contexts.

Rob works to make research code robust, ensuring software engineering best practices are applied. He also creates user interfaces that demonstrate the research methods to make sure that their power can be understood by as many people as possible.

He has so far shown a great interest in the interpretability of Machine Learning methods.

Associates

Andrew Rashbass

Joined with us in 2023

Andrew Rashbass is the former CEO of The Economist Group, Reuters and Euromoney Institutional Investor PLC, and now works closely with Mihaela and the van der Schaar Lab on a  range of initiatives.

Tim Schubert

Joined with us in 2023

Tim Schubert studies medicine in the MD/PhD track at Heidelberg University.

He is fascinated by complex systems and efforts to understand them from an interdisciplinary perspective.
Within the realm of neurogenetics research at the Institute of Human Genetics, Tim focuses on unraveling the complexities of rare neurodevelopmental disorders. His work sheds light on how genetic changes impact human cognition and behaviour, with a particular focus on the prosocial peptide oxytocin.

Moreover, Tim is excited about harnessing the power of cutting-edge machine learning methods for solving medical challenges. He believes that finding common vocabulary is one of the keys towards bridging healthcare and AI. Tim works with the van der Schaar Lab on numerous initiatives, with a particular interest in quantitative epistemology, early diagnosis and synthetic data.

Outside the lab, Tim channels his creativity into producing an educational podcast for medical students. Besides his academic endeavours, he finds immense joy in cooking up elaborate dinners with friends, gliding across (and sometimes falling into) rivers on rowing boats, exploring new horizons through travel, and composing some musical hiccups on his piano.

Tim is supported by a scholarship of the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes).

Tim Oosterlinck

Joined with us in 2023

Tim Oosterlinck, nearing the completion of his Master’s in Medicine at KU Leuven, has always been intrigued by technology, a spark ignited during his childhood by watching his father build computers. He’s keen on melding the worlds of medicine and technology to find innovative solutions to address current and future healthcare challenges.

This curiosity led him to delve into AI-driven medicine, starting with a bachelor paper on machine learning for predicting hepatic arterial thrombosis post-liver transplantation. His admittance into the Honours program at KU Leuven further propelled his research endeavours.
At KU Leuven, Tim’s current projects revolve around the integration of Augmented Reality in surgical procedures, with an emphasis on perioperative navigation, 3D printing, and AR software development. He’s also a member of the Surgical AI Research Team at Orsi Academy, an institution specializing in robotic surgery training. There, he’s working on advanced computer vision methodologies to refine the surgical operations, and elevating surgeon training by providing objective metrics to evaluate their proficiency.

At the van der Schaar Lab, Tim is currently focused on ML-powered risk prediction models, working with Large Language Models and challenging existing healthcare paradigms.

Outside academia, he enjoys spending time with friends, long-distance running, surfing, snowboarding, and skiing.

Alumni

Alan Jeffares

Alan graduated from our lab with a PhD in 2025. He now works at Pretraining @ Mistral.

Alan Jeffares graduated from University College Dublin with a BSc in Statistics and from University College London with an MSc in Machine Learning. He also spent two years in industry working in applied machine learning.

During his time in industry, Alan helped develop technologies that enabled same day postal delivery for some of the world’s biggest postal services. He and his team presented their work at the World AI Summit in Amsterdam and the Conference of Applied Statisticians Ireland (CASI).

Alan decided to pursue a career in research upon completing his master’s thesis in UCL, which he found to be a rewarding experience. His work during this thesis also resulted in a paper which was published as a spotlight at ICLR 2022.

As a Ph.D. student with the van der Schaar Lab, Alan is delighted to have the opportunity to conduct machine learning research which can contribute towards better healthcare outcomes. As he explains, “I am particularly excited by having the academic freedom to dive into new and exciting areas of machine learning research and further developing these methods towards healthcare applications.”

Alicia Curth

Alicia graduated from our lab with a PhD in 2024. Alicia is now a Senior Researcher in Machine Learning at Microsoft Research Cambridge

Alicia Curth, a self-described “full-blooded applied statistician,” recently completed an MSc in Statistical Science at the University of Oxford, where she graduated with distinction and was awarded the Gutiérrez Toscano Prize (awarded to the best-performing MSc candidates in Statistical Science each year). Her previous professional experience includes a data science role for Media Analytics, and a research internship at Pacmed, a healthcare tech start-up.

Alicia also holds a BSc in Econometrics and Operations Research and a BSc in Economics and Business Economics from the Erasmus University Rotterdam.

Since meeting Mihaela van der Schaar at Oxford, Alicia says she’s “been fascinated by the diverse, creative and bleeding edge work of everyone in the lab ever since.”

Alicia hopes to explore ways of making machine learning ready for use in applied statistics, where problems are inferential rather than purely predictive in nature and the ability to give theoretical guarantees is essential. As she sees it, “there is much to gain by replacing linear regression with more flexible machine learning models.” She is particularly excited by potential applications in the areas of personalized and precision medicine, where she hopes machine learning can help healthcare “consider more than just the average patient in the future.”

Alicia is interested in building a better understanding of which algorithms work when and why, and aims to contribute to bridging the gap between theory and practice in machine learning. She is particularly interested in building decision support systems for doctors, and aiding knowledge discovery through next-generation clinical trials as well as analyses of genomics (and other omics) data.

Alicia has played waterpolo since the age of 12, and was German champion during high school. At Oxford, she represented the university as part of the women’s Blues team.

Alicia’s studentship was funded by AstraZeneca.

Alex Chan

Alex graduated from our lab with a PhD in 2024. He works now as Research Scientist at Convergence.

Alex Chan graduated with a BSc in Statistics at University College London before moving to Cambridge for an MPhil in Machine Learning and Machine Intelligence.

Having started early in research, he won an EPSRC funding grant in his second year of undergraduate for a project on Markov chain Monte Carlo mixing times, and has now had work published at all three of the major machine learning conferences: ICML, ICLR, and NeurIPS.

Much of Alex’s research focuses on understanding and building latent representations of human behaviour, with a specific emphasis on understanding clinical decision-making (an important new area of focus for the lab’s research) through imitation, representation learning, and generative modelling. In Alex’s own words, replicating and understanding decision-making at a higher level is, in itself, incredibly interesting, but “also being able to apply it healthcare is hugely important, and promises to actually make a difference to people’s lives in the near future.”

He is particularly interested in developing approximate Bayesian methods to appropriately handle the associated uncertainty that naturally arises in this setting and which is vital to understand.

Drawn to the lab’s special focus on healthcare, Alex notes that “No other area promises the same kind of potential for really having an impact with your research, and the lab benefits from the wide diversity of work being done alongside connections everywhere in both academia and industry.”

Outside of machine learning, Alex captained the Wolfson College Boat Club and occasionally keeps up with Krav Maga as a trainee instructor.

Alex’s studentship was sponsored by Microsoft Research.

Alihan Hüyük

Alex graduated from our lab with a PhD in 2024. He works now as Postdoc at Harvard University.

Alihan was a PhD student in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge.

Prior to attending Cambridge, he completed a BSc in Electrical and Electronics Engineering at Bilkent University. Alihan’s current research focuses on developing interpretable machine learning methods with the purpose of understanding the decision-making process of clinicians.

Previously, he worked on multi-armed bandit problems in combinatorial and multi-objective settings.

Boris van Breugel

Boris graduated from our lab with a PhD in 2024. He works now as Senior Researcher at Qualcomm in Amsterdam.

Before his PhD, Boris completed a MSc in Machine Learning at University College London, for which he received a Young Talent Award by Prins Bernhard Cultuurfonds and a VSBfonds scholarship. Prior to this, he completed a MASt in Applied Mathematics at the University of Cambridge and BSc degrees in Applied Physics and Applied Mathematics at Delft University of Technology.

While studying for his MSc in Machine Learning at UCL, Boris developed a model to detect Alzheimer’s disease using MRI and PET scans, enabling diagnosis at an earlier stage and thereby aiding the development of more effective treatment plans. He found the healthcare domain uniquely challenging and rewarding, and decided to continue research in the domain.

As a PhD student with the van der Schaar Lab, he worked on the intersection of synthetic data and trustworthy AI. He says, “Synthetic data promises a future where data is more widely available and where data is tailored to individual needs. At the same time, there are many challenges for creating this data, and ensuring downstream results are trustworthy.”

Previous work has focussed on privacy, fairness, distributional shifts, uncertainty, and evaluation of synthetic data, which he has presented at major ML conferences (NeurIPS, ICML, AISTATS).

Boris’ studentship was funded by the Office of Naval Research (ONR).

Hao Sun

Hao graduated from our lab with a PhD in 2025. He now works as a Research Scientist at Google DeepMind.


Hao received his B.Sc. in Physics from Peking University and M.Phil. from the Chinese University of Hong Kong’s Multimedia Lab (MMLab).

He works on reinforcement learning, inverse reinforcement learning (reward modeling), and large language model post-training during his PhD.

His PhD thesis is titled “Inverse Reinforcement Learning in the Era of Large Language Models.


Hao’s research was supported by funding from the Office of Naval Research (ONR).

Jeroen Berrevoets

Jeroen graduated from our lab with a PhD in 2024. He is now a Machine Learning Research Scientist at Ataraxis AI.

Jeroen Berrevoets joined the van der Schaar Lab from the Vrije Universiteit Brussel (VUB). Prior to this, he analyzed traffic data at 4 of Belgium’s largest media outlets and performed structural dynamics analysis at BMW Group in Munich.

As a PhD student in the van der Schaar Lab, Jeroen explored the potential of machine learning in aiding medical discovery, rather than simply applying it to non-obvious predictions. His main research interests involved using machine learning and causal inference to gain understanding of various diseases and medications.

Much of this draws from his firmly-held belief that, “while learning to predict, machine learning models captivate some of the underlying dynamics and structure of the problem. Exposing this structure in fields such as medicine, could prove groundbreaking for disease understanding, and consequentially drug discovery.”

Jeroen’s studentship was supported under the W. D. Armstrong Trust Fund. He was supervised jointly by Mihaela van der Schaar and Eoin McKinney

Jonathan Crabbé

Jonathan graduated from our lab with a PhD in 2024. He works now as Technical Staff at Stealth.

Jonathan Crabbé’s academic passions range from black boxes to black holes. He joins the lab following a MASt in in theoretical physics and applied mathematics at Cambridge, which he passed with distinction, receiving the Wolfson College Jennings Price. Before this, he received an M.Sc. from Ecole Normale Superieure of Paris’ Department of Physics, with his studies fully funded under the LABEX-ICFP Scholarship (awarded based on academic excellence).

Jonathan’s PhD work focused on the development of explainable artificial intelligence (XAI), which he believes to be one of the biggest challenges in machine learning. Through his research over the past years, he helped to deploy state-of-the-art machine learning models, meeting the expectations of (non-expert) users by supplementing the predictions made by models with informative and actionable explanations.

Jonathan describes explainability as “crucial in numerous domains of application, such as healthcare, where life-impacting decisions might be taken based on machine learning models.” He points out that “the impact of XAI goes well beyond state-of-the-art methods, as progress in machine learning will need to be based on a better understanding of model’s architecture.”

In his time off, Jonathan enjoys hiking, swimming, diving and crafting cocktails for his friends.

Jonathan’s studentship was supported by funding from Aviva.

Nabeel Seedat

Nabeel graduated from our lab with a PhD in 2024. He now works at Thomson Reuters.

Before joining the van der Schaar Lab, Nabeel received a merit scholarship for a master’s degree at Cornell University, researching Bayesian deep learning and uncertainty estimation for high stakes applications. In addition, he holds a master’s degree from the University of the Witwatersrand (South Africa), where he was awarded a National Research Foundation grant for his work applying signal processing and machine learning to Parkinson’s disease diagnostics in low-resource settings.

Professionally, Nabeel has worked as a machine learning engineer in the United States and South Africa. The computer vision and natural language processing models he worked on are currently deployed and serving millions of customers on a daily basis.

Nabeel is keenly aware that taking methods from the lab to the bedside “requires a unique focus beyond just high-performance predictive models; it requires the development of a toolkit of methods for transfer learning across domains and locations, learning on smaller datasets, understanding model biases and quantifying model reliability and uncertainty are fundamentally needed to bridge this divide.”

Nabeel’s research is supported by funding from the Cystic Fibrosis Trust.

Sam Holt

Sam graduated from our lab with a PhD in 2025. He now works at Google DeepMind.

Sam holds a M.Eng. in Engineering Science from the University of Oxford, where he graduated with a first-class degree and numerous awards for academic excellence (placing 2nd in the year for his master’s thesis).

In the course of his studies, Sam undertook two machine learning research internships at the University of Oxford: one researching detecting and tracking cars in noisy radar data (for a self-driving car), and the other in economic time-series forecasting. He also initiated his own original research into noise reduction on propellers, developing a propeller design that emits 44% less noise.

Upon graduating, Sam worked for an Oxford spin out, Mind Foundry, investigating dialogue systems for an industrial client. He has also authored and taught an online machine learning course covering recent work, and shared his passion for machine learning through teaching students in a classroom at a London-based tech-MBA program. Previously, he has collaborated and led three quantitative financial research projects, alongside working in data science and software engineering for a quantitative finance startup, Fifth Row Technologies. He has also created proof-of-concept automation ML tools to help doctors in GP practices.

Sam’s research is supported by funding from AstraZeneca.

Yuchao Qin

Yuchao graduated from our lab with a PhD in 2024. He now works at Tencent.

Yuchao Qin joined the van der Schaar Lab from Tsinghua University, where he received an BS in Automation and MS in Control Science and Engineering.

In 2019, Yuchao won 1st prize in the Oral Presentation category of the Beijing University Artificial Intelligence Academic Forum. He was awarded a highly competitive national scholarship in same year, while pursuing his master’s degree.

Yuchao’s prior research primarily focused on control and optimization methods for smart power systems with joint utilization of control theory and machine learning techniques. He has published a number of papers at leading conferences and in journals in intelligent power and energy systems.

His recent research interests are reinforcement learning, and inverse reinforcement learning. He explains that during the course of his research at Tsinghua he learned that “there’s a strong connection between optimal control theory and reinforcement learning,” and that “reinforcement learning is definitely one of the most promising methods to achieve higher level artificial intelligence as it allows machine to learn itself via interacting with the environment.” He believes that these techniques will ultimately contribute to the intelligence revolution in healthcare.

Yuchao describes the van der Schaar Lab as “one of the world’s leading labs in the field of machine learning, including reinforcement learning,” and hopes to use his studentship to “further explore the possibility of reinforcement learning methods, and their applications in understanding decision-making strategies of clinicians and other healthcare professionals.”

Yuchao’s studentship was funded by the U.K. Cystic Fibrosis Trust.

Fergus Imrie

Fergus was a postdoc at our lab until 2024. He is now the Florence Nightingale Bicentenary Fellow at the University of Oxford in the Department of Statistics.

Fergus Imrie was a postdoc at the ECE Department, University of California, Los Angeles (UCLA).
He is excited and motivated by the promise of transforming healthcare and improving medical knowledge through the use of machine learning in combination with clinical experts.

In particular, Fergus is interested in self-supervised learning and methods for understanding clinical decision making.

Prior to joining the lab, Fergus completed his DPhil (PhD) at the University of Oxford in the Department of Statistics, developing deep learning approaches for drug discovery. Fergus values work with a strong translational impact: his research is currently being used by a number of pharmaceutical companies on active drug discovery projects to develop new therapeutics.

Zhaozhi Qian

Zhaozhi was a postdoc at our lab until 2024 after graduating as a PhD student with us. He is now a Senior Research Scientist at Elm.

Zhaozhi Qian was a postdoc at the Cambridge Centre for AI in Medicine, the University of Cambridge.

When he is not grinding code with Copilot or prompting GPT, he enjoys developing new methodologies in generative models and causal inference for the next generation of AI. He is also committed to building open-source software that democratises cutting-edge research and makes AI really Open. 

During his PhD at van der Schaar Lab, Zhaozhi has developed a host of novel algorithms for treatment effect estimation, time series forecasting, and synthetic data generation to address the pressing challenges in healthcare and medicine. 

Prior to joining academia, Zhaozhi worked as a data scientist in one of the largest mobile games companies in the world, designing and implementing AI-powered systems that automatically optimise performance marketing campaigns. He also proudly worked for the NHS as a volunteer during the pandemic, contributing to the UK’s first ICU capacity planning and forecasting system.

Dan Jarrett

Dan graduated from our lab with a PhD in 2023. He is now a Research Scientist at Google DeepMind.

Dan graduated from Princeton University with a B.A. in economics, and from Oxford with an MSc. in computer science.

He has professional experience in finance, consulting, and technology spaces, and research interests include representation learning and decision-making over time.
His PhD thesis was titled “Advances in Reinforcement Learning for Decision Support”. In July 2023, Dan has successfully passed his PhD viva.

Ioana Bica

Ioana graduated from our lab with a PhD in 2022. She is now a Research Scientist at Google DeepMind.

Ioana Bica joined the lab as PhD student at the University of Oxford and at the Alan Turing Institute in 2018. She had previously completed a BA and MPhil in Computer Science at the University of Cambridge where she specialised in machine learning and its applications to biomedicine.

Ioana’s PhD research focused on building machine learning methods for causal inference and individualised treatment effect estimation from observational data. In particular, she has developed methods capable of estimating the heterogeneous effects of time-dependent treatments, thus enabling us to determine when to give treatments to patients and how to select among multiple treatments over time. Her PhD thesis was titled “Causal Inference Methods for Supporting, Understanding, and Improving Decision-Making.” In July 2022, Ioana has successfully passed her PhD viva.

Yao Zhang

Yao graduated from our lab with a PhD in 2022. He was previously a Postdoctoral Researcher at Stanford University, and now is a Assistant Professor in Statistics and Data Science @ National University of Singapore.

Yao Zhang joined the lab as PhD student in 2019. His PhD research has focused on conditional causal inference, answering questions about personalised treatment effects, and about handling complex experimental designs.

All of this work has culminated in Yao’s Ph.D. thesis, entitled “Topics in conditional causal inference.” Yao was awarded his doctorate by the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge.

Prior to this, he studied Mathematics, Statistics and Machine Learning (BA and MPhil Hons.) at the University of Cambridge and the University of Birmingham.

Ahmed Alaa

Ahmed was a postdoc at our lab until 2021. after graduating as a PhD student with us. He is now one of the inaugural Assistant Professor in the new computational precision health program at UC Berkeley and UCSF.

Ahmed joined the van der Schaar Lab as a Ph.D. student in 2015 at the University of California, Los Angeles, and completed his doctoral research (supervised by Mihaela van der Schaar) in December 2019. His dissertation, entitled “Discovering Data-Driven Actionable Intelligence for Clinical Decision Support,” is available here.

Subsequently, Ahmed remained with the lab as a postdoctoral scholar at UCLA and an affiliated postdoctoral researcher at the University of Cambridge (COVID-19 task force). He received the 2021 Edward K. Rice Outstanding Doctoral Student Award from UCLA.

Ahmed then proceeded as a Postdoctoral Associate at the Broad Institute of MIT and Harvard, and the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL).

Alexis Bellot

Alexis graduated from our lab with a PhD in 2021. He is now a Research Scientist at DeepMind.

Alexis joined the lab as a Ph.D. student in 2017, under the supervision of Mihaela van der Schaar and affiliated with the University of Cambridge and The Alan Turing Institute.

Alexis’ research consistently focused on causal inference, hypothesis testing, and its applications, most notably in healthcare. He awarded the G-research PhD competition prize in 2019.

In June 2021, Alexis was awarded his doctorate by the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge following a successful defense of his thesis, entitled “Hypothesis testing and causal inference with heterogeneous medical data.”

Alexis then moved on to a postdoctoral research scientist at Columbia University, under the direction of Prof. Elias Bareinboim in the Computer Science Department. His research continued to focus on causal inference, hypothesis testing, and its applications.

Changhee Lee

Changhee graduated from our lab with a PhD in 2021. He is now a Assistant Professor at Korea University. Previously he was a an Assistant professor in Chung-Ang University’s School of Software and Computer Engineering (Department of Artificial Intelligence).

Changhee joined the lab as a Ph.D. student in 2016 (supervised by Mihaela van der Schaar) at the University of California, Los Angeles.

His research has focused on deep learning approaches for addressing challenges associated with modeling, predicting, and interpreting in time-to-event analysis and time-series analysis.

Changhee’s thesis, entitled “Machine Learning Frameworks for Data-Driven Personalized Clinical Decision Support and the Clinical Impact,” is available here.

James Jordon

James graduated from our lab with a PhD in 2021. He is now at The Alan Turing Institute, where he is pursuing a postdoc on synthetic data.

James joined the lab as a Ph.D. student in 2017 under Mihaela van der Schaar’s supervision at the University of Oxford.

Much of his research with the lab focused on the use of generative adversarial networks in solving supervised, unsupervised and private learning problems including estimation of individualised treatment effects, feature selection, private synthetic data generation, data imputation, and transfer learning.

James graduated on the basis of an integrated thesis comprised of multiple papers: GANITE, SCIGAN, GAIN, KnockoffGAN, PATEGAN, and DPBag.

Trent Kyono

Trent graduated from our lab with a PhD in 2021. He is now a Machine Learning Leader @Indeed.

Motivated by the desire to build cutting-edge machine learning models that can transform healthcare, Trent first joined the lab at the University of California, Los Angeles, under the supervision of Mihaela van der Schaar. He pursued a research agenda at the confluence of machine learning, computer vision, and causality.

Trent is a senior machine learning researcher within the Ads Ranking Team at Meta (Facebook) where he works on machine learning methods for ad personalisation.

Trent’s dissertation, entitled “Towards Causally-Aware Machine Learning” focuses on leveraging cause and effect relationships for improving several aspects of machine learning, such as regularization, missing data, synthetic data, and domain adaptation.

Jinsung Yoon

Jinsung graduated from our lab with a PhD in 2020. He is now a researcher at Google Cloud AI.

Jinsung is a senior research scientist at Google Cloud AI. He is currently working on diverse machine learning research topics such as generative models, self- and semi-supervised learning, model interpretation, data imputation, anomaly detection, and synthetic data generation.

Previously, he worked on machine learning for medicine with Professor Mihaela van der Schaar as a graduate student researcher in UCLA Electrical and Computer Engineering Department. He received his PhD and MSc in Electrical and Computer Engineering Department at UCLA, and his BSc in Electrical and Computer Engineering at Seoul National University (SNU).

In 2021, he was selected as the innovator under 35 in South Korea from MIT Technology Review.

Alumni (graduated pre-2020)

Post-docs
  • William Whoiles
    • Now Post-doc at the Department of Electrical and Electronics Engineering, University of British Columbia, Canada
  • Cem Tekin
    • Now Assistant Professor at the Department of Electrical and Electronics Engineering, Bilkent University, Turkey
  • Luca Canzian
    • Now Post-doc at the Computer Science Department, University of Birmingham, UK
PhD
Affiliated PhD Alumni
  • Cong Shen
    • Now Assistant Professor at University of Science and Technology China (USTC), China
  • Omar A. Nasr
    • Now Assistant Professor at Cairo University, Egypt
MSc
Past visiting students
  • Minhae Kwon (2016) (Email)
  • Sabrina Muller (2015) (Email)
  • Suoheng Li (2014-2015) (Email)
  • Zhenlong Yuan (2014-2015) (Email)
  • Meier Yannick (2015) (Email)
  • Jonas Braun (2014) (Email)
  • SaiDhiraj Amuru (2014) (Email)
  • Mahnoosh Alizadeh (2013) (Email)
  • Cuiling Lan (2013) (Email)
  • Byung-Gook Kim (2012) (Email)
  • Luca Canzian (2012) (Email)
  • Saeede Parsaee Fard (2011) (Email)
  • Oussama Habachi (2011) (Email)
  • Ulrich Berthold (2008)(Email)
  • Elodie Heslouis (2005)
  • Aymeric Larcher (2004)

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