van der Schaar Lab

Research team

Led by Professor Mihaela van der Schaar and based in Cambridge, U.K., the van der Schaar Lab’s research team is one of the most impactful and diverse teams in its field, developing a wide range of ML approaches including deep learning, causal inference, AutoML, time series analysis, ensemble learning, and many more.

The lab’s shared vision is to develop cutting-edge machine learning methods and techniques, with the goal of improving healthcare and medical knowledge.

Prof. van der Schaar

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Mihaela van der Schaar

John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela 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.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

Read Mihaela’s full bio.

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Postdocs

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Ahmed Alaa

Postdoc – joined the lab in 2015

Ahmed M. Alaa is a Postdoctoral Scholar at the ECE Department, University of California, Los Angeles (UCLA), and an affiliated Postdoctoral Researcher at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

His primary research focus has been on causal inference, automated machine learning, uncertainty quantification and time-series analysis.

He has published papers in several leading machine learning conferences including NeurIPS, ICML, ICLR and AISTATS.

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PhD students

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Alex Chan

Ph.D. student – joined the lab in 2020

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 earlier this year had his work on uncertainty calibration presented at ICML.

Much of Alex’s research will focus on understanding and building latent representations of human behavior, 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 modeling. 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.”

Alex’s studentship is sponsored by Microsoft Research.

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

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Alexis Bellot

Ph.D. student – joined the lab in 2017

Alexis’ main current endeavour is reverse engineering causal relationships from observed data.

He believes causal insights may provide robustness and extrapolation properties to predictive models which would make them safer and better behaved.

Anything related to medical data and problems also peak his interest; problems for which he has designed new hypothesis tests and survival models.

In his leisure time he enjoys performing kitchen alchemy for friends, and subsequently burning excesses with a friendly football game and routine gym sessions.

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Alicia Curth

Ph.D. student – joined the lab in 2020

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’s studentship is funded by AstraZeneca.

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.

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Alihan Hüyük

Ph.D. student – joined the lab in 2019

Alihan is a PhD student in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. He is supervised by Professor Mihaela van der Schaar.

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.

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Boris van Breugel

Ph.D. student – joined the lab in 2020

Boris van Breugel most recently completed a MSc in Machine Learning at University College London. His study was supported under a Young Talent Award by Prins Bernhard Cultuurfonds, and a VSBfonds scholarship. Prior to this, he received a MASt in Applied Mathematics from the University of Cambridge.

Reflecting his broad research background, Boris’ current research interests range from model interpretability to learning from missing data, from modelling treatment effects to high-dimensional omics data.

While studying for his MSc in Machine Learning at UCL, Boris developed a model to detect Alzheimer’s disease in different forms of medical imaging data, potentially 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, Boris aims to develop methods for finding meaningful structure in omics data—in essence, he says, “the amount of omics data is increasing at a huge speed, and machine learning methods can allow us to interpret and make sense of all this data.”

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

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Changhee Lee

Ph.D. student – joined the lab in 2016

Changhee Lee is a PhD Candidate at UCLA. His research focuses on deep learning approaches for addressing challenges associated with modeling, predicting, and interpreting in time-to-event analysis and time-series analysis.

His recent research interests lie at the intersection of deep learning and genomics with a focus on multi-view multi-task learning, feature selection and representation learning for high-dimensional genomic data.

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Dan Jarrett

Ph.D. student – joined the lab in 2019

Dan is a second-year Ph.D. student in the machine learning and artificial intelligence research group at the department of mathematics, advised by Professor van der Schaar.

He 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.

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Ioana Bica

Ph.D. student – joined the lab in 2018

Ioana Bica is a second year PhD student at the University of Oxford and at the Alan Turing Institute. She has previously completed a BA and MPhil in Computer Science at the University of Cambridge where she has specialised in machine learning and its applications to biomedicine.

Ioana’s PhD research focuses 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.

Recently, Ioana has started working on methods for understanding and modelling clinical decision making through causality, inverse reinforcement learning and imitation learning.

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James Jordon

Ph.D. student – joined the lab in 2017

James is a 3rd year DPhil student at the University of Oxford.

His research focuses 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.

Of particular interest is the use of generative modelling in creating private synthetic data to allow easier data sharing and therefore more rapid advancement in specialised machine learning technologies.

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Jeroen Berrevoets

Ph.D. student – joined the lab in 2020

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 plans to explore the potential of machine learning in aiding medical discovery, rather than simply applying it to non-obvious predictions. His main research interests involve 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 is supported under the W. D. Armstrong Trust Fund. He will be supervised jointly by Mihaela van der Schaar and Dr. Eoin McKinney.

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Jonathan Crabbé

Ph.D. student – joined the lab in 2020

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 work focuses 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 coming years, he hopes to help deploy of state-of-the-art machine learning models, meeting the expectations of (possibly 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.”

Jonathan’s studentship is supported by funding from Aviva.

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

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Trent Kyono

Ph.D. student – joined the lab in 2018

Trent is a senior machine learning engineer and researcher with The Boeing Company. He leads a small machine learning research group centered around Space Domain Awareness SDA). Additionally, he works on computer vision problems for military aircraft, autonomous flight, and general machine learning robustness for autonomous aircraft.

He joined Professor van der Schaar’s lab to participate in cutting-edge research that has a positive impact in the healthcare industry.

His interests and research lies at the confluence of machine learning, computer vision, and causality.

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Yao Zhang

Ph.D. student – joined the lab in 2019

Yao Zhang is a second-year PhD student in the van der Schaar Lab.

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

His primary research interest is Causal Inference.

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Yuchao Qin

Ph.D. student – joined the lab in 2020

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 is funded by the U.K. Cystic Fibrosis Trust.

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Zhaozhi Qian

Ph.D. student – joined the lab in 2019

After obtaining a MSc in Machine Learning at UCL, Zhaozhi Qian started his career as a data scientist in the largest mobile gaming company in Europe. Three years later, he found it might be more fulfilling to apply AI to cure cancer than to make the gamers hit the purchase button 1% more often.

He thus joined the group in 2019 as a PhD student focusing on robust and interpretable learning for longitudinal data. So far, his work has included inferring latent disease interaction networks from Electronic Health Records, uncovering the causal structure between events that unfold over time, and calibrating the predictive uncertainty under domain shift.

Zhaozhi also worked as a contractor in the NHS during the COVID-19 pandemic contributing his analytical skills to the national response to the pandemic.

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Communications

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Nick Maxfield

Communications & project coordination – joined the lab in 2020

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.

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