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 prolific and diverse teams in its field, employing 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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Hyun-Suk Lee

Postdoc – joined the lab in 2019

Hyun-Suk Lee is a post-doc researcher of the van der Schaar lab. His current research interests are intelligent decision-making methods with a focus on clinical trial designs and healthcare.

Prior to joining the van der Schaar lab, Hyun-Suk primarily worked on decision-making methods in communication networks. So, his research interests span various interdisciplinary areas in machine learning, communication networks, and medicine.

He has published various papers in machine learning conferences and flagship journals in communication networks.

Ph.D. students

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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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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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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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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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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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