Alicia Curth
Alicia graduated from our lab with a PhD in 2024Alicia 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).
Jeroen Berrevoets
Jeroen graduated from our lab with a PhD in 2024Jeroen 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.
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 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 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 is now a Postdoctoral Researcher at Stanford University.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 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
Alexis graduated from our lab with a PhD in 2021.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.
