van der Schaar Lab

van der Schaar Lab welcomes 6 new researchers

We are now accepting applications for a handful of fully-funded PhD studentships (autumn 2021 start). Find out more here.

The van der Schaar Lab will add 6 new PhD students to its team of researchers this month, capping a year of highly impactful projects and unprecedented recognition at the major conferences in machine learning.

Heading into the 2021 academic year, Alex Chan, Alicia Curth, Boris van Breugel, Jeroen Berrevoets, Jonathan Crabbé, and Yuchao Qin will join the Cambridge-based lab. Each of them will bring a new perspective, fresh ideas, and an exceptional academic record to the lab’s ongoing development of world-leading techniques in the field of machine learning for healthcare. The lab’s 6 new researchers are introduced below.

Alex Chan

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.

Alicia Curth

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.

Boris van Breugel

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).

Jeroen Berrevoets

Jeroen Berrevoets will join 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.

Jonathan Crabbé

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 MSc 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.”

One of Jonathan’s papers—co-authored with Yao Zhang and Mihaela van der Schaar from the lab—has been accepted for presentation at NeurIPS 2020.

Jonathan’s studentship is supported by funding from Aviva.

Yuchao Qin

Yuchao Qin will join 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.

During the coming academic year, the van der Schaar Lab’s research team will aim to maintain and build on the momentum from recent achievements (most notably, having 7 papers accepted for presentation at ICML 2020 and 9 at NeurIPS 2020). This will include sharpening the focus of the lab’s ongoing projects around a few core areas, including AutoML, causal inference, interpretability and explainability, trustworthiness in ML, and understanding clinical decision-making.

I’m thrilled to be welcoming 6 extraordinarily talented PhD students to our lab this year. They will be working on a wide range of new and exciting topics at the cutting edge of machine learning, with the aim of transforming healthcare.

– Mihaela van der Schaar

To find out more about the van der Schaar Lab’s research team, click here. Our publications are available here.

To inquire about PhD studentships, visit this page.

Nick Maxfield

Nick Maxfield

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.

Mihaela van der Schaar

Mihaela van der Schaar

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.