van der Schaar Lab

van der Schaar Lab welcomes 5 new researchers in 2021

We are now accepting applications for a handful of fully-funded PhD studentships (autumn 2022 start). Find out more here.

The van der Schaar Lab will add 5 new Ph.D. students to its team of researchers this autumn, capping another year of highly impactful projects and unprecedented recognition at the major conferences in machine learning.

Heading into the 2022 academic year, Hao Sun, Kamile Stankeviciute, Krzysztof Kacprzyk, Nabeel Seedat, and Sam Holt 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 5 new researchers are introduced below.

Hao Sun

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

His M.Phil. research focused on sample-efficient deep reinforcement learning (DRL) algorithms for continuous control. He proposed several sample-efficient algorithms for sparse reward goal-conditioned tasks, general continuous control locomotion tasks, diversity-seeking exploration and safe reinforcement learning tasks.

Hao’s previous work on self-supervised sample-efficient reinforcement learning algorithms has already been featured in spotlight presentation at NeurIPS 2019. As Hao points out, “the area of supervised-learning based RL is thriving nowadays, and is definitely a promising under-explored direction worthy of further investigation!”

Although DRL has achieved great success in computer games and similar applications, deploying prevailing DRL algorithms in real-world applications is still an open question—one that Hao hopes to explore as a member of the van der Schaar Lab, given the lab’s particular focus on healthcare. In his own words, Hao believes that “we should be able to take DRL beyond simply working in simulations; the lab is in a unique position to apply it in real-world scenarios where we can help people making better decisions.”

Hao believes that healthcare provides a natural scenario where high-quality decision-making policy is needed, but that there is still a pressing need to address the interpretability, transparency, and reliability issues of modern reinforcement learning and inverse reinforcement learning algorithms.

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

Kamile Stankeviciute

Before joining the van der Schaar lab, Kamile did her undergraduate studies in Computer Science at the University of Cambridge, followed by an MSc in the same field at the University of Oxford, where she explored the applications of machine learning to computational neuroscience and time series uncertainty estimation problems.

As a three-time software engineering intern at Google, Kamile has additional experience in large scale system capacity planning, search ranking, statistical experimentation and machine learning frameworks. Kamile’s impact on diversity, demonstrated leadership, and academic excellence have been recently recognized by the Generation Google Scholarship 2021 award, and she has the rare distinction of having a paper published at NeurIPS before starting her PhD!

On her original decision to pursue machine learning for healthcare, Kamile says: “Many years ago I was considering a degree in medicine, but I also believed that artificial intelligence and machine learning will be key in revolutionizing the medical practice. A PhD at the intersection of the two fields in a world-leading lab therefore seemed like an obvious choice.”

As part of the van der Schaar lab, Kamile aspires to use machine learning to meaningfully integrate structured medical domain expert knowledge with the clinical observation data. Her goal is to reduce the gap between machine learning and clinical practice by enhancing the existing models and making new medical discoveries.

Kamile’s research is supported by funding from AstraZeneca.

Krzysztof Kacprzyk

Krzysztof 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 has represented UCL at the International Mathematics Competition twice, each time winning a bronze medal. He also earned the Most Innovative Hack award at two hackathons in London. His professional experience includes software engineering positions in both a medical technology start-up and the financial industry.

Krzysztof chose to join the lab because he saw potential to connect his love for pure mathematics with the desire to produce impactful research in AI for medicine. On this note, he says: “I realized that there is no better place than van der Schaar lab if I wanted to do impactful research while utilizing my mathematical background.”

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.

Nabeel Seedat

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.

Nabeel also holds bachelor’s degrees in Biomedical Engineering and Information Engineering from the University of the Witwatersrand, where he received the top undergraduate student award.

Professionally, Nabeel has worked as a machine learning engineer at two multinational multimedia and video streaming companies 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.

On his decision to join the van der Schaar lab, Nabeel says: “I was captivated by the lab’s focus on novel machine learning to solve real healthcare problems, coupled with the cross disciplinary collaborations necessary to turn these solutions into reality.” He is particularly passionate about delivering impact not just in high-resourced healthcare settings, but also low-resourced healthcare settings.

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 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. Most recently, he has 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 views a Ph.D. with the van der Schaar Lab as “the perfect way to develop research excellence and turn ideas into long-lasting, significant contributions” by solving real-world problems and finding new ways to apply machine learning methods to the healthcare setting. He is motivated by “the thrill of researching, discovering new knowledge and understanding new work (including its limitations) deeply, then posing new ideas to test to enhance our understanding.”

Sam’s research is supported by funding from AstraZeneca.

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 14 papers accepted for publication at NeurIPS 2021). This will include sharpening the focus of the lab’s ongoing projects around a few core areas, individualized treatment effects, interpretability and explainability, trustworthiness in ML, and understanding and empowering human decision-making.

I’m delighted to welcome Hao, Kamile, Krzysztof, Nabeel, and Sam to our lab’s team. Between them, these five exceptional researchers bring a wide range of academic backgrounds to the table—including machine learning, pure maths, and engineering. This diversity is at the heart of our lab’s ability to create cutting-edge machine learning models and methods that target real-world healthcare problems.

– 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 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 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.