van der Schaar Lab

van der Schaar Lab congratulates 3 researchers on graduation

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 is pleased to announce the graduation of 3 of its researchers: postdoc Ahmed Alaa and Ph.D. students Changhee Lee and James Jordon.

Individually and collectively, Ahmed, Changhee and James have contributed immeasurably to the growth of the lab and the development of numerous impactful and cutting-edge models and methods, as well as the lab’s vision for revolutionizing healthcare through AI and machine learning.

Ahmed M. Alaa

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). His primary research focus has been on individualized treatment effect inference, automated machine learning (AutoML)uncertainty quantification and time-series analysis.

Earlier this year, Ahmed received the 2021 Edward K. Rice Outstanding Doctoral Student Award from UCLA. The award is administered by the UCLA Samueli School of Engineering on an annual basis, and honors the achievements of a single alumnus (chosen from among all of the School’s departments) who has demonstrated academic and research excellence, leadership, and service to the school, university or community.

Ahmed is now a Postdoctoral Associate at the Broad Institute of MIT and Harvard, and the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL).

Changhee Lee

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.

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

Changhee’s thesis, entitled “Machine Learning Frameworks for Data-Driven Personalized Clinical Decision Support and the Clinical Impact,” is available here.

Changhee’s next role is an assistant professorship in Chung-Ang University’s School of Software and Computer Engineering (Department of Artificial Intelligence).

James Jordon

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 has 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.

Of particular interest to James has been 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.

James graduated on the basis of an integrated thesis comprised of multiple papers: GANITE, SCIGAN, GAIN, KnockoffGAN, PATEGAN, and DPBag.

James is now at The Alan Turing Institute, where he is pursuing a postdoc on synthetic data.

It has been such an enormous pleasure to work with Ahmed, Changhee, and James. They were here during a formative period for our lab, and their brilliance has laid much of the foundation for our success. Of course, I am sad to see them go after so many years together, but I am also very excited to watch them continue to explore new frontiers.

– Mihaela van der Schaar

The van der Schaar Lab continues to attract the strongest talent from a broad range of disciplines: having already added 6 Ph.D. students in 2020 and a postdoc in spring this year, last week the lab announced that 5 new Ph.D. students have joined its research team. Recruitment for a handful of Ph.D. studentships starting in 2022 has now begun; prospective applicants should click here to learn more.

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.

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.