van der Schaar Lab congratulates 3 researchers on graduation

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.

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.