My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

Irene works on machine learning methods to improve understanding of human health and reduce inequality.

Michael’s research interests include developing learning algorithms for dealing with non-stationarity / dataset shift in predictive modelling, as well as robust learning of treatment policies from observational data.

Monica’s research interests include reasoning over longitudinal clinical notes, building more intelligent electronic health records, studying user-ML interactions in clinical settings, and developing algorithms that can incorporate domain knowledge.

Zeshan’s research interests include modelling time-series data, particularly clinical data, deep generative models, as well as chronic disease progression modelling.

Rebecca’s research interests include developing methods to learn disease subtypes and disease progression models for precision medicine applications. She is particularly interested in leveraging machine learning algorithms together with bioinformatics to better understand disease.

Christina is interested in characterizing variation in treatment policies, examining the theoretical assumptions behind off-policy evaluation of reinforcement learning for healthcare, and developing algorithms for disease progression modeling.

Chandler’s research interests include causal structure learning, active learning and experimental design, and combining causal inference with modern machine learning techniques. His application interests include robustness and explainability of machine learning for healthcare, and the use of AI for scientific discovery.

Hussein’s interests focus on human-centric aspects of machine learning, namely how to integrate expert decision makers into machine learning pipelines while ensuring fairness and an understanding of long-term consequences.

Hunter’s research focuses on understanding and improving the performance of machine learning algorithms in the wild, with particular applications in MAP inference for graphical models, stochastic optimization, and weak supervision.

Justin works on interpretable and robust algorithms to learn treatment policies and understand their variation in practice.

Jason is interested in investigating NLP models and their applications to clinical prediction tasks, and using them to develop robust systems that incorporate expert knowledge.