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

My research interests lie broadly in the area of artificial intelligence for social impact, particularly spanning the fields of multi-agent systems and data science for conservation and public health.

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 progression models and discover new biological insights for precision medicine applications. She works on machine learning algorithms that can utilize clinical and genomic data for this purpose, with a particular focus on single cell RNA-sequencing data and cancer.

Christina is interested in applying machine learning to healthcare, detecting distribution shift and developing transfer learning algorithms, and evaluating treatments and reinforcement learning policies with causal inference.

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.

My research lies at the intersection between NLP and HCI. I am interested in understanding languages in scientific, legal, or clinical text from documents that are authored and used by domain experts. With newly developed NLP approaches, I study how they can enable better Human-AI collaboration to assist experts in these high-stake settings.

Ilker is interested in developing principled methods that can use multimodal medical data to provide actionable insights towards mechanisms of diseases and to facilitate clinical decision-making.

Sharon’s research interests include clinical natural language processing, machine learning augmented electronic health records, and deployment of AI to enhance clinical decision making.

Sama is an undergraduate at MIT, with interests in software development and machine learning.

Alejandro’s interests include modeling over longitudinal health data and understanding human disease through representation learning on single-cell genomic and transcriptomic data. He is especially interested in leveraging techniques from natural language processing in these areas.