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

Ming-Chieh’s research interest include causal inference in longitudinal data, and also treatment assignment and treatmet effect heterogeneity in healthcare data.

Mercy’s research interest include developing natural language processing models to interpret clinic visit notes, using generative adversarial networks to enhance medical ultrasound imaging and machine learning models for clinical risk assessment. She is particularly interested in using these tools to bridge disparities in healthcare.

Irene works on machine learning methods for equitable healthcare. Her research focuses on two main areas, 1) developing machine learning methods for equitable clinical care, and 2) auditing and addressing algorithmic bias.

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 characterizing variation in how patients are treated, evaluating the effect of treatment policies, and handling dataset shift in predictive 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.

Yuria’s research focuses on building models with local interpretability so that practitioners can understand why a model assigns a patient a particular risk status.

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

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.

Stefan’s research interests include interpretable machine learning and natural language processing applications in the healthcare setting.