Machine learning for precision medicine
Speaker: Ruishun Liu, Postdoctoral Researcher, Stanford University
Abstract: Predicting the impact of interventions in the real world from observational data alone represents a major statistical challenge. Indeed, treatment assignments are usually correlated with the predictors of the response, resulting in a lack of data support for counterfactual predictions and therefore in poor quality estimates. Developments in causal inference have lead to methods addressing this confounding by requiring a minimum level of overlap. However, overlap is difficult to assess and usually not satisfied in practice. In this work, we propose to circumvent the overlap assumption by predicting the impact of treatments continuously over time using neural ordinary differential equations equipped with uncertainty estimates.
Speaker Bio: Ruishan Liu is a postdoctoral researcher in Biomedical Data Science at Stanford University, working with Prof. James Zou. She received her PhD in Electrical Engineering at Stanford University in 2022. Her research lies in the intersection of machine learning and applications in human diseases, health and genomics. She was the recipient of Stanford Graduate Fellowship, and was selected as the Rising Star in Data Science by University of Chicago, and the Rising Star in Engineering in Health by Johns Hopkins University and Columbia University. She led the project Trial Pathfinder, which was selected as Top Ten Clinical Research Achievement in 2022 and Finalist for Global Pharma Award in 2021.
Monday, January 30, 2023
3:30pm – Pre-talk meet and greet teatime – Dana House, 24 Hillhouse Avenue
4:00 pm – 5:00pm – Talk – Mason Lab 211, 9 Hillhouse Avenue
In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation.