Doctoral Thesis Title: Learning and Designing Interventions

Presenter: Jiaqi Zhang

Presenter’s Affiliation: LIDS, MIT; Schmidt Center, Broad Institute

Thesis Supervisor: Caroline Uhler

Thesis Committees: Munther A. Dahleh, Nir Hacohen (Harvard Medical School)

Abstract: 

Complex causal mechanisms among genes govern cellular functions in health and disease. Understanding these mechanisms can accelerate therapeutic discovery but remains challenging due to the large number of genes and their intricate dependencies. Recent advances in experimental technologies are making this problem increasingly tractable: it is now possible to systematically intervene on individual genes or gene combinations in single cells and measure their downstream effects, enabling empirical identification and validation of causal relationships. However, interventional data are high-dimensional, making interpretation challenging, and costly to collect.

In this thesis, I present our work tackling these challenges in three parts. In the first part, we develop efficient algorithms for causal discovery when the causal variables are observed, addressing the bottleneck of using too many conditional independence tests in existing algorithms. In the second part, we introduce theories and methods for learning causal representations when the causal variables are not directly observed. We also demonstrate how to predict the effects of novel interventions using these representations. Finally, in the third part, we propose frameworks for optimizing intervention designs and showcase that these frameworks can be useful for and applied to biomedical problems in disease-relevant contexts. 

Details

• Date: Thursday, April 30
• Time: 3:30 pm - 5:00 pm
• Location: 75 Ames St, Floor M1, Joshua Tree (If you are not affiliated with the Broad Institute, you will need a government ID to sign in as a visitor. Please contact viczhang@mit.edu when you arrive.)