4:00–5:00 pm
DSI 105 5460 S University Ave
Chicago IL 60615
Jiaqi Zhang
PhD Candidate
Massachusetts Institute of Technology
Title: Modeling Large-Scale Interventions
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 talk, I will present our work tackling these challenges from three aspects. First, we introduced causal representation theories and algorithms with identifiability guarantees to uncover latent variables behind high-dimensional data. Second, we developed a method to model interventional data that can predict the effects of novel interventions with high accuracy, incorporating both distributional shifts and prior domain knowledge. Finally, we showed how predictive intervention modeling can improve future experimental design, illustrated by an application where we predicted and validated previously unknown T-cell regulators with therapeutic potential for cancer immunotherapy.