Jiaqi Zhang (MIT): Statistics and DSI Joint Colloquium

Please join us for a Statistics and DSI joint colloquium.

Thursday, February 26
4:00pm – 5:00pm
Data Science Institute, Room 105
5460 S University Ave, Chicago, IL 60615

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.

Bio: I’m a final-year Ph.D. student in MIT EECS, advised by Caroline Uhler. My research focuses on establishing statistical and algorithmic foundations for discovery and decision-making within systems created by underlying causal rules. In particular, I develop tools to understand causal relationships from data, model and extrapolate to predict the effects of interventions, and select informative interventions for experimental design. Motivated by problems in cell biology, these tools help accelerate mechanistic discovery and translation to biomedical engineering.

My research has been supported by the Eric and Wendy Schmidt Center PhD Fellowship and the Apple Scholarship. I was a research intern at Byedance, Microsoft Research, and Apple. I obtained my Bachelor’s degree in Mathematics from Peking University, where I worked with Zaiwen Wen, Mengdi Wang, and Le Cong. I am a recipient of the Stuart L. Schreiber Award in Scientific Excellence and was selected for Rising Stars in EECS.