Upcoming Event: Oden Institute & College of Natural Sciences

Learning and Design from Large-Scale Interventions in Cellular Systems

Jiaqi Zhang, PhD Candidate, Massachusetts Institute of Technology

Abstract

Complex molecular mechanisms govern cellular functions in living organisms and shape their behavior in health and disease. Understanding these mechanisms can greatly accelerate therapeutic discovery, yet it remains challenging due to the high dimensionality and intricate dependencies within biological systems. Recent advances in experimental technologies, however, are beginning to make this problem more tractable. For example, we can now systematically perturb individual or combinations of genes in single cells and measure their downstream effects, enabling empirical identification and validation of causal relationships. Nevertheless, perturbation data remain noisy and high-dimensional, with effects often sparse and subtle.

In this talk, I will present our work addressing three key challenges in this emerging paradigm: (1) how to define and learn the causal programs that govern high-dimensional or perceptual data; (2) how to model perturbational effects in a way that enables the prediction of novel perturbations; and (3) how to design future experiments to achieve desired cellular states. For (1), we introduce new causal representation theories that guarantee identifiability of the underlying causal programs under appropriate conditions. For (2), we develop a modular framework that models perturbation effects through distributional discrepancies. This approach captures nuanced, sample-level changes and enables extrapolation to predict the effects of unseen perturbations across diverse conditions and data types. Finally, for (3), we illustrate how iterative integration of perturbational data and machine learning can prioritize gene targets for desired T-cell state changes, leading to experimental validation of novel regulators with cancer immunotherapy potential.

Biography

Jiaqi Zhang is a final-year PhD candidate in Electrical Engineering and Computer Science at MIT. She earned a BSc in Mathematics from Peking University. Her research focuses on establishing statistical and algorithmic foundations for learning and decision-making in causal systems, grounded in applications to cell biology. Her work has been featured in NeurIPS, ICML, and Nature Machine Intelligence, and is supported by the Eric and Wendy Schmidt Center Fellowship at the Broad Institute and the Apple AI/ML PhD Scholarship. She is a recipient of the Stuart L. Schreiber Award in Scientific Excellence and was selected for Rising Stars in EECS.