Jiaqi Zhang
MIT
viczhang@mit.edu
Bio
Jiaqi Zhang is a Ph.D. student in MIT EECS, advised by Caroline Uhler. Prior to that, she received her BacheloråÕs degree in Mathematics from Peking University. Her research focuses on establishing statistical and algorithmic foundations for decision-making within systems created by underlying causal rules. In particular, she develops algorithms and tools to understand causal relationships in data, extrapolate to predict the effects of unseen interventions, and select informative interventions for experimental design with applications in cell biology. Her research is supported by the Eric and Wendy Schmidt Center PhD Fellowship and the Apple AI/ML Scholarship.
Areas of Research
- AI for Healthcare and Life Sciences
Learning and designing of large-scale interventions
Advances in recent technologies have enabled the possibility to perform and measure the effects of external interventions in many fields. For example, it is now possible to target and perturb single or combinatorial genes in living cells. These provide a unique opportunity to study underlying causal mechanisms behind complex systems, such as the regulatory mechanisms behind cell states. In our work, we focus on three key aspects that have arisen from these new capabilities: (1) how to define and learn the causal programs governing high-dimensional or perceptual data; (2) how to model interventional effects in scenarios where the measurements are incomplete; and (3) how to design the next experiments if one is interested in achieving a desired state. For (1), we establish new causal disentanglement theories that guarantee identifiability of the underlying causal programs, given sufficient samples and regularizing conditions. For (2), we develop a scalable algorithm that models the interventional effect using the discrepancy between distributions. It can capture nuanced changes at the sample level and extrapolate to identify non-additive combinatorial perturbations. Lastly, for (3), we discuss our proposals borrowing ideas from extreme event theory for finding desirable interventions more efficiently.