Research Project Title:
Causal Representation Learning for Predicting Drug Effects on Gene Expression.
abstract:Predicting the effect of given chemicals on gene expression (e.g., reverting a diseased cell back to its normal state) can be framed as a causal imputation problem: given a set of perturbations applied to various contexts, we seek to make predictions on unseen perturbation/context pairs. Since the raw perceptual data contains a large number of redundant variables (e.g., housekeeping genes), we turn to representation learning to find low-dimensional causal features that enable us to make such predictions. In this project, we build on the recently proposed Causal VAE model, which introduces a causal layer after the encoder to transform the latent into a causal variable. This allows us to model the interventions in a structurally faithful way and predict the outcomes of unseen perturbations.
From this SuperUROP, I hope to learn more about research in causal machine learning, which is a field I find interesting because it attempts to tackle many of the current “improvement areas” in machine learning (OOD generalization, spurious correlations, etc.). Moreover, I am interested in exploring the relationship between ML and biological problems. I’m very excited to work with this group!