I am a Ph.D. candidate at the NYU Center for Data Science, advised by Kyunghyun Cho and Krzysztof Geras. In my research, I apply ideas from causality to improve out-of-distribution generalization in machine learning. My aim is to develop simple and effective methods that are applicable to important real-world problems such as drug discovery.

News
Sep. 25, 2024: “Jointly modeling inter- & intra-modality dependencies for multi-modal learning” was accepted to NeurIPS 2024.

Apr. 22, 2024: I am interning with the Regev lab at Genentech to work on causal representation learning.

Aug. 29, 2023: “Detecting incidental correlation in multimodal learning via latent variable modeling” was accepted to TMLR.

May 8, 2023: I am interning with the Prescient Design group at Genentech to work on causal representation learning.

Sep. 14, 2022: “Generative multitask learning mitigates target-causing confounding” was accepted to NeurIPS 2022.