Hi 👋! I am a Ph.D. student at Purdue University in the Probabilistic and Understandable Machine Learning Lab led by Dr. David Inouye. My main research interest is to enable the development of robust and trustworthy generative AI models (e.g., LLMs) for safe deployment in an increasingly AI-driven world. I am working on advancing this from the perspectives of developing novel methods to train/finetune ML models for better generalization performance to new settings and via building ML tools to monitor and understand the dynamics of a deployment environment. My works have been published in top-tier conferences such as NeurIPS, ICML, ICLR, and CVPR, are being patented by Microsoft, and are being integrated into Microsoft Azure’s ML monitoring toolbox as well as Microsoft Office’s Query Understanding pipeline.
I have worked in various ML research roles for both production-level and research-level industry impacts. This includes working with Ankur Mallick and Kevin Hsieh from Microsoft Research, Bhavya Kailkhura from Lawrence Livermore National Lab, and Nicholas Waytowich from the Army Research Lab.
PhD in Computer Engineering, 2019 - Dec 2023
Purdue University
BS in Electrical Engineering, 2015 - 2019
Purdue University
We build generative models by learning latent causal models from data observed from different domains for the purpose of generating domain counterfactuals, and further characterize the equivalence classes for such latent causal models.
We answer the question: ‘‘What is a distribution shift explanation?’’ and introduce a novel framework for explaining distribution shifts via transportation maps between a source and target distribution which are either inherently interpretable or interpreted using post-hoc interpretability methods.
We introduce a large-scale easy to use spatial reasoning including 3.6 million images summarizing 10-seconds of human-played matches from the StarCraft II video game.
This work with Microsoft365 Research studied using generative language models (e.g., LLMs) to improve enterprise search results in Microsoft Apps by adding related search terms to the user’s search query.