About Me

I am a last year PhD student at University of Southern California studying Machine Learning in Yan Liu's Melady Lab. I graduated from The Ohio State University in 2020 with a double major in Computer Science and Mathematics. My current research focuses on rooting the impressive representational power of deep neural networks with: high-dimensional statistics for theoretically grounded methods; interpretability for human-machine interaction and decision-making; and causality-based approaches for data-driven reasoning.

Research

The essence of my research direction is to make sense of the amazing power of deep neural networks. I am approaching this problem from a variety of different perspectives mainly focusing on interpretability, generalizability, statistical validity, and causality. My research statement and current directions can be viewed here. Some of my latest projects can be viewed here. My current direction mainly focuses on interpretable machine learning and probabilistic modeling (causal discovery) with a focus on the problem of feature interactions, specifically the problem of feature interaction selection.

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