I am a PhD student in UCSD working with Prof. Meenakshi Khosla. My research is at the intersection of machine learning and neuroscience, focusing on how information is represented across different cortical areas of the human brain and how artificial neural networks can model these processes. I take a two-way approach: drawing inspiration from brain function to guide new computational methods, and using artificial models to probe the brain’s own mechanisms. I’m also interested in how diverse artificial systems encode information, how these representations parallel those in animal brains, and in developing a unified framework that embeds biological and artificial representations in a shared space.
Outside the lab, I enjoy hiking, climbing, and swimming. Feel free to reach out if my work resonates with you or if you’re interested in collaborating.

An universal embedding framework that brings together representations from different networks into a single common space using shape analysis and optimal‐transport theory.

Information encoded in the human language cortex can be modeled from diverse representational sources that differ substantially from the original linguistic stimulus in both form and modality.

A Similarity Network Fusion based framework that integrates diverse representational metrics to reveal how architecture and training jointly shape vision models’ computational strategies beyond superficial design categories.

Information encoded in the human language cortex can be modeled from diverse representational sources that differ substantially from the original linguistic stimulus in both form and modality.

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