Terry (Taehan) Kim

Hi! I’m Terry (Taehan) Kim, an incoming Ph.D. student in EECS at MIT. During my undergraduate studies at UC Berkeley, I broadly explored AI4Science, including AI drug discovery, biological structure prediction, and interpretability.

My long-term goal is to build full-stack AI systems that accelerate scientific discovery across computational infrastructure, scientific modeling, and reliable interpretation.

At the modeling layer, I am interested in efficient architectures for large, structured scientific datasets. At the interpretation layer, I aim to develop statistically reliable and interpretable methods for understanding complex biological data.

At the infrastructure layer, I work on generative and interpretable methods for inverse design in silicon photonics. I believe this could become a core direction for the next wave of AI infrastructure and data centers.

Across these areas, I am motivated by a common question: how can we remove the computational and methodological bottlenecks that prevent promising ideas from becoming reliable scientific discoveries and deployable technologies?