Yusik Kim

My current research interest lies in the intersection of probability and formal logic to build transparent AI systems. Specifically, I am looking for symbolic knowledge representation systems that are capable of (imperfect) induction from observations and (imperfect) deduction from this induced knowledge together with any axiomatic facts. Identifying the appropriate probability semantics to quantify this imperfection and devising scalable inference algorithms are the main challenges.

In the past, I was involved in storage systems research and briefly left research to pursue a software engineering career.

I hold a B.S. in mathematics and statistics from Seoul National University and a Ph.D. in operations research from UC Berkeley.