Michael P. Kim is a Miller Postdoctoral Fellow at UC Berkeley, working with Shafi Goldwasser. Michael completed his PhD in the Stanford Theory Group under the guidance of Omer Reingold.
His research investigates foundational questions about responsible machine learning. Much of this work aims to (1) identify ways in which machine-learned predictors can exhibit unfair discrimination and (2) develop algorithmic tools that provably mitigate such forms of discrimination. More broadly, Michael is interested in how the computational lens (i.e. algorithms and complexity theory) can help tackle emerging societal and scientific challenges.
- Computational Complexity of Statistical Inference, Fall 2021. Visiting Postdoc.
- Probability, Geometry, and Computation in High Dimensions, Fall 2020. Visiting Postdoc.
- Theory of Reinforcement Learning, Fall 2020. Visiting Postdoc.
- Summer Cluster: Fairness, Summer 2019. Visiting Graduate Student.