Machine learning, optimization and sampling algorithms
My research is in the design and analysis of efficient algorithms for fundamental problems in machine learning, including for problems in optimization, sampling, and game theory. We seek to develop rigorous computational and statistical guarantees for practical algorithms that can be applied to large-scale machine learning or scientific applications. We seek to understand the dynamics of interacting agents, to control the flow of information and summarize their collective behaviors.
Andre Wibisono receives his BS degree in Mathematics and Computer Science from MIT, and his PhD in Computer Science at UC Berkeley. He joined Yale University in 2021 as an assistant professor in Computer Science.