Andre Wibisono

Andre Wibisono serves as Assistant Professor in Yale University's Department of Computer Science with a secondary appointment in Statistics & Data Science, joining the faculty in 2021 after postdoctoral research at University of Wisconsin-Madison and Georgia Institute of Technology.

His educational background includes:

• Ph.D. in Computer Science, UC Berkeley
• M.A. in Statistics, UC Berkeley
• M.Eng. in Computer Science, MIT
• S.B. in Mathematics and Computer Science, MIT

Wibisono's research focuses on algorithm design for machine learning through optimization, sampling, and game theory, leveraging dynamical systems and information theory to develop accelerated discrete-time algorithms from continuous dynamics. His work provides theoretical foundations for efficient machine learning systems with applications in generative modeling and constrained optimization.

Recent publications (2023-2025) demonstrate consistent innovation in Hamiltonian-based optimization, constrained-space sampling, and min-max game convergence, characterized by rigorous mathematical analysis connecting continuous dynamics to discrete algorithms. Key trends include randomized integration for acceleration, phi-divergence convergence guarantees, and symplectic geometry applications to mirror descent.

Scientific recognition includes:

• NSF CAREER Award for developing algorithmic frameworks bridging continuous and discrete dynamics

He actively mentors current students (Siddharth Mitra, Kaylee Yang, Jane Lee, Qiang Fu, Peter Wang) and has guided two postdocs to faculty positions. Research is funded through the NSF CAREER award and collaborative CIF grants focused on Hamiltonian dynamics for sampling and optimization.

His Yale research group develops theoretical foundations for next-generation machine learning algorithms, emphasizing mathematical rigor in optimization and sampling with applications to generative modeling and constrained inference problems.