This colloquium is intended to bring together Computer Science and Data Science researchers in the tri-state area (especially in NYC) and to foster collaboration. We welcome talks on any topic of interest to the CS community, including theory, algorithms, machine learning, and data science. If you are interested in attending in-person or online, or would like to give a talk, please contact Seminar Organizer, Jun Li at jun.li@qc.cuny.edu.

Monday, 02/09/2026, 12:15pm – 1:30pm
Room: Science Building, C205
Speaker: Danial Waxman, Basis AI

Abstract: Many real-world applications of machine learning require continuous, adaptive learning strategies over the course of deployment. We discuss a unified framework for online and sequential inference and ensembling of Bayesian models. We give particular focus to Gaussian processes, a family of flexible non-parametric models, and show how to construct general streaming estimators, and further show how they can be adapted to decentralized federated and robust learning. We finally discuss the fragility of the typical online ensembling method, Bayesian model averaging, and introduce a principled alternative from optimization theory, online Bayesian stacking.

Monday, 02/23/2026, 12:15pm – 1:30pm
Room: Science Building, C205
Speaker: Yingcong Li, New Jersey Institute of Technology

Abstract:

Monday, 03/09/2026, 12:15PM – 1:30PM
Room: Science Building, C205
Speaker: Lu Wang, Stevens Institute of Technology

Abstract: