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.

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: Despite their remarkable empirical success, language models remain poorly understood from a principled machine learning perspective. To this end, in this talk, we present a unified Markovian perspective on memory and reasoning in modern language models. We show that the attention mechanism can be formally interpreted as a context-conditioned Markov process, enabling a principled analysis of learning dynamics. Under this view, model memory corresponds to a Markov transition matrix, and incorporating new knowledge can be understood as expanding the state space. This perspective motivates embedding-level update methods for continual learning that achieve sample-efficient knowledge integration with zero catastrophic forgetting. Furthermore, by formulating multi-step reasoning as a Markov process, we analyze reasoning in small language models and explain why standard supervised fine-tuning and reinforcement learning can fail under sparse rewards. Together, these results demonstrate that Markov processes provide a unifying lens for understanding and improving memory, reasoning, and learning in language models.

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

Abstract:

Monday, 03/25/2026, 12:15PM – 1:30PM
Room: Science Building, C205
Speaker: Sai Zhang, NYU

Abstract: Augmented and virtual reality (AR/VR) systems are emerging as a critical computing platform in modern life, with increasing impact across fields like education, healthcare and industrial applications. Despite their growing importance, AR and VR devices operate under strict constraints on latency, energy consumption, and computational resources, making efficient system design a fundamental challenge. A defining characteristic that distinguishes AR and VR from conventional edge devices is their direct and continuous interface with the human user, where perception, attention, and intention fundamentally shape system behavior. By leveraging natural human behavior such as gaze, head motion, and hand interaction as first class signals, AR and VR systems can adapt their computation to what truly matters to the user, enabling selective processing and more efficient use of limited resources.

In this talk, I will present recent progress from my group on efficient AR and VR computing, spanning a broad range of applications including AI, graphics, and tracking, as well as the corresponding hardware and system designs that enable these solutions to be implemented efficiently. Together, these efforts illustrate how human centered system design can unlock new opportunities for building efficient, intelligent, and responsive AR and VR platforms.