معرفی
Jeffrey Negrea is an Assistant Professor at the University of Waterloo. His research focuses on foundational aspects of machine learning, statistics, and optimization, with a particular emphasis on stochastic algorithms, Markov chain theory, and adversarial analysis. His work bridges theoretical computer science and applied statistics, addressing challenges in algorithmic convergence, generalization bounds, and scalable inference methods.
Key research directions include developing robust methods for online learning, analyzing stochastic iterative algorithms through scaling limits, and exploring the interplay between information theory and machine learning generalization. He has contributed to advancements in Thompson Sampling for adversarial environments, minimax regret optimization, and the theoretical underpinnings of MCMC and approximate inference techniques.
His publications span topics from adversarial analysis of Thompson Sampling to quantitative error bounds for stochastic processes. While no formal awards or grants are explicitly listed in the provided data, his active publication record reflects ongoing contributions to computational statistics and machine learning theory. No student advising or lab affiliations are mentioned in the available information.
