主 讲 人： Florida State University, 朱凌炯 教授

报告时间：2025年7月7日 上午10:30-11:30

报告地点：览秀楼105学术报告厅

报告摘要:  Sampling is a powerful tool in modern-day applications in machine learning and generative AI. We will discuss two examples: Langevin algorithms and score-based generative models. Both are stochastic algorithms that can be used to solve large-scale problems in machine learning and generative AI respectively. In particular, we will provide non-asymptotic Wasserstein convergence guarantees and iteration complexities. Numerical results and applications will also be discussed.

主讲人简介：朱凌炯，博士，Florida State University教授和Thinking Machines杰出学者，博士生导师。2008年本科毕业于University of Cambridge。2013年博士毕业于New York University，师从S.R.S. Varadhan。 现任Probability in the Engineering and Informational Sciences编委。主要研究兴趣有应用概率，数据科学，金融工程及运筹学，在Annals of Applied Probability, Bernoulli, Finance and Stochastics, ICML, INFORMS Journal on Computing, Journal of Machine Learning Research, NeurIPS, Operations Research, Production and Operations Management, SIAM Journal on Financial Mathematics, Stochastic Processes and their Applications, Review of Economics and Statistics等杂志发表数十篇论文。曾多次主持 NSF项目。曾于2013年获得NYU Courant Institute的Kurt O. Friedrichs最佳博士论文奖，2022年获得FSU的发展学者奖，2023年获得FSU的研究生导师奖，MSOM Society的MSOM iFORM SIG最佳论文奖。