We are pleased to announce the following members have been selected to receive the 2022 IMS Lawrence D. Brown PhD Student Award: Yaqi Duan, Yuetian Luo, and Tudor Manole. The award will fund their travel to next year’s IMS Annual Meeting, which takes place at JSM Toronto (August 5–10, 2023). They will each present a paper in the IMS Lawrence D. Brown PhD Student Award invited session.
Yaqi Duan is a postdoctoral researcher at the Laboratory for Information & Decision Systems at MIT, working with Martin Wainwright. Her research interests lie in machine learning, particularly statistical aspects of reinforcement learning. She graduated with a PhD degree from the Department of Operations Research and Financial Engineering at Princeton University; she received a BS in Mathematics from Peking University. In fall 2023, Yaqi will join the Department of Technology, Operations, and Statistics in the Stern School of Business at New York University as an Assistant Professor. The title of her talk is: “Optimal policy evaluation using kernel-based temporal difference methods.”
Yuetian Luo is a postdoctoral scholar in the Data Science Institute at the University of Chicago. He received his PhD in Statistics from the University of Wisconsin–Madison in 2022, advised by Anru Zhang. He is broadly interested in methodology, computation, and theory in complex and large-scale statistical inference problems. In the past, he has worked on developing efficient algorithms for high-dimensional matrix/tensor learning problems. Many of these problems are nonconvex and one of his focuses is to understand the statistical guarantees for these algorithms. Recently, he has been interested in distribution-free inference. The title of his talk is: “Tensor-on-tensor Regression: Riemannian Optimization, Over-parameterization, Computational Barriers, and their interplay”.
Tudor Manole is a fifth-year PhD candidate in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), jointly advised by Sivaraman Balakrishnan and Larry Wasserman. Before moving to CMU, he completed a BSc in Mathematics at McGill University. He is broadly interested in nonparametric statistics and statistical machine learning. Most of his recent research is focused on developing inferential methods for the optimal transport problem. He is also interested in theoretical aspects of latent variable models, and has worked on applications of statistical optimal transport to data-driven modeling in high energy physics. His talk is titled: “Plugin Estimation of Smooth Optimal Transport Maps”.
You can catch their talks at JSM Toronto. Registration opens May 1, 2023.
Yuetian Luo commented, “It is my great honor to be selected for the IMS Lawrence D. Brown award! It is a big encouragement to me and motivates me to continue my research in the statistics community. This award would have been impossible without the help I received from those around me, especially my advisor Anru Zhang. Sincere thanks to all of them!”