Yanjun Han

Yanjun Han is an Assistant Professor in the Computer Science Department at New York University, affiliated with the Courant Institute of Mathematical Sciences and the Center for Data Science. She received her B.E. in Electronic Engineering from Tsinghua University in 2015, followed by M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 2021, under the supervision of Tsachy Weissman. Han was a postdoctoral scholar at the Simons Institute for the Theory of Computing, University of California, Berkeley, in 2021-22, and a Norbert Wiener postdoctoral associate at the Statistics and Data Science Center (SDSC) in MIT IDSS in 2022-23. Her research interests include the mathematics of data science, with a focus on statistics, learning theory, bandits, information theory, and empirical Bayes methods.

Yanjun Han is an Assistant Professor at New York University, specializing in statistics, learning theory, and information theory. Her research focuses on statistical estimation, learning algorithms, and information-theoretic limits in distributed and sequential decision-making problems. She has published extensively on topics such as minimax estimation of functionals of discrete distributions, performance limits in array localization, and optimal rates of entropy estimation. Her work also explores the interplay between communication constraints and statistical inference, as well as the design of efficient learning algorithms in auction settings and bandit problems. Han's research bridges theoretical foundations with practical applications in machine learning and signal processing.