معرفی
March Boedihardjo is an Assistant Professor in the Department of Mathematics at Michigan State University (MSU). His academic journey includes postdoctoral roles at ETH Zurich (2022-2023), the University of California, Irvine (2021-2022), and UCLA (2016-2021). He earned his Ph.D. in Mathematics from Texas A&M University (2012-2016) under advisors Bill Johnson and David Kerr, and holds a B.Sc./M.Phil. from Hong Kong Baptist University (2007-2011).
Boedihardjo's research focuses on operator algebras, random matrices, and privacy-preserving data synthesis. His work bridges functional analysis and modern data science, with contributions to covariance loss minimization, max-sliced Wasserstein distances, and privacy-utility tradeoffs in synthetic data. He has presented at venues like Carnegie Mellon's Probability/Math Finance Seminar and the International Conference on Machine Learning (ICML). His teaching includes advanced courses like MTH 890: Readings in Mathematics.
His research outputs span foundational mathematics (e.g., C*-algebra representations on ℓp spaces) and applied topics (e.g., differentially private synthetic data generation). While no specific awards are listed, his active conference participation reflects scholarly engagement in operator theory, probability, and data science intersections.
