معرفی
Daren Wang is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, within the College of Science. He holds a Ph.D. in Statistics from Carnegie Mellon University (2018), an M.S. in Mathematics from the University of Michigan (2011), and a B.S. in Mathematics from the University of Virginia (2010). His research focuses on high-dimensional statistical methods, including scientific computing, nonparametric estimation, and change point detection in functional and temporal-spatial models. He has developed R packages such as 'changepoints' for change point localization and 'functional_regression' for mixed predictor models. His work bridges theoretical statistics, machine learning, and computational methods.
Research interests include high-dimensional linear algebra applications to machine learning, nonparametric estimation in complex systems, and the development of efficient algorithms for change point detection in dynamic networks and time series. He advises Ph.D. students from diverse backgrounds, including applied mathematics and computational physics. His contributions include advancements in density estimation via tensor decomposition and variance-reduced sketching techniques.
