Assistant Professor Miaoyan Wang received the prestigious NSF CAREER Award

Higher-order tensor datasets are rising ubiquitously in modern data science applications. Tensor provides an effective representation of data structure that classical low-order methods fail to capture. However, empirical success has uncovered a myriad of new challenges.

Miaoyan Wang, an assistant professor in the Department of Statistics at UW-Madison, has just received a CAREER award–the most prestigious early-career award given by the National Science Foundation (NSF). The award will allow Miaoyan to develop a suite of statistical learning theory, efficient algorithms, and data-driven solutions for high-dimensional tensor problems.

“I am very excited to push tensor learning theory to new realms. I also feel lucky to receive this funding support at my first attempt of NSF CAREER. My colleagues and staffs in the department have provided me tremendous support, and I’d like to take this opportunity to thank them.’’

Miaoyan Wang plans to investigate the fundamental computational-statistical tradeoffs for a range of tensor problems, including, but not limited to, low-rankness, non-negativity, block-structure, and smoothness. Optimization landscape will be studied for non-convex algorithms involving tensors. The new framework will fill in the gap between statistical oracles and the empirical algorithms for addressing higher-order high-dimensional tensor problems. The research will be applied to a variety of data problems, such as classification of brain connectivity data, pattern detection in recommendation systems, and omics data integration.

Miaoyan Wang has been a UW-Madison faculty member since 2018. Her research is in machine learning theory, nonparametric statistics, higher-order tensors, and applications to genetics. Her interdisciplinary research efforts have been reflected in her training. In 2015-2018, she was a postdoc at the Department of EECS at UC Berkeley and a Simons Math+X postdoc at University of Pennsylvania. She received a PhD in Statistics from the University of Chicago in 2015. She has won a Best Student Paper Award (with her as advisor) from American Statistical Association in 2021, the Madison Teaching and Learning Excellence Fellow, and multiple prestigious young researcher awards in statistics, machine learning, and genetics.