Meet Qian Tang, Postdoctoral Fellow

IRSA Faragher Distinguished Postdoctoral Fellow Qian Tang joined the School of Statistics faculty this fall. Tang’s research focuses on improving prediction performance in datasets.  

What brought you to the University of Minnesota?

I chose the University of Minnesota because of its strong reputation in statistics and the breadth of expertise in the department. There are leading researchers across many areas, which creates excellent opportunities for collaboration and for learning from some of the best statisticians in the field. In addition, my PhD advisor, Dr. Boxiang Wang, is a graduate of this department, and hearing about his experience here made me really look forward to joining and becoming part of this community.

What are your areas of specialty? How did you become interested in what you study and teach?

My research interests lie at the intersection of machine learning, optimization, and statistical computation, with a particular focus on the development of efficient algorithms for analyzing modern and complex data structure. Currently, I am working on high-dimensional transfer learning with Dr. [Hui] Zou. We are developing a new transfer learning method for clustering that aims to improve prediction performance on a target dataset by leveraging information from related source datasets. In addition, I am working on Bayesian quantile regression with Dr. [Galin] Jones, and on convex clustering methods for spatial transcriptomics data with Dr. [Eric] Chi.

What are you most excited about right now? 

I really enjoy collaborating with the many wonderful researchers in the School of Statistics. In addition, the Twin Cities have so many great restaurants, exploring new places to eat is something I’m very interested in right now. There are also many beautiful lakes, and it’s very relaxing to take walks by the water. Overall, I enjoy life here very much.

This story was edited by Avery Vrieze, an undergraduate student in CLA.