Research

The lab’s primary research is to use our expertise in statistics (such as semiparametrics, unconventional likelihood methods, feature selection and post-selection inference) and in statistical learning (such as semi-supervised learning, transfer learning, robust methods and non-convex optimization), to analyze data with massive structures in various biomedical studies. The lab is interested in traditional randomized controlled trials, as well as its integration with other modern databases.

The lab is also highly motivated and always enthusiastic about interdisciplinary research in clinical trials and observational studies as well as in a wide range of disciplines. Currently, the lab is interested in patient-reported outcomes, electronic health records, health services research, pain research, cancer, health disparities, orthopaedics and sport medicine, and dental medicine. The lab is consistently looking forward to establishing new exciting collaborations of cutting-edge research.


Dr. Zhao serve(d) as the Principal Investigator of the following grants:

NSF/DMS/1953526
Title: A Robust and Efficient Statistical Framework for Handling Missing-Not-At-Random Data in Patient Reported Outcomes and Beyond
Duration: 8/1/2020-7/31/2023
Cost: $599,662


Dr. Zhao serve(d) as an editorial board member of the following journals:
Associate Editor for Stat, 2019-present
Associate Editor for Journal of Nonparametric Statistics, 2017-present
Associate Editor for Statistica Sinica (special issue), 2015-2018