The primary focus of our lab’s research is to apply our expertise in statistics, encompassing a range of methodologies including semiparametrics, robust methods, unconventional likelihood methods, feature selection, and post-selection inference. Additionally, we specialize in statistical learning techniques such as semi-supervised learning, transfer learning, federated learning, and non-convex optimization. Our objective is to analyze data with complex structures across a diverse array of biomedical studies.

In our lab, we are not only interested in traditional randomized controlled trials but also in their integration with other modern data types. By combining different data sources and utilizing advanced statistical methods, we aim to gain deeper insights and improve our understanding of various biomedical phenomena. Our research endeavors revolve around addressing the challenges posed by massive datasets, which require innovative approaches to extract meaningful information. Through our work, we strive to contribute to the advancement of biomedical research by leveraging statistical methodologies and statistical learning techniques. Our ultimate goal is to enhance the quality of healthcare and make significant contributions to scientific knowledge in the field.

Our lab is driven by a strong motivation and unwavering enthusiasm for interdisciplinary research spanning various disciplines, including clinical trials, observational studies, and beyond. We embrace the opportunity to collaborate across diverse fields to broaden our understanding and make meaningful contributions. Presently, our lab is particularly invested in exploring patient-reported outcomes, clinical trials, real-world evidence, real-world data, health disparities, health equity, and health services research. These areas represent key focal points where we seek to uncover insights, address pressing challenges, and drive advancements in biomedical knowledge.

We are constantly eager to forge new and exciting collaborations in order to engage in cutting-edge research. By establishing novel partnerships, we can combine expertise and perspectives from different disciplines, leading to innovative approaches and transformative discoveries. We welcome opportunities to join forces with like-minded researchers and push the boundaries of scientific exploration in pursuit of improved healthcare outcomes.

Dr. Zhao is now serving as the Principal Investigator (PI/MPI/Co-PI) of the following grants:

Title: CoPE II: Individualizing Patient-Reported Outcomes in Patient Care for Vocal Fold Paralysis in the Clinic and in Research (MPI: Francis/Zhao)
Duration: 9/18/2023-8/31/2028
Cost: $2.4M

Title: Semiparametric Techniques for Data Exploitation across Heterogeneous Populations
Duration: 7/1/2023-6/30/2026
Cost: $225K

NSF/NIGMS/1953526 and 2122074
Title: A Robust and Efficient Statistical Framework for Handling Missing-Not-At-Random Data in Patient Reported Outcomes and Beyond
Duration: 8/1/2020-7/31/2024
Cost: $0.6M

American Family Funding Initiative (administered by the Data Science Institute of UW-Madison)
Title: ELSA: Efficient Label Shift Adaptation for Accurate Claim Fraud Detection
Duration: 9/1/2023-8/31/2024
Cost: $100K