Embedded Files
SC INBRE Data Science Core Seminar Series
UPCOMING
Friday, March 27, 2026, 12 to 1 pm, via Zoom (register)
Statistical Significance of Clustering and Some Recent Developments
Dr. Yufeng Liu
John D. MacArthur Professor of Statistics
University of Michigan
The Data Science Core of the South Carolina IDeA Networks of Biomedical Research Excellence (SC INBRE) presents the next in their Data Science Seminar Series to be presented by Dr. Yufeng Liu. Dr. Liu’s research interests include statistical machine learning, complex data analysis, precision medicine, bioinformatics, and e-commerce. He has published more than 150 papers on top statistical research and application journals. Dr. Liu served as principal investigators for multiple research grants from National Science Foundation and National Institute of Health. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute.
Seminar is open to all. Use the link above to register to receive the Zoom link.
For questions or more information, please contact our Data Science Co-Directors:
Presented on Friday, March 6, 2026
Dynamic System for Modeling Disease Progression with Temporal Registration
Dr. Shanghong Xie
Assistant Professor
Department of Statistics
University of South Carolina
Dr. Xie is an Assistant Professor in the Department of Statistics at the University of South Carolina. She earned her PhD in Biostatistics from Columbia University and completed her postdoctoral training there. Dr. Xie’s research interests lie in the broad areas of machine learning, network analysis, precision medicine, functional data analysis, causal inference, mediation analysis with neuroimaging and genetic biomarkers, and variable selection. Her work focuses on developing machine learning methods and generative models to address complex challenges in neuroscience, medicine, and public health.
Presented on Friday, January 23, 2026
Empowering Maternal Health with AI: Large Language Models for Coding and Reinforcement Learning for Resource Allocation
Dr. Yuhao Kang
Assistant Professor, Department of Geography and the Environment
Director, GISense Lab
The University of Texas at Austin
Dr. Kang’s lab focuses on Human-centered Geospatial AI and GIS to understand human-environment relationships and address real-world challenges. He previously leveraged Large Language Models to support coding process of maternal health. He is currently developing a cutting-edge Reinforcement Learning to support maternal health resource allocation to inform policy, and measuring maternal care accessibility to identify underserved regions.
Dr. Kang previously worked at the University of South Carolina, Google X and the MIT Senseable City Lab, and earned his PhD from the University of Wisconsin-Madison.
He was the recipient of the Waldo-Tobler Young Researcher Award by the Austrian Academy of Sciences, CaGIS Rising Award, CPGIS Education Excellence Award, etc.
Page updated March 6, 2026
Page updated
Google Sites
Report abuse