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Classes on the Columbia campus beginning at 5 p.m. Thursday, Sept. 26 and all classes scheduled for Friday, Sept. 27 will be virtual. sc.edu/weather

Department of Statistics

Welcome new faculty and students

The Department of Statistics is pleased to welcome Drs. William Consagra, Yuexuan Wu, and Shanghong Xie as new faculty members. 

Eight new graduate students also joined the Department in Fall 2024. Currently, our department has 16 tenured/tenure-track faculty, 9 instructors, 4 part-time instructors, 2 staff members, and 37 graduate students (32 Ph.D. students and 5 M.S. students).

Meet the New Faculty

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Dr. William Consagra joined the Department of Statistics as an Assistant Professor in Fall 2024. He earned his Ph.D. in Statistics from the University of Rochester in 2022 under the supervision of Prof. Xing Qiu and subsequently completed a two-year postdoctoral fellowship at Harvard Medical School. His research interests include functional data analysis, computational statistics, and deep learning, with applications to neuroimaging inverse problems and connectomics.

 

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Dr. Yuexuan Wu joined the Department of Statistics as an Assistant Professor in Fall 2024. He received his Ph.D. in Statistics from Florida State University in 2022 under the supervision of Prof. Anuj Srivastava. Before joining the department, he completed a two-year Biostatistics postdoc at the University of Washington, supervised by K.C. Gary Chan. His research interests include medical image analysis, shape and functional data analysis, computational neuroscience, and causal inference.

 

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Dr. Shanghong Xie joined the Department of Statistics as an Assistant Professor in Fall 2024. She received her Ph.D. in Biostatistics from Columbia University in 2019 and completed her postdoctoral training there under the supervision of Prof. Yuanjia Wang and Prof. R. Todd Ogden. Prior to joining the University of South Carolina, she was a faculty member in Data Science at the Southwestern University of Finance and Economics in China. Her 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, with applications in neuroscience, medicine, and public health.

 


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