Xingche Guo will join UConn after serving as a postdoctoral research scientist in the Department of Biostatistics at Columbia University. He obtained his Ph.D. in Statistics from Iowa State University. Xingche Guo’s research interests lie in the development of innovative and interpretable statistical/machine learning methods and theory, equipped with computationally efficient algorithms, to tackle real-world challenges, including mental health, neuroscience, agriculture, and environmental health. He is actively engaged in the development of a broad range of methods, including semiparametric reinforcement learning, variable selection methods for high-dimensional functional data, state-space models, latent variable models, spatial models, Bayesian methods for large-scale data, and deep learning.

Trevor Harris will join UConn after serving as an assistant professor in the Department of Statistics at Texas A&M University. His current research is primarily on the application of deep learning to problems in Climatology and Epidemiology and on developing robust tools for applying deep learning models in scientific contexts. Ongoing work develops analyzes and post-processes climate model output with deep neural networks, tools for granger causality and policy evaluation with differentiable models, forecasts West Nile virus with graph neural networks, and more. Past research includes work in functional data analysis, anomaly detection, change point detection, and robust nonparametric hypothesis testing.

Wenrui Li will join UConn after serving as a postdoctoral researcher at the University of Pennsylvania. She received her Ph.D. in Statistics from Boston University. Her research interests include high-dimensional data analysis, statistics for network data, causal inference under interference, and statistical methods for infectious disease transmission and surveillance.