معرفی
Xingche Guo serves as an Assistant Professor in the Department of Statistics at the University of Connecticut, Storrs Campus. His research program develops advanced statistical methodologies with critical applications in behavioral decision-making and neuroscientific data analysis.
His primary research domains include:
- Reinforcement Learning and Inverse Reinforcement Learning
- Functional Data Analysis in high-dimensional contexts
- Variable Selection techniques for complex models
- Markov Chain Monte Carlo (MCMC) methods
- State-space modeling for dynamic systems
- Statistical applications in mental health and neuroscience
Analysis of recent publications reveals concentrated innovation in modeling decision dynamics through reinforcement learning frameworks applied to mental disorders and behavioral experiments. His methodological contributions frequently integrate hierarchical modeling and semiparametric approaches to address complex data structures in neuroscience, particularly for EEG analysis and biomarker-guided treatment development. The publication pattern demonstrates consistent output in top-tier statistical journals and growing interdisciplinary impact.
