معرفی
Xiaojing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. She holds multiple affiliations including the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), the Connecticut Institute for the Brain and Cognitive Sciences (IBACS), and the Center for Environmental Sciences and Engineering.
Education:
- Ph.D. in Statistics and M.A. in Economics from Duke University (2012)
- M.S. in Probability and Mathematical Statistics from Chinese Academy of Sciences (2008)
- B.S. in Information and Computational Science from Hunan University (2005)
Her research focuses on Bayesian methodologies with applications spanning neural decoding, environmental science, psychometrics, and computational biology. She develops novel statistical models for high-dimensional data including latent variable frameworks for neural spiking activity and dynamic item response theory for educational testing. Her work integrates machine learning with traditional statistical inference to address complex problems in interdisciplinary domains.
Wang's publications demonstrate a consistent focus on Bayesian computation and model development, with significant contributions to Gaussian process methodologies, subgroup analysis in clinical applications, and ecological forecasting. Recent work shows increasing emphasis on neural data science and computationally intensive methods for high-dimensional inference.
Awards and Honors:
- NSF Faculty Early Career Development (CAREER) Award (2019)
- Elected Member of International Statistical Institute (ISI) (2017)
- Jeffrey’s Excellence Prize for Best Methodological Poster (2013)
As a statistical consultant, she advises through UConn's Statistical Consulting Services. Her methodological innovations support applications across health interventions, cognitive science, and environmental research through interdisciplinary collaborations.
