Sheng Jiang 江声

Welcome to my website! 

My research is in Bayesian nonparametrics and computational Bayesian statistics, with a focus on developing flexible, scalable methods for large and complex datasets. I work on Gaussian process models, dynamic network models, and Bayesian mixture-of-experts methods for structured, high-dimensional, and time-evolving data. Recent projects include dynamic integer-valued networks, dynamic bi-directed Euclidean networks, Gaussian processes on graph structures, and scalable Bayesian inference through variational Bayes and predictive resampling. My work is motivated by applications in urban traffic flow, oceanographic flow cytometry, Cryo-EM data analysis, environmental science, and related scientific domains.

Before joining CUHK-Shenzhen, I served as a Visiting Assistant Professor in the Department of Statistics at the University of California, Santa Cruz. Before moving to Santa Cruz, I received my statistics training at Duke. My PhD advisor is Professor Surya T. Tokdar; I also worked with Professor Alex Volfovsky and Professor Galen Reeves during my post-doc.  Prior to studying statistics, I studied economics at Tsinghua University. 

Here are my resume and Google Scholar. 

I'm looking for motivated students to work with me! 

For prospective PhD students interested in the application, and/or theoretical aspects of Bayesian nonparametrics, I encourage you to reach out. Please send me an email that includes a brief overview of your research interests, relevant experience, and, if available, a short writing sample or statement of purpose. I look forward to learning more about your goals and how we might collaborate. 

We are currently seeking motivated graduate students to join us in addressing critical challenges in urban transportation systems. We focus on building intelligent systems that model, predict, and optimize urban mobility, from multi-agent traffic flows to human travel behaviors, enhancing efficiency, safety, resilience, and sustainability. Our work spans traffic flow modeling and forecasting, public transit network evaluation, travel mode choice modeling, and the development of “digital twin” platforms for data-driven and model-based policy-making.  

Description of our research group: Chinese version, English version. To apply, please follow the instructions on this page.