Sheng Jiang 江声

Welcome to my website! 

My research interests primarily lie in Bayesian nonparametrics, encompassing both theoretical and computational aspects, as well as applications to real-world data. My goal is to develop novel methods for understanding large, complex datasets in real-world applications, striking a balance between modeling flexibility and scientific interpretability. Specifically, I have been working on Bayesian nonparametric methods with Gaussian process priors, network models,  variational Bayes, urban traffic flow data, Cryo-EM data, and oceanographic flow cytometry data analysis.

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.