The University of Illinois Urbana-Champaign Department of Statistics is pleased to welcome three new assistant professors—Xin Bing, Chen Cheng, and Yujia Zheng—to the department beginning in the 2026–27 academic year. Their research spans machine learning theory, high-dimensional statistics, causal inference, and modern statistical methodology, strengthening the department’s work at the intersection of statistics, data science, and machine learning.
The three new faculty members bring diverse expertise and research perspectives to the department, with work that addresses emerging challenges in statistical theory and methodology and their applications to complex, high-dimensional data.
Xin Bing
Xin Bing joins Illinois after serving as an Assistant Professor in the Department of Statistical Sciences at the University of Toronto since 2022. He earned his Ph.D. in Statistics from Cornell University, where he was jointly advised by Florentina Bunea and Marten Wegkamp. He also holds an M.S. in Statistics from the University of Washington and bachelor's degrees in mathematics and statistics and financial mathematics from Shandong University.
Bing’s research focuses on developing statistical methodology with theoretical guarantees for modern, high-dimensional problems. His research interests include high-dimensional statistics, low-rank matrix estimation, mixture models, multivariate analysis, model-based clustering, latent factor models, topic models, minimax estimation, and high-dimensional inference.
His work also examines statistical and computational trade-offs and applications of statistical methodology across fields including genetics, neuroscience, and immunology. He has additional research interests in the use of the Wasserstein distance in statistics.
Bing will begin teaching in the Department of Statistics in spring 2027.
Chen Cheng
Chen Cheng joins the Department of Statistics as an Assistant Professor after serving as a postdoctoral scholar in Statistics at the University of Chicago. He earned his Ph.D. in Statistics from Stanford University, where he was jointly advised by John Duchi and Andrea Montanari, and his bachelor’s degree in Computational Mathematics from Peking University.
Cheng’s research focuses on theoretical questions motivated by modern machine learning. His work develops theoretical tools for understanding increasingly complex statistical and machine learning models, including overparameterized models, neural networks, structured data such as matrices and heterogeneous data sources, and iterative algorithms.
His research draws on random matrix theory, high-dimensional statistics, and information theory, with applications to areas including machine learning, deep learning, conformal prediction, and reinforcement learning.
“I am interested in studying problems motivated by modern machine learning and building theoretical tools to better understand these models and algorithms,” Cheng writes on his research website.
Cheng will teach STAT 410 during the fall 2026 semester.
Yujia Zheng
Yujia Zheng joins the Department of Statistics as an Assistant Professor and will also be affiliated with the Department of Computer Science. He received his Ph.D. from Carnegie Mellon University in 2026, where he was advised by Kun Zhang.
Zheng’s research explores how statistical and machine learning methods can uncover the underlying processes that generate observational data. His work seeks to move beyond simply identifying correlations by developing methods that can provide insight into the causal mechanisms underlying complex data.
His research focuses on causal representation learning, trustworthy machine learning, and foundation models. In particular, Zheng is interested in developing foundations for causal representation learning and translating these insights into foundation models, including world models, with an emphasis on improving both efficiency and reliability.
His broader research areas include machine learning, causality, latent variable models, and generative models.
Zheng will begin teaching in the Department of Statistics in spring 2027.
Expanding Illinois Statistics
Together, Bing, Cheng, and Zheng bring complementary expertise to Illinois Statistics, contributing to the department’s growing strength in statistical theory, machine learning, high-dimensional data analysis, and emerging areas of data science.
Their research reflects the increasingly interconnected nature of statistics and machine learning, while also extending statistical methodology into applications involving complex data and scientific questions. Their arrival will provide new opportunities for collaboration across the department and with researchers throughout the broader Illinois community.
The Department of Statistics looks forward to welcoming all three faculty members to Illinois and to the contributions they will make to the department’s research, teaching, and academic community.