As previously announced, Chengchun Shi of the London School of Economics and Political Science has received the 2025 Peter Gavin Hall IMS Early Career Prize. Dr. Shi receives the award “for ground-breaking contributions to the development of a wide range of statistical methods and advanced tools in reinforcement learning and their applications to healthcare and technological industries, bridging gaps between statistics and machine learning, and combining exciting theory with practical usefulness.”

The Peter Gavin Hall IMS Early Career Prize annually recognizes one researcher within the first eight years of completing their doctoral degree. Dr. Shi’s outstanding achievements recognize his potential to shape the future of statistics. His dedication and expertise have positioned him as an emerging leader in the field, and his innovative contributions continue to push the boundaries of statistical research.

Chengchun Shi is an associate professor (and formerly assistant professor) of data science at London School of Economics and Political Science (LSE). His PhD in Statistics is from North Carolina State University (NCSU), working with Wenbin Lu and Rui Song; prior to this, he obtained a BS in Statistics from Zhejiang University. He received last year’s IMS Tweedie Award, and delivered the Tweedie lecture at the New Researchers Conference. He has also received the 2021 Royal Statistical Society (RSS) Research Prize, “for his impressive contributions to the statistical analysis of complex data. Of particular note is his paper ‘Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects’ (co-authored with Rui Song, Wenbin Lu and Bo Fu, JRSSB, 80, 681-702).”

According to his departmental webpage, Dr. Shi’s research is concentrated on statistical learning methods in individualized decision making and statistical analysis of complex data. “The motivation behind his work stems from real world applications. In precision medicine, individualizing the treatment decision rule can capture patients’ heterogeneous response towards treatment. In finance, individualizing the investment decision rule can improve individual’s financial well-being. In a ride-sharing company, individualizing the order dispatching strategy can increase its revenue and customer satisfaction. With the fast development of new technology, modern datasets often consist of massive observations, high-dimensional covariates and are characterized by some degree of heterogeneity. In an era of big and complex data, he is interested in developing computationally efficient algorithms with statistical performance guarantees.”