Lingzhou Xue receives ICDS Seed Grant to develop AI for risk prediction
Lingzhou Xue, professor of statistics at Penn State, is part of a multidisciplinary team selected to receive a Mid-Scale Seed Grant from the Penn State Institute for Computational and Data Sciences (ICDS). The new seed grants are designed to expand multi- and interdisciplinary research projects that can build new collaborations; increase the competitiveness of an upcoming high-risk, high-reward proposal; build a foundation for future large projects; and support Penn State researchers contributing to multidisciplinary teams that extend beyond the University.
The project is led by Ying Sun, assistant professor of electrical engineering, and also includes Dajiang Liu, distinguished professor of public health sciences and biochemistry and of molecular and precision medicine in the College of Medicine. The team will develop a new method for training AI models to support risk prediction and decision-making. Xue brings expertise in creating efficient approaches for training predictive models using data from many different sources. This approach allows organizations to work together effectively without sharing sensitive information, ensuring robust data privacy and security.
In his research, Xue explores how to build reliable models and learn from large and complex datasets. Much of his recent work focuses on learning from data collected across diverse sources, and designing models that can support better decision-making in areas such as health, the environment, and society. In recent years, Xue has been actively working with his students and collaborators to advance machine learning theory and methods, particularly in reinforcement learning, federated learning, and foundation models. His research is regularly featured at the field’s most prestigious venues, including the International Conference on Learning Representations, the International Conference on Machine Learning, and the Neural Information Processing Systems.
Xue received a bachelor’s degree in statistics from Peking University in 2008 and master’s and doctoral degrees in statistics from the University of Minnesota in 2011 and 2012, respectively. He was a postdoctoral research associate at Princeton University from 2012 to 2013. Xue is a dedicated mentor to Ph.D. students and postdoctoral researchers, and five of his former advisees have become tenure-track faculty members in statistics. Currently, he serves as an associate editor for the Journal of the American Statistical Association, Annals of Applied Statistics, Stat, ACM Transactions on Probabilistic Machine Learning, and Data Science in Science, and also serves as the area chair for the International Conference on Learning Representations.