Likai Chen, our newly promoted Associate Professor of Statistics and Data Science, has been awarded a collaborative research grant from the National Science Foundation (NSF). The grant will support her joint project with Dr. Jiaqi Li, titled "Online Statistical Inference for Modern Machine Learning."

Dr. Jiaqi Li, who received her Ph.D. from our department in 2024, is currently a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago. Together, Dr. Chen and Dr. Li will develop mathematically rigorous methods for uncertainty quantification to enhance the trustworthiness of artificial intelligence (AI) systems.

AI relies on efficient machine learning algorithms to learn from large datasets, and statisticians play a crucial role in understanding the intrinsic behaviors and quantifying uncertainties of these systems. The principal investigators aim to provide a theoretical framework for online statistical inference in machine learning, focusing on constant learning-rate stochastic gradient descent (SGD) algorithms. They will explore theoretical guarantees and interpretability of various neural networks, develop robust estimation and inference methods for econometric and biomedical studies, and design algorithms for detecting real-time change-points in high-dimensional time series data. 

In addition to this new grant, Dr. Chen's research has been supported by two other NSF grants: one on a non-parametric framework to understand emergent behaviors of microbial consortia (URoL:EN-2222403) and another on nonparametric inference of temporal data (DMS-2311251).

We extend our heartfelt congratulations to Jiaqi and Likai on receiving this new NSF award!