I am an Associate Professor (with tenure) in the Department of Information & Decision Sciences within the College of Business Administration, University of Illinois Chicago [CV].

My research lies at the intersection of high-dimensional statistics, robust inference, and modern machine learning. I develop principled statistical methodologies that remain reliable under complex data environments, including heavy-tailed noise, model misspecification, dependence, and high dimensionality. A central theme of my work is advancing robust and scalable methods for quantile regression, expected shortfall regression, and nonparametric learning, tools that are essential for risk modeling, econometrics, and data-intensive business applications. I am also interested in the theoretical foundations of deep learning and neural network models, particularly their behavior under non-ideal data conditions. My past work extends classical asymptotic theory and moderate deviation techniques to modern inference problems, enabling valid uncertainty quantification in high-dimensional and distributionally challenging settings.

Overall, my research integrates asymptotic theory, robust statistics, optimization, and machine learning to develop practical, theoretically grounded tools for real-world data analysis. A complete list of my publications is available on Google Scholar.