Haotian Jiang

Haotian Jiang is an Assistant Professor of Computer Science at the University of Chicago. He previously served as a Postdoctoral Researcher at Microsoft Research, Redmond, and earned his PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington under Yin Tat Lee. His research focuses on the design and analysis of algorithms for optimization problems, including continuous and discrete optimization, and leverages discrepancy theory as a foundational tool. His work has been recognized with a Best Student Paper Award at SODA 2021.

Education:

• PhD in Computer Science from the University of Washington (2022)

Research interests encompass theoretical computer science, applied mathematics, algorithm design, and computational complexity. He explores methodologies in discrepancy theory to address challenges in optimization, emphasizing both foundational and applied aspects. His recent publications highlight advancements in quasi-Monte Carlo methods, differential privacy, and convex optimization techniques.

His articles span topics such as geometric analysis of tensors, privacy-preserving synthetic data, and efficient convex minimization algorithms. While no specific grants or current advising roles are listed, his academic position suggests active participation in research mentorship and collaborative projects.

Affiliations include the Department of Computer Science at the University of Chicago, with an office in the John Crerar Library Building. No lab or team affiliations are explicitly mentioned in the text.