Mayank Goswami

Mayank Goswami is an Associate Professor at the Department of Computer Science, City University of New York (CUNY) Queens College and Graduate Center. He earned his Ph.D. in Applied Mathematics and Statistics from Stony Brook University, advised by Joe Mitchell and David Gu, followed by a post at the Max-Planck Institute for Informatics in Germany. His research spans Algorithms, Computational Geometry, Theoretical Machine Learning, and Conformal Geometry, with a focus on geometric optimization, data structures, and adversarial robustness in AI. His work addresses problems like diverse solution design, Teichmuller maps, and noise-tolerant learning models.

Publications highlight trends in

• Geometric algorithms for NP-hard problems (FCT 2025, ICALP 2025)
• Theoretical ML for manifold topology and adversarial robustness (TMLR 2025, AISTATS 2022)
• Efficient data structures for similarity search and sorting (WADS 2024, APPROX 2024)

He has received a Best Paper Award at FCT 2025 and NSF grants for projects on geometric solution diversity (CCF-1751847) and dynamic optimality conjectures (CCF-1910873). His students include Paul Cesaretti and GiBeom Park, and he organizes the Queens College CS Colloquium and Fall Workshop on Computational Geometry (FWCG 2018, 2024–2025).