William Kuszmaul

William Kuszmaul is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on designing and analyzing randomized algorithms and data structures, with a particular emphasis on hashing techniques, algorithm complexity, and data structure optimization. He holds a PhD from MIT, advised by Charles E. Leiserson, and previously served as the Rabin Postdoc of Theoretical Computer Science at Harvard University.

His research interests include theory and algorithms and complexity, with recent work addressing optimal bounds for open addressing, efficient cuckoo hashing, and tight analyses of linear probing. Notable contributions include studies on list labeling, minimal perfect hashing, and distributed load balancing under dependency constraints. Kuszmaul has advised students Jingxun Liang and Renfei Zhou in his current role.

His publications span 2023–2025, reflecting a focus on foundational algorithmic problems with applications in memory management, parallel computing, and data structure efficiency. Key themes include optimizing hash table performance, analyzing algorithmic phase transitions, and establishing theoretical limits for dynamic retrieval systems. No scientific awards are explicitly listed in the provided materials.

His academic trajectory includes postdoctoral work at Harvard and doctoral research at MIT, supported by the John and Fannie Hertz Fellowship. Current courses listed include 15-151 (Fall 2025) and 15-756 (Fall 2024), suggesting involvement in both introductory and advanced computer science education.