Kent R. Quanrud

Kent R. Quanrud is an Assistant Professor in the Department of Computer Science at Purdue University, where he joined in Fall 2019. His research focuses on the design and analysis of algorithms within theoretical computer science, particularly in approximation algorithms, randomized algorithms, combinatorial optimization, and discrete geometry. He holds a PhD from the University of Illinois at Urbana-Champaign (UIUC), awarded in 2019.

Dr. Quanrud's work spans topics such as submodular function maximization, matroid intersection, densest subgraph algorithms, and optimization in directed graphs. His research is supported by the National Science Foundation (NSF). He has taught courses including undergraduate and graduate algorithms, randomized algorithms, and advanced topics in algorithms. His publications reflect a strong emphasis on algorithmic efficiency, scalability, and theoretical foundations in combinatorial optimization.

Notable contributions include advancements in approximation algorithms for metric TSP, submodular optimization, and dynamic graph algorithms. His collaborations with researchers like Chandra Chekuri have led to breakthroughs in areas such as matroid sparsification and densest subgraph problems. While no formal awards are listed, his work is widely cited in top venues like FOCS, SODA, and STOC.

His research interests and publications highlight a commitment to advancing fundamental algorithmic techniques with applications in graph theory, optimization, and theoretical computer science.