Laxman Dhulipala

Laxman Dhulipala is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a research scientist at Google Research in the Graph Mining team. He holds a Ph.D. from Carnegie Mellon University, advised by Guy Blelloch, and was a postdoctoral researcher at MIT with Julian Shun.

• Ph.D., Carnegie Mellon University
• Postdoctoral Research, MIT

His research focuses on efficient parallel algorithms, particularly for graph processing and clustering. He explores theoretical and practical models of parallel computation aligned with modern hardware. His work spans parallel graph algorithms, computational geometry, and scalable systems for massive datasets.

The recent publications demonstrate a strong trend in scalable and dynamic graph algorithms, with a focus on hierarchical clustering, connectivity, and benchmarking. Key themes include batch-dynamic updates, memory-efficient data structures, and practical parallel implementations for massive-scale problems. Many works appear in top venues such as SPAA, VLDB, NeurIPS, and SIGMOD.

• Best Paper Award at VLDB'25
• Best Paper Award at SPAA'22
• Best Paper Runner Up at VLDB'22
• Distinguished Paper Award at PLDI'19
• Best Paper Award at SPAA'18
• Memorable Paper Award Finalist at NVMW'20
• Honorable Mention, CMU SCS Dissertation Award
• Nominated for ACM Dissertation Award

Dhulipala has advised and collaborated with numerous students and researchers, including Quinten De Man, Shangdi Yu, Jessica Shi, and others, contributing to influential projects such as Aspen, ParGeo, GBBS, and ParlayLib. He has received recognition for both theoretical and practical contributions to parallel computing. He teaches courses such as CMSC858N (Scalable Parallel Algorithms and Data Structures) and CMSC451 (Design and Analysis of Computer Algorithms).

He is actively involved in building tools and frameworks for parallel algorithm development and evaluation, including benchmark suites and graph processing systems. His dual affiliation with academia and Google Research enables impactful, scalable research bridging theory and practice.