Phillip B. Gibbons

Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty.

His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations.

Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field.

He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory.

• IEEE Fellow (2014) - For contributions to parallel computing and databases
• ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks
• Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions)
• Co-winner of the best paper award for NSDI '06

Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems.

His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.