Phil Gibbons

Phil Gibbons is a Professor in the Electrical & Computer Engineering and Computer Science Departments at Carnegie Mellon University. He holds a Ph.D. from UC Berkeley (1989) and has extensive industry experience at AT&T Bell Labs, Lucent Bell Labs, and Intel Research. His research focuses on parallel computing, distributed systems, databases, and machine learning, with a emphasis on algorithmic and systems-level innovations. He has led major initiatives like the Intel Science and Technology Center for Cloud Computing and contributed to projects such as IrisNet (a planetary-scale sensor network).

Education:

• Ph.D. in Computer Science, University of California at Berkeley (1989)

Research Interests:
Gibbons' work spans big data analytics, high-performance computing, and cloud systems. He develops scalable algorithms and systems for emerging memory technologies, distributed ML, and robotics. Notable contributions include processing-in-memory (PIM) optimizations, pipeline parallelism for DNN training, and system architectures for robotic processors.

Awards:

• IEEE Fellow (2014)
• ACM Fellow (2006)
• ACM Paris Kanellakis Theory and Practice Award (2019)
• Best Paper Award at NSDI 2006

Grants & Leadership:

• Co-PI of the $15M Intel STC for Cloud Computing (2011-2015)
• Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018)
• Leadership roles in conferences like SPAA, EuroSys, and MLSys

Teams & Labs: Active in robotics computing (RobotPerf benchmark), distributed ML systems, and hardware-software co-design initiatives.