Regular biography
Ravi Netravali is an Associate Professor in the Department of Computer Science at Princeton University. His research interests are broadly in systems and networking, with a recent focus on improving large-scale machine learning and video platforms in terms of performance, resource efficiency, affordability, and ease of use. His work has been recognized with a Sloan Research Fellowship, the SIGCOMM Rising Star Award, an NSF CAREER Award, multiple industry faculty research awards, and an IRTF Applied Networking Research Prize.
Scholar-generated biography
Ravi Netravali is an Associate Professor of Computer Science at Princeton University, specializing in systems and networking research. His work focuses on improving the efficiency, scalability, and performance of modern computing systems, particularly in the areas of distributed deep learning, real-time video analytics, and wireless networking. He has developed frameworks for resource-efficient video processing, such as Reducto and Gemel, and has contributed to the design of systems for scalable and accurate graph learning, including P3 and Dorylus. His research also addresses challenges in network measurement and congestion control, as seen in Mahimahi and ABC. Netravali's work emphasizes practical solutions for deploying advanced technologies in real-world environments.