Anwar Walid, an adjunct professor of electrical engineering at Columbia Engineering and a trailblazer in network systems and machine learning, has been named a 2024 Association of Computing Machinery (ACM) Fellow. The prestigious distinction recognizes Walid’s transformative work in the theory and practical deployment of multipath congestion control, an innovation that has significantly advanced the field of computing and finds applications in the Internet, wireless, and data centers.
The ACM Fellows program honors exceptional members whose achievements have driven major advancements in computing technologies. This year, 55 Fellows from leading institutions around the globe were inducted, highlighting the program's commitment to celebrating innovation across diverse technical domains.
Walid’s contributions extend beyond academia. A prolific researcher and practitioner, he teaches advanced courses at Columbia Engineering on machine learning for networks and systems and massive content management and delivery. His influence spans many years of impactful work in academia, and in industry where he has held senior leadership roles.
Walid’s accolades underscore his profound impact on computing and networking. These include the 2022 IEEE INFOCOM Test of Time Paper Award, the 2019 ACM SIGCOMM Networking Systems Award, and the 2017 IEEE Communications Society William R. Bennett Prize, among others.
The ACM Fellows program reflects the global nature of innovation, with 2024 honorees representing universities, corporations, and research centers across 14 countries. The Fellows’ collective expertise spans cutting-edge fields, from artificial intelligence and machine learning to cybersecurity and human-computer interaction.
For more information about the 2024 ACM Fellows and their achievements, visit the ACM Fellows website.
About Anwar Walid
Dr. Anwar Walid earned his Ph.D. from Columbia University and has been an adjunct professor since 2009. He is an IEEE Fellow and serves as a senior editor for the IEEE Journal on Selected Areas in Communications. His research interests include machine learning for network systems, content delivery, and advancing the theory and practice of computing technologies.