Fellow & Tutor in Engineering; Associate Professor of Engineering Science
Professor Noa Zilberman is a network-hardware researcher, focusing on the integration of micro-level architectures and macro level, large scale networked-systems.
Before joining Oxford, she was a Leverhulme Early Career Fellow and an Affiliated Lecturer at the University of Cambridge. Prior to that, she spent close to 15 years in industry, last as a Chip Architect and Engineering Manager at Broadcom.
Prof Noa Zilberman leads the Computing Infrastructure Group at the Department of Engineering Science.
Her research focuses on the integration of micro-level architectures and macro level, large scale networked-systems. Such research requires a breadth of knowledge and expertise, building upon Zilberman’s rich experience. Her research interests range from computer architecture, programmable hardware and networking to data science, with a specific interest in the combination of multiple disciplines (and a touch of measurements). Current research buzzwords include sustainable computing infrastructure, data systems, networked-systems architectures, rackscale computing, in-network computing and in-network machine learning, converged interconnects, memories architecture and performance, performance measurements, and others.
Before joining Oxford, Prof Zilberman was a Fellow and an Affiliated Lecturer at the University of Cambridge’ Department of Computer Science and Technology, where she was the PI on multiple projects and the Chief Architect of the NetFPGA project.
Prof Zilberman has over 15 years of industrial experience. In her last role before moving to academia, she was a Senior Principal chip architect in Broadcom’s Network Switching group.
Finding Hard-to-Find Data Plane Bugs with a PTA
Bressana P, Zilberman N & Soule R (2020)
An artifact evaluation of NDP
Zilberman N (2020), Computer Communication Review, 50(2)
Thoughts about artifact badging
Zilberman N & Moore AW (2020), Computer Communication Review, 50(2), 60-63
P4xos: consensus as a network service
Dang HT et al. (2020), IEEE ACM Transactions on Networking
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Krueger G et al. (2020), arXiv