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" alt="Stephen Strowes" />

Stephen is a Principal Engineer in the Office of the CTO at Fastly, where he works on Internet measurements and analysis of measurement data. He is active in related research venues and regularly serves on program or review committees, most recently PAM, TMA, ANRW and the annual IRTF Applied Networking Research Prize (ANRP).

Prior to Fastly, he worked in the R&D team at the RIPE NCC, where he focused on projects based on RIS, RIPE Atlas and other data sources. Before that, he worked on the widespread deployment and measurement of IPv6 at Yahoo. He received his PhD in Internet routing scalability from the University of Glasgow in 2012.