Anant received his PhD from the Colorado State University in 2018. His interests include large scale Internet measurements, routing analysis, BGP, and outage detection. At VDMS, his current focus is on measuring CDN connectivity, TCP and traffic optimizations.
Paul received his PhD in Computer Science from University of California Irvine. His interests include leveraging cloud computing for Internet-scale network performance monitoring, development of scalable analytics pipelines, CDN storage optimizations, and applied machine learning in security.
Marcel was originally an intern in the summer of 2014 and joined the team full-time after completing his Ph.D. at Northwestern University, with a focus on enabling additional channels of communication in existing networks and improving network performance. At VDMS he has further explored transport layer optimizations, large-scale traffic management strategies, and cache optimizations.
Bedi joined the team in 2013 as an Intern when it was still part of EdgeCast. He received his Ph.D. from the University of Memphis where he focused on mitigating throughput-based denial of service attacks using techniques in applied game theory and active queue management. His interests include exploring traffic profiles, evaluating web-caching techniques, and investigating Video QoE. Bedi currently leads the Research team.
Derek Shiell leads the Edge Innovation function at Verizon Digital Media Services. This division focuses on new and emerging areas around IoT, Multi-Access Edge Computing, and Edge/Serverless Compute. Derek has been with Verizon Digital Media Services and Edgecast for the past 8 years. Prior to leading the Edge Innovation function, Derek was the Director of Engineering for the Edgecast CDN. Derek has a Masters of Science in Electrical Engineering from Northwestern University where he focused on Audio-visual speech recognition and biometrics, and face alignment/tracking.
Dave 'Bear' Andrews is Chief Architect at Verizon Digital Media Services, overseeing the evolution of the Edgecast CDN and Uplynk video platform. He enjoys low-level security exploitation techniques and has an appreciation for the nuances and resulting surprised faces that accompany discovering failure modes in globally distributed systems. Previously, Dave brought several web security products to market at Verizon Digital Media Services and worked for startups in the Los Angeles area, building security products in the virtualization and content delivery network (CDN) spaces. He holds a PhD in computer security from a small university in Australia.
As the CDN Architect, Paulo's focus is empowering researchers to investigate CDN-specific challenges and identify opportunities for innovation. He joined in 2014 as Lead Software Engineer working on the telemetry platform of the Edgecast CDN. His technical interests are distributed systems and data engineering. Previously, he worked at SpaceX and Microsoft.
Stephen is an intern in the research team. He is studying for his Ph.D. at the University of Glasgow, where his research focuses on improving transport-layer support for low-latency applications. At VDMS, he is developing a methodology to use RIPE Atlas to provide a deeper understanding of anycast performance on the Internet.
Marc is one of our 2018 Summer interns. Currently a Ph.D. candidate at Northwestern University, his current aspirations include graduating, and, perhaps more importantly, adding his own plot to one of the NOC's big screens. Marc's research interests and expertise lie in 'one-ended' network performance measurement systems.
Lan Wei is a PhD student in USC, an intern in the summer of 2018, with a focus on latency performance of the Edgecast CDN to any possible user in the world. Her work is special for focusing more about the hops of the path between the CDN and users, possible for both troubleshooting and enhancing CDN performance.
Evita is a PhD student at UC Irvine, where her research focuses on mobile data privacy and machine learning. As a summer research intern (2018) at VDMS, she works on CDN traffic analysis and modeling request time with machine learning methods.