Victor Bahl is a Distinguished Scientist and the Director of Mobility & Networking in Microsoft Research. In this role he advises Microsoft's CEO and senior leadership team on long-term vision/strategy around networked systems, mobile computing, wireless systems, cloud computing, and datacenter networking. He heads a high-powered group that executes on this vision through research, technology transfers to product groups, industry partnerships, and associated policy engagement with governments and research institutes around the world. Dr. Bahl has published over 125 scientific papers, authored over 130 issued patents, and won numerous technical and leadership awards incl. a test-of-time award, three best paper awards, two awards from the United States FCC, distinguished service and lifetime technical achievement awards from ACM , distinguished alumni award and a IEEE outstanding leadership award. Over the years he has developed seminal technologies including white space networking (2010), edge-based cloud computing (2009), mesh networking (2005), multi-radio wireless systems (2001), Wi-Fi hot-spots (2000), and indoor localization systems (1999). Under his direction his group has had game changing impact on Microsoft's cloud computing infrastructures both in their datacenter and in wide-area networking. Dr. Bahl is a Fellow of ACM, IEEE, and AAAS.
Talk Title: Distributed Video Analytics
Talk Abstract: The virtues of edge computing have been expounded in the research community but deployment across our industry has been slow. Reflecting on this, we have been working on a compelling video analytics application for edge computing Our motivation for pursuing this is based on the observation that cities worldwide have deployed millions of cameras for planning and security purposes. These cameras record images 24x7x365, mostly storing them for possible analysis at a later time. The time lag between capturing and analyzing is a limitation of current technology and cost. We believe that near real-time video analytics of live video streams is compelling for many important reasons and a perfect application of edge computing. Unfortunately existing state-of-the-art video analytics systems are costly, insufficient, and often require manual intervention. Large-scale automated video analytics is a grand challenge for the research community and for those of us who work on big data systems. Privacy regulations, bandwidth constraints and latency naturally lead us to design and develop systems where video is analyzed across both edge and cloud clusters. In this talk I will describe our hybrid edge-cloud video analytics infrastructure and a pilot system that we are building in collaboration with the City of Bellevue in Washington, USA.