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Talk Title: Machine Learning for Networking
Abstract: There’s never been a more exciting time to work in networking and networked applications. This talk will examine how machine learning (ML) benefits networking by focusing on four examples. First, we’ll examine for Intent-Based Networking (a modern architecture for designing and operating a network) and how ML can be used to increase visibility, diagnose problems and identify associated remedies, and provide assurance that the network is operating as intended. Next, we’ll look at how the move from today’s Cloud-based ML to the promising approach of Distributed ML across Edge and Cloud can lead to improved scalability, reduced latency, and improved privacy. We’ll also discuss how to identify what devices are on the network and how the network should treat those devices. Lastly, in the context of every growing security threats, we examine how ML can be applied to address the challenge of malware sneaking in an encrypted flow. Specifically, how we can detect malware hidden in encrypted flows without requiring decryption of those flows. It is noteworthy that while ML is often associated with reducing privacy, this example showcases how an elegant application of ML can both preserve privacy and reduce complexity.
Bio: John is VP/CTO of Cisco's Enterprise Networking Business (Cisco's largest business) where he drives the technology and architectural direction in strategic areas for the business. This covers the broad Cisco portfolio including Intent-Based Networking (IBN), Internet of Things (IoT), wireless (ranging from Wi-Fi to emerging 5G), application-aware networking, multimedia networking, indoor-location-based services, connected vehicles, machine learning and AI applied to the aforementioned areas, and deep learning for visual analytics.
Previously, John was Lab Director for the Mobile & Immersive Experience Lab at HP Labs. The MIX Lab conducted research on novel mobile devices and sensing, mobile client/cloud multimedia computing, immersive environments, video & audio signal processing, computer vision & graphics, multimedia networking, glasses-free 3D, next-generation plastic displays, wireless, and user experience design.
John received a number of honors and awards including IEEE Fellow, IEEE SPS Distinguished Lecturer, named “one of the world’s top 100 young (under 35) innovators in science and technology” (TR100) by MIT Technology Review, received a Certificate of Honor for contributing to the US Digital TV Standard (Engineering Emmy Award 1997) and his work on media transcoding in the middle of a network while preserving end-to-end security (secure transcoding) was adopted in the JPSEC standard. He has published over 100 papers, including receiving 5 best paper awards, and has about 75 granted US patents. John also has strong collaborations with the academic community and was a Consulting Associate Professor of EE at Stanford (2000-09), and frequently lectures at MIT. He received his B.S., M.S., and Ph.D. in EECS from MIT.