Sep 23–26, 2019
Andrew Leamon

Andrew Leamon
Director - Engineering Analysis, Comcast

Website

Drew Leamon started his career at Microsoft while studying Computer Science at Princeton University. In his studies, he delved into Computer Graphics, Artificial Intelligence and Computational Neurobiology. At Microsoft, he collaborated with Microsoft Research on one of the first commercial implementations of collaborative filtering for e-commerce. This was released as Microsoft Site Server: Commerce Edition.

Graduating into the DotCom boom, Drew caught the entrepreneurial spirit of the time and went on to sell cars on the internet through CarOrder.com, a Trilogy Software spin-off. While there, he created new ways to sell content online through innovative configuration solutions. Next Drew became one of the charter members of AirClic where he helped to create a platform to support workforce automation using wireless technologies. Drew’s work and IP in this space became core to the company’s business value. At Traffic.com / Navteq / Nokia, Drew pioneered the visualization of traffic data collected from highway sensors and digital probe devices.

Moving on to Comcast, Drew has taken his diverse experience and background and now leads part of the Engineering Analysis organization. His team is developing advanced data visualizations for network data. They are building elastically scaling Big Data infrastructure to support Analytic workloads. Simulations of Comcast’s CDNs and platforms, developed by Drew’s team, are leveraging this platform and guiding the business and engineering teams. His team is identifying high ROI opportunities. They apply machine learning to datasets and are currently operationalizing the resulting predictive models to help improve customer experience.

Sessions

2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 12
Andrew Leamon (Comcast), Wadkar Sameer (Comcast NBCUniversal)
And overview of the Data Management and privacy challenges around automating ML model (re)deployments and stream based inferencing at scale. Read more.

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