Gabor Szabo works on large-scale data analysis and modeling problems in social networks, self-organized online ecosystems, transportation systems, and autonomous driving. Previously, his research focus was on the description of randomly organized networks in online communities and biological systems at Harvard Medical School, the University of Notre Dame, and HP Labs. After that he built distributed algorithms to understand and predict user behavior at Twitter. He has created models for resource allocation in Lyft’s ride-sharing network, and most recently he led a team at Tesla’s Autopilot.
Gungor Polatkan is a machine learning expert and engineering leader with experience in building massive-scale distributed data pipelines serving personalized content at LinkedIn and Twitter. Most recently, he led the design and implementation of the AI backend for LinkedIn Learning and ramped the recommendation engine from scratch to hyper-personalized models learning billions of coefficients for 500M+ users. He deployed some of the first deep ranking models for search verticals at LinkedIn improving Talent Search. He has worked in several notable applied research groups in Twitter, Princeton, Google, MERL and UC Berkeley before joining LinkedIn.
Oscar Boykin works on machine learning infrastructure at Stripe, building systems to predict fraud at scale. Prior to Stripe, Oscar spent more than 4 years at Twitter, first working on modeling and prediction for ads, and later on data infrastructure systems. At Twitter, Oscar co-developed many open-source scala libraries including Scalding, Algebird, Summingbird, and Chill. Before Twitter, Oscar was an assistant professor of electrical and computer engineering at the University of Florida.
Antonios Chalkiopoulos is a fast/big data distributed system specialist with experience in delivering production-grade data pipelines in the media, IoT, retail, and finance industries. Antonios is a published author in big data, an open-source contributor, and the co-founder and CEO of Landoop LTD. Landoop LTD builds the innovative and award-winning Lenses platform for data in motion, which provides visibility and control over streaming data, data discovery via an intuitive web interface and is a comprehensive SQL experience for data in motion, monitoring, alerting, data governance, multi-tenancy, and security. Lenses is a complete user experience for building and managing real-time data pipelines and micro-services.
