Abstract: In this talk, we will focus on the broader angle of applying machine learning to different aspects of transportation - ranging from traffic congestion, real-time speed estimation, image based localization, and active map making as examples. In particular, as we grow the portfolio of models, we see an unique opportunity in building out a unified framework with a number of micro-perception services for intelligent transport which allows for portability and optimization across multiple transport use cases. We also discuss implications for existing ride-sharing transport as well as potential impact to autonomous.

Bio: Dr. Ramesh Sarukkai currently heads up the Geo teams (Mapping, Localization & Perception) at Lyft. Prior to that he was a Director of Engineering at Facebook and Google/YouTube where he led a number of platform & products initiatives including applied machine learning teams, consumer/advertising video products and core payments/risk/developer platforms. He has given a number of talks/keynotes/panelist at major conferences/workshops such as W3C WWW Conferences, ACM Multimedia, and published/presented papers at leading journals/conferences on internet technologies, speech/audio, computer vision and machine learning, in addition to authoring a book on “Foundations of Web Technology” (Kluwer/Springer). He also holds a large number of patents in the aforementioned areas and graduated with a PhD in computer science from the University of Rochester.