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Or Cohen, Lyft
Machine Learning for Spatio-Temporal Problems
May 3, 2021, 5pm:
Join Zoom Meeting
Machine learning is used widely at Lyft. A few examples include predicting where and when ride requests will happen, predicting travel time between two locations or predicting the probability for a passenger to cancel his/her ride. Such applications of machine learning are one of the key factors that differentiate Lyft from traditional taxi companies. In this talk, I will present a few of these use-cases in detail. I will describe the unique challenges that come when applying machine learning for such spatio-temporal problems, in which the main features are location (e.g. where the passenger is) and time (e.g. when the ride is requested). I will describe the different techniques for discretizing these variables, which models work best for such problems and what challenges still remain.
Or Cohen is Staff Data Scientist at Lyft focusing on ETA (Expected Time of Arrival). He has a PhD in statistical physics.