Seminar: Machine Learning for Spatio-Temporal Problems

Or Cohen, Lyft

Machine Learning for Spatio-Temporal Problems

May 3, 2021, 5pm:

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https://ucsd.zoom.us/j/95036249217?pwd=a2kwSFg2ZXMvc21yMHZLRVR2c0pqUT09 

Abstract:

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.

Bio:

Or Cohen is Staff Data Scientist at Lyft focusing on ETA (Expected Time of Arrival). He has a PhD in statistical physics.

https://www.deeplearning.ai/blog/working-ai-at-the-office-with-research-scientist-or-cohen/