Stanford MLSys Seminar Episode 64: Xinyu Hu and Olcay Cirit [Today, 1.35-2.30pm PT]

[Karan Goel]

Hi everyone,

We're back with the sixty-fourth episode of the MLSys Seminar today from 1.35-2.30pm PT. 

We'll be joined by Xinyu Hu and Olcay Cirit, who will talk about predicting arrival times at Uber. The format is a 30 minute talk followed by a 30 minute podcast-style discussion, where the live audience can ask questions.

Livestream Link: https://www.youtube.com/watch?v=CJTitzj0qBo

Guests: Xinyu Hu and Olcay Cirit

Title: DeepETA: How Uber Predicts Arrival Times Using Deep Learning

Abstract: Estimated Time of Arrival (ETA) plays an important role in delivery and ride-hailing platforms. For example, Uber uses ETAs to calculate fares, estimate pickup times, match riders to drivers, plan deliveries, and more. Commonly used route planning algorithms predict an ETA conditioned on the best available route, but such ETA estimates can be unreliable when the actual route taken is not known in advance. In this talk, we describe an ETA post-processing system in which a deep residual ETA network (DeepETA) refines naive ETAs produced by a route planning algorithm. Offline experiments and online tests demonstrate that post-processing by DeepETA significantly improves upon the accuracy of naive ETAs as measured by mean and median absolute error. We further show that post-processing by DeepETA attains lower error than competitive baseline regression models.

Bio: Xinyu Hu is a Senior Research Scientist at Uber, focusing on large-scale machine learning applications in spatial-temporal problems and causal inference. She currently works on projects in personalized incentives targeting, including user promotion targeting, spatial-temporal paid movement targeting, etc.. Prior to Uber, Xinyu graduated from Columbia University with a Ph.D. in Biostatistics. Olcay Cirit is a Staff Research Scientist at Uber AI focused on ML systems and large-scale deep learning problems. Prior to Uber AI, he worked on ad targeting at Google.

See you all there!

Best,

Karan