Faculty Job Seminar by XU Yixin | Efficiently finding ride-sharing trips through geometric properties

Ride-sharing has emerged as a prevalent transportation mode to provide timely and convenient rides to passengers. It creates a win-win situation for all involved parties and brings substantial environmental benefits. A fundamental problem of ride-sharing is determining how to dispatch vehicles to passengers in a short time, which is challenging due to the large number of involved participants, highly dynamic scenarios, and expensive road network distance computation.

In this talk, the speaker will present a simple, efficient, and scalable algorithm that enables fast and high-quality dispatching in ride-sharing by solving a fundamental problem in the dispatching process, i.e., quickly finding possible vehicles for passengers. By utilizing geometric properties, the algorithm improved efficiency by orders of magnitude over the state-of-the-arts. This talk will also include a short mock teaching session on machine learning.

About the Speaker

Yixin Xu received her Ph.D. degree in computer science from the University of Melbourne in 2021. Her research focuses on spatial-temporal databases and spatial-temporal data management, aiming for real-time processing of massive and highly dynamic spatial-temporal data with cheap storage costs. She is especially interested in the on-demand ride-sharing setting and algorithms that are potential to be deployed in the real world. Her research outcomes are published in top-tier conferences in the spatial data field. She is also interested in natural language processing. After graduation, she worked as a data scientist with a special focus on sentiment analysis, sentence similarity measurement, and text summarization.

She is a lecturer-track faculty candidate.