Matching Algorithms in E-Commerce with Pan Xu, University of Maryland

February 28, 2019


This talk is open to MIE faculty and graduate students. Registration is not required.

Matching is a fundamental model in combinatorial optimization. During the last decade, stochastic versions of matching models have seen broad applications in various matching markets emerging in E-Commerce. In this talk, Pan Xu will first present several basic online matching models and related fundamental algorithmic frameworks, which are primarily motivated by the Internet advertising business. Then Dr. Xu will survey new challenges and our corresponding algorithmic solutions when we apply matching models to different real matching markets, including crowdsourcing marketplaces (e.g., Amazon Mechanical Turk), ridesharing platforms (e.g., Uber and Lyft), online food-ordering platforms (e.g., Grubhub), and online recommendation systems (e.g., Amazon recommendations).

Pan Xu is currently a Ph.D. student in the Department of Computer Science at the University of Maryland (UMD), College Park. He is very fortunate to be supervised by Dr. John Dickerson and Dr. Aravind Srinivasan. Before joining UMD in 2013, he worked closely with Dr. Lizhi Wang and Dr. Srikanta Tirthapura at Iowa State University (ISU) from 2009 to 2012.

Pan’s research interests broadly span the intersection of Algorithms, Operations Research, and Artificial Intelligence. Recently, he focuses on the design of efficient algorithms for offline and online matching models and their applications in various real matching markets, including crowdsourcing marketplaces, ridesharing platforms, and online recommendation systems. He has been fortunate to be supported by several Fellowships and Awards including an F. Wendell Miller Fellowship (2009-2012, ISU), a Research Excellence Award (2013, ISU), an Ann G. Wylie Dissertation Fellowship (2018-2019, UMD) and an Outstanding Graduate Assistant Award (2018, UMD). He is the single nominee by the CS Department at UMD for the CMNS Board of Visitors Outstanding Graduate Student Award, 2018.