Abstract: 

In this workshop, we will provide an overview of techniques for matrix and tensor estimation. We will showcase a wide variety of applications for matrix estimation in analyzing large heterogeneous datasets that may have missing or incorrect entries, including retail, causal inference, sports and networks. These applications will form the basis for some practical demos with opportunities for hands-on experience. Subsequently we will explain the intuition for matrix and tensor estimation algorithms, with a focus on collaborative filtering.

Bio: 

Christina Lee Yu is an Assistant Professor at Cornell University in Operations Research and Information Engineering. Prior to Cornell, she was a postdoc at Microsoft Research New England. She received her PhD in 2017 and MS in 2013 in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in the Laboratory for Information and Decision Systems. She received her BS in Computer Science from California Institute of Technology in 2011. She received honorable mention for the 2018 INFORMS Dantzig Dissertation Award. Her research focuses on designing and analyzing scalable algorithms for processing social data based on principles from statistical inference.