Predictions in Excel through Estimating Missing Values

Abstract: In this workshop, we introduce a new data analysis tool that enables predictions in Excel-like environment **without** any prior knowledge of Machine Learning, Statistics or Data Science. This, seemingly magical, ability is direct consequence of viewing the question of prediction as estimating missing values or correcting errors within observations. More precisely, this boils down to estimating a structured "tensor" from its noisy, missing observations. We will show an intuitive, simple and scalable approach for estimating tensor as well as provide a collection of case-studies using an actual tool.

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.