Data-Driven Decision Making: Do We Know What We Know?

Abstract

From portfolio optimization to inventory management, applications of data driven decision-making abound. Do I have enough data? What if my model is wrong? Is optimized optimal? How do we make reasonably good decisions with a limited amount of data, uncertainties in modeling and finite computational resources are often the central theme of these tasks. Through a couple of prototypical examples, the speaker shall demonstrate that, to better understand the challenges and develop strategies to counter them, it is imperative to look at these problems from both statistical and optimization viewpoints.

About the Speaker

Prof. Ming YUAN is Professor of Statistics and Associate Director for Academic Affairs, Data Science Institute at Columbia University. He was previously Senior Investigator in Virology at Morgridge Institute for Research and Professor of Statistics at University of Wisconsin-Madison, and prior to that Coca-Cola Junior Professor of Industrial and Systems Engineering at Georgia Institute of Technology. His research and teaching interests lie broadly in statistics and its interface with other quantitative and computational fields such as optimization, machine learning, computational biology and financial engineering. Prof. Yuan has over 100 scientific publications in applied mathematics, computer science, electrical engineering, financial econometrics, medical informations, optimization, and statistics among others.

Prof. Yuan has served as the program secretary of the Institute for Mathematical Statistics (IMS), and was a member of the advisory board for the Quality, Statistics and Reliability (QSR) section of the Institute for Operations Research and the Management Sciences (INFORMS). He is also a co-Editor of The Annals of Statistics and has been serving on numerous editorial boards. He was named a Medallion Lecturer of IMS in 2018, and a recipient of the John van Ryzin Award (2004; International Biometrics Society), CAREER Award (2009; US National Science Foundation), the Guy Medal in Bronze (2014; Royal Statistical Society), and the Leo Breiman Junior Researcher Award (2017; the Statistical Learning and Data Mining section of the American Statistical Association).

For Attendees' Attention

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