2015 Informs Annual Meeting

PLENARY AND KEYNOTE PRESENTATIONS

15

All Plenary & Keynote Presentations will take place in the Convention Center.

Annals of Probability, Annals of Applied Probability, and Mathematics of Operations Research and Queueing Systems . She is a recipient of the Erlang Prize of the INFORMS Applied Probability Society (2006), was elected fellow of the Institute for Mathematics and Statistics (IMS) in 2013, and was an IMS Medallion Lecturer in 2015. KEYNOTE 3:10–4pm Grand Ballroom B, Upper 200 Level Optimization Techniques in Data Analysis Stephen J. Wright, Professor of Computer Sciences, University of Wisconsin-Madison Optimization perspectives have provided valuable insights into machine learning and data analysis problems, and optimization formulations have led to practical algorithms with good theoretical properties. In turn, the rich collection of problems arising in learning and data analysis is driving new fundamental research in optimization, reviving interest in well-established techniques and stimulating development of new methods. We discuss research on several areas of learning and data analysis, including regression/classification, signal and image reconstruction, and manifold learning, in each case describing problem areas in which optimization algorithms have been developed and successfully applied. Stephen J. Wright is a professor of applications to many areas of science and engineering. Prior to joining UW-Madison in 2001, Wright was a senior computer scientist at Argonne National Laboratory (1990–2001), and a professor of computer science at the University of Chicago (2000–2001). He has served as chair of the Mathematical Optimization Society and as a Trustee of the Society for Industrial and Applied Mathematics (SIAM). He is a Fellow of SIAM. In 2014, he won the W.R.G. Baker Award from IEEE. Wright is the author or coauthor of widely used text/reference books in optimization including Primal Dual Interior- Point Methods (SIAM 1997) and Numerical Optimization (2nd ed., Springer 2006, with J. Nocedal). He has published on optimization theory, algorithms, software, and applications. computer sciences at the University of Wisconsin-Madison. His research focuses on computational optimization and its

Wright is editor-in-chief of the SIAM Journal on Optimization and has served as editor- in-chief or associate editor of Mathematical Programming: Series A and B, SIAM Review, and Applied Mathematics and Computation . KEYNOTE 3:10–4pm 201C, 200 Level Reprise of 2015 Edelman Award-Winning Presentation Joseph Byrum, Craig Davis, Gregory Doonan, Tracy Doubler, David Foster, Bruce Luzzi, Ronald Mowers, Chris Zinselmeier Syngenta, a leading developer of crop varieties (seeds) that provide food for human and livestock consumption, is committed to bringing greater food security to an increasingly populous world by creating a transformational shift in farm productivity. Syngenta Soybean Research & Development (R&D) is leading Syngenta’s corporate plant-breeding strategy by developing and implementing a new product development model that is enabling the creation of an efficient and effective soybean breeding strategy. Key to the new strategy is the combination of advanced analytics and plant- breeding knowledge to find opportunities to increase crop productivity and optimize plant-breeding processes. Syngenta uses discrete-event and Monte Carlo simulation models to codify Syngenta Soybean R&D best practices, and uses stochastic optimization to create the best soybean breeding plans and strategically align its research efforts. As a result of using these new analytical tools, Syngenta estimates that it will save more than $287 million between 2012 and 2016. FRANZ EDELMAN AWARD The Franz Edelman Award for Achievement in Operations Research and the Management Sciences calls out, recognizes, and rewards outstanding, high impact applications of OR/MS. Each year, six finalists compete in the “Super Bowl” of O.R. in practice. The 2015 finalists include IBM, Ingram Micro, LMI/ Defense Logistics Agency, Saudi Arabia Ministry of Municipal and Rural Affairs, Sygenta, and U.S. Army. In this keynote, the first-place Syngenta will reprise their winning presentation.

MONDAY, NOVEMBER 2 PLENARY 10–10:50am Grand Ballroom B, Upper 200 Level Computational Thinking, Inferential Thinking and Big Data Michael I. Jordan, Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science, Department of Statistics, University of California, Berkeley The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. The fact that classical perspectives from these fields are not adequate to address emerging problems in “Big Data” is apparent from their sharply divergent nature at an elementary level—in computational science, the growth of the number of data points is a source of “complexity” that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of “simplicity” in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as “runtime” in core statistical theory and the lack of a role for statistical concepts such as “risk” in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, algorithm weakening as a tool for trading off the speed and accuracy of inference, and the theoretical study of lower bounds that embody computational and statistical constraints. Michael I. Jordan is the Pehong Chen Distinguished

Professor in the Department of

Electrical Engineering and Computer Science and the Department

of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language