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Keynote speakers

Victor DeMiguel
London Business School
Victor DeMiguel is Professor of Management Science and Operations at London Business School, where he has been a full-time faculty member since 2001. Victor’s research focuses on the application of optimization models for managerial decision making. He has applied optimization techniques to financial portfolio selection and supply chain modelling. His papers have been published in journals such as Management Science, Operations Research, Mathematics of Operations Research, and SIAM Journal on Optimization. One of his most popular papers is "Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy", which received the Best Paper Award from the Institute for Quantitative Investment Research and was published in The Review of Financial Studies.

Data Driven Investment Management
The Nobel laureate Harry Markowitz showed that an investor who cares only about the mean and variance of portfolio returns should hold a portfolio on the efficient frontier. To compute these portfolios, one needs to solve an optimization problem whose coefficients depend on the mean and the covariance matrix of asset returns. In practice, one needs to replace these quantities by their sample estimates, but due to estimation error the resulting portfolios typically perform poorly out of sample; a phenomenon known as the “error-maximization” property of the portfolio optimization problem. In this talk, we discuss several approaches proposed in the recent literature to overcome these difficulties, including robust optimization and estimation, shrinkage estimation, Bayesian estimation, and norm constraints. Finally, we highlight open issues that offer opportunities for researchers in Management Science to contribute to this area.

William Pulleyblank
United States Military Academy, West Point
William R. Pulleyblank is a Professor of Operations Research in the Department of Mathematical Sciences at The United States Military Academy, West Point. He joined the faculty in 2010, continuing a career that spanned both academia and business.
His previous position was IBM Vice President, responsible for launching the Business Analytics and Optimization function within IBM Global Services. This group develops and deploys high powered optimization and analytic capabilities to improve the business performance of a broad range of companies.
Prior to this, he was the Director of Exploratory Server Systems and Director of the Deep Computing Institute within IBM Research. The research teams he led provided broad-based support to IBM’s server activities as well as leading research in high performance computing. This included the Blue Gene project, which led to the creation of the Blue Gene/L supercomputer which was certified as the most powerful supercomputer in the world in 2004 and maintained this distinction until 2008. He also served as the Research relationship executive responsible for the Financial Services sector in IBM, the Utility and Energy Services industry.
Dr. Pulleyblank has been awarded honorary doctorates from The College of St. Rose and from McMaster University. He is a Fellow of the Fields Institute of Mathematical Sciences and member of Omega Rho, the International Honor Society of INFORMS. In 2007 he was elected a Fellow of INFORMS, the international operations research and management science society. In 2011 he was awarded the Philip McCord Morse Lectureship by INFORMS.
In 2008, the Blue Gene project which he led was awarded the National Medal of Technology and Innovation. In 2010 Dr. Pulleyblank was elected to the National Academy of Engineering.

Analytics, Sports and Force-on-Force Situations
Many challenging analytics problems arise in the sports domain. This is, in part, due to a situation where two groups compete in a zero sum situation. This is driving many of the advances in data analytics and presents real opportunities for both theoretical and applied advances. We discuss the case of NCAA Division I (American) Football and a multiyear project with the goal of helping Army defeat Navy.
We also discuss recent work with Brian Macdonald on showing how to restructure sports leagues to minimize league travel in advance of schedule creation. This gives rise to an interesting class of quadratic assignment problems which we solve using heuristics as well as Mixed Integer Programming Techniques.

Daniel Ralph
University of Cambridge, Judge Business School
Danny Ralph is Professor of Operations Research and Academic Director of the Centre for Risk Studies in Cambridge Judge Business School, and a Fellow of Churchill College. Prior to taking up a joint appointment in the Business School and Engineering Department at Cambridge University he held positions at Cornell University and then the University of Melbourne. Danny’s technical background is mathematical modelling for optimisation and equilibrium systems. He was Editor-in-Chief of Mathematical Programming (Series B) from 2007-2013. Danny engages with the broader community on risk management, especially systemic risk.

Capacity decisions in electricity production under risk aversion and risk trading
Risk neutral optimization models are standard in exploring how changes in production capacity are made over time in a stochastic economic equilibrium setting. However investors in electricity (and other) manufacturing plants are risk averse given long payback periods and risks that can’t be hedged in financial markets, e.g., regulation relating to penalising or pricing CO2 emissions. We use a two stage model – invest in plants & financial products today and produce electricity in a stochastic market tomorrow – to illustrate how risk aversion and risk markets can be adapted to economic equilibrium models for long term planning. The opposite cases of complete risk markets and no risk trading are used to give a “corridor” of outcomes. We conclude with a multistage example of this approach.

Rüdiger Schultz
Zentrum für Logistik & Verkehr, Universität Duisburg-Essen
Professor Ruediger Schultz studied mathematics at Humboldt-University Berlin. He received his Ph.D. in mathematics from this university in 1985 with a thesis on sensitivity analysis in convex programming under the supervision of Prof. Dr. Bernd Kummer, and he earned his postdoctoral lecturer qualification from the same university in 1995 with a thesis on stability in stochastic programming. From 1993 to 1997 he was with the Konrad Zuse Center for Information Technology Berlin as a research assistant. In 1997, he became associate professor in discrete mathematics at the University of Leipzig. After one year he was appointed full professor to the Chair of Discrete Mathematics and Optimization at the Gerhard-Mercator University Duisburg, today University of Duisburg-Essen.

From One to Infinity - Dimensions of Stochastic Programming
Recent and not so recent developments in stochastic programming are put into perspective. Dimensions serve as guidelines for the presentation starting out from model classics, (re)visiting optimization under uncertainty in different branches of mathematical programming, and finally arriving at Infinity. Along the way, various models, algorithms, numerical tests, and practical applications are met.