All attendees receive free access to the INFORMS 2019 TutORials in Operations Research online content concurrently with the meeting. Registrants of the 2019 INFORMS Annual Meeting have online access to the 2019 chapters, written by select presenters, beginning on October 19, 2019. Access this content using the link provided to all attendees by email or, if you are a 2019 member, simply login to INFORMS PubsOnLine.
The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new developments in the field, and serves as augmenting material for a selection of the tutorial presentations offered at the INFORMS Annual Meetings.
Omni Channel Fulfillment Optimization
Author: Vivek Farias, MIT
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Authors: Daniel Kuhn, EPFL; Peyman Mohajerin Esfahani, Viet Anh Nguyen and Soroosh Shafieezadeh Abadeh
Daniel Kuhn holds the Chair of Risk Analytics and Optimization at EPFL. Before joining EPFL, he was a faculty member at Imperial College London (2007-2013) and a postdoctoral researcher at Stanford University (2005-2006). He received a PhD in Economics from the University of St. Gallen in 2004 and an MSc in Theoretical Physics from ETH Zurich in 1999. His research interests revolve around robust optimization and stochastic programming.
Computer Vision and Deep Learning
Authors: Param Singh, CMU; Nikhil Malik
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis
Authors: Cynthia Rudin, Duke; David Carlson
Cynthia Rudin is an associate professor of computer science, electrical and computer engineering, and statistics at Duke University, and directs the Prediction Analysis Lab, whose main focus is in interpretable machine learning. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD in applied and computational mathematics from Princeton University. She is the recipient of the 2013 and 2016 INFORMS Innovative Applications in Analytics Awards, an NSF CAREER award, was named as one of the “Top 40 Under 40” by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. Work from her lab has won 10 awards in the last 5 years. She is past chair of both the INFORMS Data Mining Section and the Statistical Learning and Data Science section of the American Statistical Association. She has served on committees for DARPA, the National Institute of Justice, and AAAI. She has served on three committees for the National Academy of Sciences, including the Committee on Applied and Theoretical Statistics, the Committee on Law and Justice, and the Committee on Analytic Research Foundations for the Next-Generation Electric Grid.David Carlson is an Assistant Professor in the Department of Civil and Environmental Engineering and the Department of Biostatistics and Bioinformatics. He is also a member of the Duke Clinical Research Institute. He previously completed postdoctoral training at Columbia University and received his Ph.D in Electrical and Computer Engineering from Duke University. His research is focused in machine learning and data-driven science; in particular how machine learning and statistical techniques can be used not only for the analysis of large data sets, but integrated into the design of novel experiments to elucidate scientific understanding. He has developed algorithms and analysis methods for diverse engineering and health applications, with a special focus in neuroscience and psychiatric disorders.
Authors: Retsef Levi, MIT; Kyan Safavi, Martin Copenhaver
Retsef Levi is the J. Spencer Standish (1945) Professor of Operations Management at the MIT Sloan School of Management, member of the Operations Management Group at MIT Sloan, faculty co-director of the MIT Leaders for Global Operations Program, and co-director of the Sloan Health System Innovation Initiative. Levi’s current research is focused on the design of analytical data-driven decision support models and tools addressing complex business and system design decisions under uncertainty in areas such as health and health care management, supply chain, food safety and security, risk, management, procurement and inventory management, revenue management, pricing optimization, and logistics. Levi teaches courses in operations management, analytics, risk management, system thinking, and health care. He received the NSF Faculty Early Career Development award, 2008 INFORMS Optimization Prize for Young Researchers, 2013 Daniel H. Wagner Prize, and the 2016 Harold W. Kuhn Award. Levi earned a bachelor’s degree in mathematics from Tel Aviv University, and a PhD in operations research from Cornell University.
Kyan is an Instructor in Anesthesia at Harvard Medical School in the Massachusetts General Hospital and the David F Torchiana Fellow in Healthcare Policy and Management for the Mass General Physicians Organization. He is co-Founder of Position Health, a digital healthcare startup. Kyan received his MD and MBA degrees from Yale and completed his residency and ICU fellowship at MGH.
Martin Copenhaver is an operations research scientist at Massachusetts General Hospital and a lecturer in operations research and statistics at MIT Sloan School of Management. His research and teaching interests lie in the intersection of optimization and statistics, especially with applications in healthcare operations management. Martin completed his Ph.D. in Operations Research at MIT’s Operations Research Center (advised by Dimitris Bertsimas) and B.S. in Applied Math at Georgia Tech.
Reinforcement Learning for Sequential Decision Making
Authors: Shipra Agrawal, Columbia; Douglas Shier
Shipra Agrawal is an Assistant Professor in the Department of Industrial Engineering and Operations Research, and Data Science Institute, at Columbia University. Her research spans several areas of optimization and machine learning, including optimization under uncertainty, multi-armed bandits, online learning, and reinforcement learning. She is also interested in prediction markets and game theory. Application areas of her interests include internet advertising, recommendation systems, revenue management, and resource allocation. She serves as an associate editor for Management Science (Optimization area) and Mathematics of Operations Research (Learning theory area) journals, and as a member of ACM future of computing academy. She is the recipient of Google Faculty research award 2017, and Amazon research award 2017.
Scheduling an Overloaded Multiclass Many-Server Queue with Impatient Customers
Authors: Amy Ward, Chicago; Amber Puha
Amy R. Ward is Professor of Operations Management and William S. Fishman Faculty Scholar at the University of Chicago Booth School of Business. She received her Ph.D. from Stanford University in 2001. She recently completed her term as chair of the INFORMS Applied Probability Society (term 11/2016-11/2018). She is the Service Management Special Interest Group Chair for the INFORMS Manufacturing and Service Operations Management Society (term 6/2017-6/2019). She is the Stochastic Models co-Area Editor for the journal Operations Research.
Data-driven Methods for MDPs with Parametric Uncertainty
Authors: Shie Mannor, Technion; Huan Xu
The Use of Data Science for Supply Chain Transportation Management
Author: Sam Eldersveld, Amazon.com
Authors: Bradley Staats, UNC; Maria Ibanez
Bradley R. Staats is the author of Never Stop Learning: Stay Relevant, Reinvent Yourself, and Thrive and is a professor at the University of North Carolina Kenan-Flagler Business School. He works with companies around the world to develop their learning and analytics strategies. He advises individuals and organizations on how to learn and improve in order to stay relevant, innovate, and succeed on an ongoing basis. And he leads UNC’s Business of Health Care Initiative, a cross-campus, interdisciplinary effort to tackle the most pressing challenges in health care.
His teaching and research focus on how to improve individual learning and design organizations that create successful learning environments. He also incorporates analytics in this process so that data can drive decision-making. Staats investigates the understudied role of human behavior in learning and operational improvement. He integrates work in operations management and behavioral science to understand how and under what conditions individuals, teams, and organizations can perform their best. He conducts field-based research in such settings as health care and software services, consulting, call centers, and retail. He also uses archival data and field experiments to provide an interdisciplinary perspective to improve both theory and practice.
Staats publishes frequently in and serves on the editorial boards of numerous leading academic journals, and his work has been featured in a variety of media outlets, including Fast Company, The New Yorker, Financial Times, The Wall Street Journal, and NPR. He has won numerous teaching and research awards, including the Wickham Skinner Early-Career Research Accomplishments Award from the Production and Operations Management Society, the Poets & Quants award as one of the 40 most outstanding business-school professors under 40 in the world, and the Warren Bennis Prize for best article in Harvard Business Review on leadership.
From Harvard Business School, Staats received his Doctorate of Business Administration in technology and operations management and Master of Business Administration. He received his Bachelor of Science with honors in electrical engineering and his Bachelor of Arts with high honors in Plan II and Spanish from The University of Texas at Austin, where he was named the Most Outstanding Male Graduate of his graduating class. Prior to his academic career, he worked as a venture capitalist at a leading firm in the southeastern United States. He also worked in investment banking at Goldman Sachs and strategic planning at Dell Corporation.
Structural Econometric Models
Authors: Yong Tan, UW; Yan Huang, Param Vir Singh