Contributing Authors
Jose Blanchet (“Statistical Analysis of Wasserstein Distributionally Robust Estimators”) is a full professor in the Department of Management Science and Engineering at Stanford University (MS&E). Prior to joining MS&E, he was a faculty member at Columbia University (Department of Industrial Engineering and Operations Research and Department of Statistics) and at Harvard University (Department of Statistics). A recipient of the 2009 Best Publication Award given by the INFORMS Applied Probability Society and of the 2010 Erlang Prize, he also received a Presidential Early Career Award for Scientists and Engineers Award given by the National Science Foundation in 2010. He has research interests in applied probability, Monte Carlo methods, and stochastic optimization. He serves on the editorial boards of Mathematics of Operations Research, Stochastic Systems, Insurance: Mathematics and Economics, and Extremes.
Elizabeth L. Bouzarth (“Storytelling with Sports Analytics”) is an associate professor of mathematics at Furman University. She earned her PhD in mathematics from the University of North Carolina at Chapel Hill in 2008. As an applied mathematician, she has many research interests ranging from sports analytics to computational fluid dynamics with applications to biology, with projects often involving undergraduate collaborators. She has collaborated with ESPN and The Athletic on various sports analytics projects and has co-organized numerous iterations of the Carolinas Sports Analytics Meeting. She is currently serving as past chair of the Mathematics and Sports Special Interest Group of the Mathematical Association of America.
Kalyanmoy Deb (“Evolutionary Computation: An Emerging Framework for Practical Single and Multicriterion Optimization and Decision Making”) is University Distinguished Professor and Koenig Endowed Chair Professor in the Department of Electrical and Computer Engineering, Michigan State University. His research interests are in evolutionary optimization and its application in multicriterion optimization, modeling, and machine learning. He received the Institute of Electrical and Electronics Engineers (IEEE) Evolutionary Computation Pioneer Award, a lifetime achievement award from Clarivate Analytics, the Infosys Prize, the TWAS Prize in Engineering Sciences, the CajAstur Mamdani Prize, the Distinguished Alumni Award from the Indian Institute of Technology Kharagpur, the Edgeworth-Pareto Award, the Shanti Swarup Bhatnagar Prize in Engineering Sciences, the Friedrich Wilhelm Bessel Research Award from Germany, and an honorary doctorate degree from the University of Jyväskylä, Finland. A fellow of IEEE and the American Society of Mechanical Engineers, he has published over 570 research papers, with Google Scholar citations of over 160,000 and an h-index of 124. More information about his research contributions can be found at https://www.egr.msu.edu/∼kdeb/.
Archis Ghate (“Response-Guided Dosing in Cancer Radiotherapy”) is a professor and associate chair in the Department of Industrial & Systems Engineering at the University of Washington in Seattle, where he holds the College of Engineering Endowed Professorship in Healthcare Operations Research. He joined the University of Washington as an assistant professor in 2006 after receiving a PhD in industrial and operations engineering from the University of Michigan in 2006 and an MS in management science and engineering from Stanford University in 2003. He completed his undergraduate education at the Indian Institute of Technology, Bombay, India, in 2001. A recipient of the National Science Foundation CAREER Award and of the Excellence in Teaching Operations Research Award from the Institute of Industrial Engineers, he has served on the editorial boards of several journals, as the general chair of the INFORMS 2019 Annual Meeting, and as a program co-chair of the 2021 IISE Annual Conference.
Benjamin C. Grannan (“Storytelling with Sports Analytics”) is the Robert E. Hughes Assistant Professor of Business and Accounting at Furman University. Ben earned his PhD from Virginia Commonwealth University in 2014 and has published research with military, healthcare, and sports analytics applications. Additionally, Ben serves the SpORts Section of INFORMS as the vice chairperson for programs and is the SpORts cluster chair for the 2021 INFORMS Annual Meeting.
John M. Harris (“Storytelling with Sports Analytics”) is a professor of mathematics at Furman University. His research interests are in the areas of sports analytics, recreational mathematics, and graph theory. Recent projects have included analyses of board games and card games, work on baseball and soccer analytics, and studies of mathematical magic tricks. He received his PhD in mathematics from Emory University.
L. Jeff Hong (“Surrogate-Based Simulation Optimization”) is the Fudan Distinguished Professor and Hongyi Chair Professor at Fudan University in Shanghai, China, with joint appointment with the School of Management and School of Data Science. His research interests include stochastic simulation, stochastic optimization, financial risk management, and supply chain management. He is currently associate editor-in-chief for the Journal of Operations Research Society of China, simulation area editor for Operations Research, associate editor for Management Science and ACM Transactions on Modeling and Computer Simulation, and president of the INFORMS Simulation Society.
Kevin R. Hutson (“Storytelling with Sports Analytics”) is currently a professor of mathematics at Furman University. His research focuses on network optimization and sports analytics. He received his PhD in mathematical sciences from Clemson University. He is one of the co-organizers of the Carolina Sports Analytics Meeting, a conference designed to help undergraduate and graduate students showcase their work in the area of sports analytics. Over the past decade, he has worked with colleagues at ESPN on the Giant Killers project, trying to predict major upsets in National Collegiate Athletic Association (NCAA) college basketball tournaments. Since 2013, he has also consulted with the NCAA to rank Football Bowl Subdivision College Football teams in order to help the NCAA Selection Committee seed their 24-team end-of-year playoffs.
Peter J. Keating (“Storytelling with Sports Analytics”) is a journalist whose award-winning Numbers column covered the world of analytics for ESPN from 2011 to 2019. He was the coauthor of BracketBreakers at The Athletic in 2021 and Giant Killers at ESPN from 2009 to 2017; both projects used a variety of statistical techniques to successfully predict National Collegiate Athletic Association tournament upsets. He also created ESPN’s Ultimate Standings, which from 2003 to 2016 rated pro teams according to how much they give back to fans. He is a founding member of ESPN’s Investigative Unit, and his pioneering work on concussions in sports exposed how the National Football League deals with brain injuries. He is the author of Dingers! A Short History of the Long Ball, a history of the home run.
Fatma Kılınç-Karzan (“Exactness in Semidefinite Program Relaxations of Quadratically Constrained Quadratic Programs: Theory and Applications”) is an associate professor of operations research at the Tepper School of Business, Carnegie Mellon University. She holds a courtesy appointment at the Department of Computer Science as well. Her research interests are on foundational theory and algorithms for convex optimization and structured nonconvex optimization and their applications in optimization under uncertainty, machine learning, and business analytics. Her work was the recipient of several best paper awards, including the 2015 INFORMS Optimization Society Prize for Young Researchers and the 2014 INFORMS JFIG Best Paper Award. Her research has been supported by generous grants from the National Science Foundation (NSF) and the Office of Naval Research, including an NSF CAREER Award. She is an elected member of the board of directors of the INFORMS Computing Society (2021–2023). She serves on the editorial board of the MOS/SIAM Optimization Classics Book Series and as an associate editor for Operations Research, the INFORMS Journal on Computing, and Optimization Methods and Software.
Karthyek Murthy (“Statistical Analysis of Wasserstein Distributionally Robust Estimators”) serves as an assistant professor in the Department of Engineering Systems and Design at Singapore University of Technology and Design. He previously completed postdoctoral training at Columbia University and received a PhD from Tata Institute of Fundamental Research in Mumbai. His research interests broadly lie in applied probability and optimization under uncertainty. His recent research focuses on models and methods for effectively incorporating robustness and tail risk considerations in large-scale decision problems affected by uncertainty. He serves as an associate editor for Stochastic Systems.
Anna Nagurney (“Game Theory and the COVID-19 Pandemic”) is the Eugene M. Isenberg Chair in Integrative Studies in the Operations and Information Management Department of the Isenberg School of Management at the University of Massachusetts (UMass) Amherst. She is also the director of the Virtual Center for Supernetworks. Her research focuses on network systems, including supply chain networks, with applications ranging from perishable product supply chains from food to healthcare, as well as disaster management. She is an INFORMS Fellow and is a recipient of the WORMS Award for the Advancement of Women in OR/MS, the Moving Spirit Award, and the Volunteer Service Award from INFORMS, and she was an Omega Rho Distinguished Lecturer. Her recent awards have included the 2020 Harold Larnder Prize of the Canadian Operational Research Society and the 2019 Constantin Caratheodory Prize of the International Society of Global Optimization. She has served as the faculty advisor to the UMass Amherst INFORMS Student Chapter since 2004.
Viet Anh Nguyen (“Statistical Analysis of Wasserstein Distributionally Robust Estimators”) is a postdoctoral scholar in the Department of Management Science and Engineering, Stanford University, and is also the head of the Machine Learning Group at VinAI, Vietnam. He is interested in very large-scale decision making under uncertainty, statistical optimization, and machine learning, with applications in energy systems, operations management, and data/policy analytics. He holds a BEng and a MEng from the National University of Singapore, a French engineering diploma (Diplôme d’Ingénieur) from École Centrale Paris, and a PhD from École Polytechnique Fédérale de Lausanne.
Johannes O. Royset (“Good and Bad Optimization Models: Insights from Rockafellians”) is a professor of operations research at the Naval Postgraduate School. His research focuses on stochastic and deterministic optimization problems arising in data analytics, sensor management, and reliability engineering. He was awarded a National Research Council postdoctoral fellowship (2003), a Young Investigator Award from the Air Force Office of Scientific Research (2007), and the Barchi Prize as well as the MOR Journal Award from the Military Operations Research Society (2009). He received the Carl E. and Jessie W. Menneken Faculty Award for Excellence in Scientific Research (2010) and the Goodeve Medal from the Operational Research Society (2019). He was a plenary speaker at the 2016 International Conference on Stochastic Programming and at the 2018 SIAM Conference on Uncertainty Quantification. He received his PhD from the University of California, Berkeley, in 2002. An author of over 90 papers, a monograph, and the textbook An Optimization Primer, he serves as associate editor for the SIAM Journal on Optimization, Operations Research, the Journal of Optimization Theory and Applications, the Journal of Convex Analysis, Set-Valued and Variational Analysis, and Computational Optimization and Applications.
Pascal Van Hentenryck (“Machine Learning for Optimal Power Flows”) is the A. Russell Chandler III Chair and Professor, as well as the Associate Chair for Innovation and Entrepreneurship, in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. His current research primarily focuses on machine learning, optimization, and privacy with applications in mobility, energy, and resilience.
Neil Walton (“Learning and Information in Stochastic Networks and Queues”) is a reader in mathematics at the University of Manchester. His is interested in all aspects of applied probability, and his research principally concerns the decentralized minimization of congestion in networks. He is an associate editor for the journals Operations Research, Operations Research Letters, and Stochastic Systems. He has won the best paper awards at the ACM SIGMETRICS conference, and he was awarded the 2018 Erlang Prize by the INFORMS Applied Probability Society.
Alex L. Wang (“Exactness in Semidefinite Program Relaxations of Quadratically Constrained Quadratic Programs: Theory and Applications”) is a PhD student in the Computer Science Department at Carnegie Mellon University, advised by Fatma Kılınç-Karzan. His research interests lie at the intersection of optimization and theoretical computer science. Most recently, his research has focused on quadratically constrained quadratic programs—specifically, in understanding when semidefinite program relaxations of these problems admit notions of exactness.
Ruxian Wang (“Discrete Choice Models and Applications in Operations Management”) is a professor at the Johns Hopkins Carey Business School. Before returning to academia, he worked at the Hewlett–Packard Company for several years as a research scientist. He received his PhD in operations research from Columbia University. He is particularly interested in developing new discrete choice models and studying consumer purchase behavior and the associated operations problems, including assortment optimization, pricing, and data-driven decision making. His articles have appeared in several flagship journals in these fields, such as Management Science, Manufacturing & Service Operations Management, Operations Research, and Production and Operations Management.
Kuang Xu (“Learning and Information in Stochastic Networks and Queues”) is an associate professor of operations, information and technology at Stanford’s Graduate School of Business and associate professor (by courtesy) with the Electrical Engineering Department at Stanford University. Born in Suzhou, China, he received his BS in electrical engineering (2009) from the University of Illinois at Urbana–Champaign and PhD in electrical engineering and computer science (2014) from the Massachusetts Institute of Technology. His research focuses on understanding fundamental properties and design principles of large-scale stochastic systems using tools from probability theory and optimization, with applications in queueing networks, privacy, and machine learning. He is a recipient of first place in the 2011 INFORMS George E. Nicholson Student Paper Competition, the Best Paper Award and the Kenneth C. Sevcik Outstanding Student Paper Award at the 2013 ACM SIGMETRICS Conference, and the 2020 ACM SIGMETRICS Rising Star Research Award. He currently serves as an associate editor for Operations Research.
Xiaowei Zhang (“Surrogate-Based Simulation Optimization”) is an assistant professor in the Faculty of Business and Economics at the University of Hong Kong. His research interests lie in the intersection of simulation optimization and machine learning. He received his PhD in management science and engineering from Stanford University.
