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Editorial Board
Editor-in-Chief
Jose Blanchet
Stanford University
Stanford, California, United States
[email protected]
Area Editors
Continuous Optimization
Frank Curtis
Lehigh University
Bethlehem, Pennsylvania, United States
[email protected]
George Lan
Georgia Institute of Technology
Atlanta, Georgia, United States
[email protected]
Discrete Optimization
Amitabh Basu
Johns Hopkins University
Baltimore, Maryland, United States
[email protected]
Game Theory
Panayotis Mertikopoulos
CNRS
Grenoble, France
[email protected]
Marco Scarsini
Luiss University
Rome, Italy
[email protected]
David Gamarnik
Massachusetts Institute of Technology
Cambridge, Massachusetts, United States
[email protected]
Stochastic Models
Xin Guo
University of California, Berkeley
Berkeley, California, United States
[email protected]
Henry Lam
Columbia University
New York City, New York, United States
[email protected]
Area Statements
Each area statement reflects the journal’s common standards of originality, significance, and mathematical depth, while allowing for differences in emphasis across methodological communities.
The journal’s editorial areas are intended to provide a durable structure, but not a rigid one. As the field evolves, Mathematics of Operations Research will continue to refine the scope of its areas and engage with emerging methodological directions that may shape the future of OR/MS.
Continuous Optimization
The Continuous Optimization area of Mathematics of Operations Research focuses on the development and analysis of mathematical models, theory, and algorithms for optimization problems involving continuous decision variables. We welcome submissions that advance the methodological and theoretical foundations of continuous optimization and contribute broadly to operations research and mathematical optimization.
Continuous optimization is a central component of modern decision making and underpins many areas of operations research, as well as related disciplines such as machine learning, statistics, economics, and engineering. Increasing problem scale, uncertainty in data and models, and the need for real-time decision making continue to drive advances in optimization theory and algorithms.
Problem classes include, but are not limited to, convex and conic optimization, including semidefinite programming; nonconvex and nonsmooth optimization; variational inequalities; and stochastic, robust, or dynamic optimization. Methodological topics include first-order, higher-order, and zeroth-order (derivative-free) methods; stochastic and randomized algorithms; algorithms for large-scale and structured problems; convergence analysis and oracle complexity; as well as duality, convex analysis, and variational analysis.
Game Theory
The Game Theory area of Mathematics of Operations Research welcomes papers that develop the mathematical aspects and foundations of game theory in ways that are motivated by problems arising in operations research and management science.
Topics include stochastic, repeated, differential, and mean-field games; equilibrium concepts and refinements; evolutionary game theory; learning in games; and algorithmic game theory, auctions, market and mechanism design, matching, and social choice.
Submissions should clearly articulate their insights, motivation, and significance for a broad OR/MS audience. Highly technical papers lacking clear motivation or OR/MS context are unlikely to fit this area.
Stochastic Models
The Stochastic Models area seeks to publish high-quality research on applied probability, stochastic analysis, stochastic computation, stochastic control, stochastic games, and stochastic optimization relevant to operations research.
Applications span queueing, finance, manufacturing, supply chains, communication networks, healthcare, energy, transportation, social networks, and emerging data- driven domains.
Submissions are evaluated based on novelty, insight, breadth of applicability, and potential to open new research directions. Motivation and implications are as important as technical correctness.
Discrete Optimization
Discrete optimization is the study of optimization problems with an inherent discreteness or combinatorial aspect in their decision spaces. It has applications in diverse areas ranging from supply chain management and manufacturing to healthcare and energy.
Its mathematical foundations bring together ideas from discrete mathematics and combinatorics, convex analysis and geometry, polyhedral theory, geometry of numbers, probability theory, and computational complexity theory.
We invite papers with significant contributions to discrete optimization, including but not limited to discrete and convex geometry, polyhedral combinatorics, deterministic and stochastic combinatorial optimization, mixed-integer optimization, approximation and online algorithms, learning-augmented algorithm design, and quantum computational aspects of discrete optimization.
Learning and Computation
The Learning and Computation area welcomes mathematically grounded contributions at the interface of operations research with learning, artificial intelligence, and emerging computational paradigms.
Topics include learning-augmented algorithms, online learning, bandits, reinforcement learning, automated reasoning, theorem proving, formal verification, quantum algorithms, and other computational approaches that contribute foundational insight relevant to OR/MS.
Papers focused primarily on implementation or empirical performance, without substantial methodological or theoretical contribution, are generally not a good fit for this area.
Associate Editors
Continuous Optimization
Necdet Serhat Aybat
The Pennsylvania State University
University Park, Pennsylvania, United States
Jelena Diakonikolas
University of Wisconsin-Madison
Madison, Wisconsin, United States
Dmitriy Drusvyatskiy
University of Washington
Seattle, Washington, United States
Rui Gao
The University of Texas at Austin
Austin, Texas, United States
Geovani Nunes Grapiglia
Universite catholique de Louvain
Ottignies-Louvain-la-Neuve, Belgium
Benjamin Grimmer
Johns Hopkins University
Baltimore, Maryland, United States
Mert Gurbuzbalaban
Rutgers University
New Brunswick, New Jersey, United States
Niao He
ETH Zürich
Zürich, Switzerland
Didier Henrion
University of Toulouse
Toulouse, France
Tim Hoheisel
McGill University
Montreal, Quebec, Canada
Fatma Kilinc-Karzan
Carnegie Mellon University
Pittsburgh, Pennsylvania, United States
Haihao Lu
Massachusetts Institute of Technology
Cambridge, Massachusetts
Zhaosong Lu
University of Minnesota
Minneapolis, Minnesota, United States
Patrick Mehlitz
Philipps-Universität Marburg
Marburg, Germany
Johannes Milz
Georgia Institute of Technology
Atlanta, Georgia, United States
Jiawang Nie
University of California, San Diego
San Diego, California, United States
Ting Kei Pong
The Hong Kong Polytechnic University
Kowloon, Hong Kong
Daniel Robinson
Lehigh University
Bethlehem, Pennsylvania, United States
Shoham Sabach
Cornell University
Ithaca, New York, United States
Uday Shanbhag
University of Michigan-Ann Arbor
Ann Arbor, Michigan, United States
Anthony Man-Cho So
The Chinese University of Hong Kong
Sha Tin, Hong Kong
Zaiwen Wen
Peking University
Beijing, China
Yangyang Xu
Rensselaer Polytechnic Institute
Troy, New York, United States
Discrete Optimization
Daniel Dadush
Centrum Wiskunde en Informatica
Amsterdam, Netherlands
Hiroshi Hirai
Nagoya University
Nagoya, Japan
Aida Khajavirad
Lehigh University
Bethlehem, Pennsylvania, United States
James Luedtke
University of Wisconsin-Madison
Madison, Wisconsin, United States
Ben Moseley
Carnegie Mellon University
Pittsburgh, Pennsylvania, United States
Viswanath Nagarajan
University of Michigan
Ann Arbor, Michigan, United States
Giacomo Nannicini
University of Southern California
Los Angeles, California, United States
Alberto Del Pia
University of Wisconsin-Madison
Madison, Wisconsin, United States
Jean-Philippe P. Richard
University of Minnesota
Minneapolis, Minnesota, United States
Laura Sanità
Bocconi University
Milan, Italy
Vera Traub
ETH Zürich
Zürich, Switzerland
László Végh
London School of Economics and Political Science
London, United Kingdom
Yu Yokoi
Institute of Science Tokyo
Meguro, Japan
Giacomo Zambelli
London School of Economics
London, United Kingdom
Game Theory
Yakov Babichenko
Technion
Ozan Candogan
University of Chicago,
Chicago, Illinois, United States
Pierre Cardaliaguet
Paris Dauphine University
Paris, France
José Correa
Universidad de Chile
Santiago, Chile
Paul Dütting
Google Zürich
Zürich, Switzerland
Tobias Harks
Universität Passau
Passau, Germany
Jean-Jacques Herings
Tilburg University
Tilburg, Netherlands
David Kempe
University of Southern California
Los Angeles, California, United States
Bettina Klaus
University of Lausanne
Lausanne, Switzerland
David Leslie
Lancaster University
Lancaster, Lancashire, England
Irene Lo
Stanford University
Stanford, California, United States
Ruta Mehta
University of Illinois at Urbana-Champaign
Champaign, Illinois, United States
Francesca Parise
Cornell University
Ithaca, New York, United States
Vianney Perchet
ENSAE Paris, Polytechnic Institute of Paris
Palaiseau, France
Catherine Rainer
Université de Brest
Brest, France
Daniela Saban
Stanford University
Stanford, California, United States
Eilon Solan
Tel Aviv University
Tel Aviv, Israel
Philipp Strack
Yale University
New Haven, Connecticut, United States
Warut Suksompong
National University of Singapore
Singapore, Singapore
Bruno Ziliotto
Toulouse School of Economics, CNRS
Toulouse, France
Learning Theory
Alessandro Arlotto
Duke University
Durham, NC, United States
Boris Hanin
Princeton University
New Haven, Connecticut, United States
Gilles Stoltz
CNRS-Université Paris Sud and HEC, Paris
Paris, France
Stochastic Models
Erhan Bayraktar
University of Michigan
Ann Arbor, Michigan, United States
Amarjit Budhiraja
The University of North Carolina at Chapel Hill
Chapel Hill, North Carolina, United States
Rama Cont
Oxford University
Oxford, United Kingdom
Jing Dong
Columbia University
New York City, New York, United States
Philip A. Ernst
Imperial College London
London, England
Peyman Mohajerin Esfahani
University of Toronto
Toronto, Ontario, Canada
Daniel Freund
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
Varun Gupta
The University of Utah
Salt Lake City, Utah, United States
Daniel H. Lacker
Columbia University
New York City, New York, United States
Yiping Lu
Stanford University
Stanford, California, United States
Siva Theja Maguluri
Georgia Institute of Technolgy
Atlanta, Georgia, United States
Karthyek Murthy
University of Southern California
Los Angeles, California, United States
Marcel Nutz
Columbia University
New York, New York, United States
Guodong Pang
Rice University
Houston, Texas, United States
Dylan Possamai
ETH Zürich
Amber L. Puha
California State University San Marcos, San Marcos, California, United States
Naci Saldi
Bilkent University
Ankara, Turkey
Mete Soner
Princeton University
Princeton University, United States
Vijay Subramanian
University of Michigan
Ann Arbor, Michigan, United States
Vasilis Syrgkanis
Stanford University
Stanford, California, United States
Wenpin Tang
Columbia University
New York City, New York, United States
Guanyang Wang
Rutgers, The State University of New Jersey
New Brunswick, New Jersey, United States
Ruodu Wang
University of Waterloo
Waterloo, Ontario, Canada
Weina Wang
Carnegie Mellon University
Pittsburgh, Pennsylvania, United States
Johannes Wiesel
University of Copenhagen
Copenhagen, Denmark
Qiaomin Xie
University of Wisconsin-Madison
Madison, Wisconsin, United States
Renyuan Xu
Stanford University
Stanford, California, United States
Zuoquan Xu
Hong Kong Polytechnic University
Hung Hom, Hong Kong
Thaleia Zariphopoulou
The University of Texas at Austin
Austin, Texas, United States
Zhengyuan Zhou
New York University
New York, New York, United States
Advisory Editors
Former Editors-in-Chief
| 2013–2018: | J.G. "Jim" Dai |
| 2009–2012: | Uriel G. Rothblum |
| 2004–2009: | Nimrod Megiddo |
| 1999–2003: | Gérard Cornuéjols |
| 1993–1998: | Jan Karel Lenstra |
| 1987–1992: | Erhan Çinlar |
| 1981–1986: | Stephen M. Robinson |
| 1976–1980: | Arthur F. Veinott Jr. |
Managing Editor
Hayes Simpson
INFORMS
5521 Research Park Drive
Suite 200
Catonsville, Maryland 21228
[email protected]
