Merve Bodur

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Merve Bodur is an Associate Professor (Reader) in the School of Mathematics at The University of Edinburgh. She obtained her Ph.D. from the University of Wisconsin-Madison, her B.S. in Industrial Engineering and her B.A. in Mathematics from Bogazici University, Turkey. Her main research area is optimization under uncertainty, primarily for discrete optimization problems, with applications in a variety of areas such as scheduling, transportation, healthcare, telecommunications, and power systems. She serves on the editorial boards of INFORMS Journal on Computing, Operations Research Letters, Omega, and INFOR. She is currently the Vice Chair/Chair-Elect of the INFORMS Computing Society, and serves on the Committee on Stochastic Programming and Mathematical Optimization Society Council.

Antonis Economou

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Antonis Economou is Professor of Operations Research in the Department of Mathematics at National and Kapodistrian University of Athens (NKUA). He holds a PhD degree in Mathematics from the NKUA and an MA degree in Mathematics from the University of California Los Angeles (UCLA). His research interests concern various aspects of Operations Research, Applied Probability and Game Theory with a particular emphasis on their interplay in Queueing Theory. In recent years, he has been working on the study of strategic behavior in service systems, with a special focus on the role of information on customer strategic behavior and on the associated problems of social welfare and profit maximization. He has served in various editorial boards and is currently an Associate Editor of Queueing Systems and Methodology and Computing in Applied Probability.

Matthew O. Jackson

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Matthew O. Jackson is the William D. Eberle Professor of Economics at Stanford University and an external faculty member of the Santa Fe Institute. He was at Northwestern University and Caltech before joining Stanford. Jackson's research interests include game theory, microeconomic theory, and the study of social and economic networks, on which he has published many articles and the books `The Human Network' and `Social and Economic Networks'. He also teaches an online course on networks and co-teaches two others on game theory. Jackson is a Member of the National Academy of Sciences and a Fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the Econometric Society, the Game Theory Society, and an Economic Theory Fellow. His other honors include a Guggenheim Fellowship, the von Neumann Award from Rajk Laszlo College, the Jean-Jacques Laffont Prize from the Toulouse School of Economics, and the BBVA Frontiers of Knowledge Award in Economics, Finance, and Management.

Madeleine Udell

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Madeleine Udell is Assistant Professor of Management Science and Engineering at Stanford University, with an affiliation with the Institute for Computational and Mathematical Engineering (ICME) and courtesy appointment in Electrical Engineering. She was previously a tenured Associate Professor of Operations Research and Information Engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University. Her work builds the mathematical and computational foundations needed for scalable, accessible, and responsible data-driven decisionmaking in high-stakes domains, with impact on challenges in healthcare, finance, marketing, operations, and engineering, among others. She develops new efficient algorithms to accelerate and automate optimization and data science, and new frameworks that empower users to invoke these algorithms and interpret the resulting decisions, motivated by the view that hidden mathematical structure in the data, algorithms, and procedures that humans use to make decisions can accelerate and automate verifiable AI-driven methods.

Her awards include the Kavli Fellowship (2023), Alfred P. Sloan Research Fellowship (2021), a National Science Foundation CAREER award (2020), an Office of Naval Research (ONR) Young Investigator Award (2020), a Cornell Engineering Research Excellence Award (2020), an INFORMS Optimization Society Best Student Paper Award (as advisor) (2019), and INFORMS Doing Good with Good OR (2018). Her work has been supported by the NSF, ONR, AFOSR, DARPA, IBM, and the Canadian Institutes of Health.

Madeleine has advised more than 60 students and postdocs, including eight graduated PhD students who later joined Google, Amazon, Two Sigma, Uber, the University of Washington, UCSD, and Tsinghua University. She has developed several new courses in optimization and machine learning, earning Cornell's Douglas Whitney ’61 Engineering Teaching Excellence Award in 2018.

Wouter Kool

Wouter Kool is VP Research & AI at ORTEC, specializing in combining machine learning with combinatorial optimization to tackle complex real-world problems. He earned his PhD at the University of Amsterdam, supervised by Max Welling, and was among the first to apply deep learning to vehicle routing problems. Recognized for his work at the intersection of operations research and machine learning, he has helped connect the fields through initiatives such as the EURO meets NeurIPS 2022 Vehicle Routing Competition, and leads efforts at ORTEC to turn academic advances into innovative solutions.

Madeleine Udell

Personal website

Madeleine Udell is Assistant Professor of Management Science and Engineering at Stanford University, with an affiliation with the Institute for Computational and Mathematical Engineering (ICME) and courtesy appointment in Electrical Engineering. She was previously a tenured Associate Professor of Operations Research and Information Engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University. Her work builds the mathematical and computational foundations needed for scalable, accessible, and responsible data-driven decisionmaking in high-stakes domains, with impact on challenges in healthcare, finance, marketing, operations, and engineering, among others. She develops new efficient algorithms to accelerate and automate optimization and data science, and new frameworks that empower users to invoke these algorithms and interpret the resulting decisions, motivated by the view that hidden mathematical structure in the data, algorithms, and procedures that humans use to make decisions can accelerate and automate verifiable AI-driven methods.

Her awards include the Kavli Fellowship (2023), Alfred P. Sloan Research Fellowship (2021), a National Science Foundation CAREER award (2020), an Office of Naval Research (ONR) Young Investigator Award (2020), a Cornell Engineering Research Excellence Award (2020), an INFORMS Optimization Society Best Student Paper Award (as advisor) (2019), and INFORMS Doing Good with Good OR (2018). Her work has been supported by the NSF, ONR, AFOSR, DARPA, IBM, and the Canadian Institutes of Health.

Madeleine has advised more than 60 students and postdocs, including eight graduated PhD students who later joined Google, Amazon, Two Sigma, Uber, the University of Washington, UCSD, and Tsinghua University. She has developed several new courses in optimization and machine learning, earning Cornell's Douglas Whitney ’61 Engineering Teaching Excellence Award in 2018.