Jérôme Bolte is a Full Professor at the Toulouse School of Economics and holds a Chair in Artificial Intelligence at the Artificial Intelligence Institute of Toulouse (ANITI). He studied pure and applied mathematics before completing a degree in mathematics and then a doctorate at Montpellier University. Prior to moving to Toulouse in 2010, he spent six years as an associate professor at Sorbonne University in Paris and one year at École Polytechnique. He received the SIAM Optimization Prize in 2017, along with S. Sabach and M. Teboulle, for work at the crossroads of semi-algebraic geometry and first-order methods. He is currently an associate editor at Mathematical Programming and Foundations of Computational Mathematics. His research interests range from continuous optimization to machine learning.

John Duchi is an associate professor of Statistics and Electrical Engineering and (by courtesy) Computer Science at Stanford University. His work spans statistical learning, optimization, information theory, and computation, with a few driving goals. (1) To discover statistical learning procedures that optimally trade between real-world resources---computation, communication, privacy provided to study participants---while maintaining statistical efficiency. (2) To build efficient large-scale optimization methods that address the spectrum of optimization, machine learning, and data analysis problems we face, allowing us to move beyond bespoke solutions to methods that robustly work. (3) To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.

He has won several awards and fellowships. His paper awards include the SIAM SIGEST award for "an outstanding paper of general interest" and best papers at the Neural Information Processing Systems conference, the International Conference on Machine Learning, the International Conference on Learning Theory, and an INFORMS Applied Probability Society Best Student Paper Award (as advisor). He has also received the Society for Industrial and Applied Mathematics (SIAM) Early Career Prize in Optimization, an Office of Naval Research (ONR) Young Investigator Award, an NSF CAREER award, a Sloan Fellowship in Mathematics, the Okawa Foundation Award, the Association for Computing Machinery (ACM) Doctoral Dissertation Award (honorable mention), and U.C. Berkeley's C.V. Ramamoorthy Distinguished Research Award.

Maryam Fazel is the Moorthy Family Professor of Electrical and Computer Engineering at the University of Washington, with adjunct appointments in Computer Science and Engineering, Mathematics, and Statistics. Maryam received a PhD from Stanford University and a BS from Sharif University of Technology in Iran. She is a recipient of the NSF Career Award, UWEE Outstanding Teaching Award, UAI conference Best Student Paper Award, and her paper on low-rank matrix estimation was selected by ScienceWatch as a “Fast Breaking Paper” (based on number of citations). She directs the Institute for Foundations of Data Science (IFDS), a multi-site NSF TRIPODS Institute. She currently serves on the program committee of ICML 2025, the Editorial board of the MOS-SIAM Book Series on Optimization, and as an Action Editor for Journal on Machine Learning Research. Her current research interests are in the area of optimization in machine learning and control.

Tim Hoheisel received his doctorate of mathematics from Julius-Maximilians University  (Würzburg) under the supervision of Christian Kanzow in 2009. He was a postdoctoral researcher there until 2016. During this time, he was a visiting professor at Heinrich-Heine University (Düsseldorf) in the winter semester 2011/12 as well as a visiting researcher at University of Washington (Seattle) under mentorship of James V. Burke in 2012 and 2014.  In 2016, he became an assistant professor at the department of mathematics and statistics at McGill University (Montreal) where he was awarded early tenure in 2021 and promoted to associate professor. Since 2022 he has been director of the applied mathematics laboratory at the "Centre de Recherches Mathématiques" in Montreal. His research interests lie in nonsmooth optimization and variational analysis where he is particularly interested in stability of nonsmooth problems arising in various applications.

Mingyi Hong received his Ph.D. degree from the University of Virginia, Charlottesville, in 2011. He is currently an Associate professor in the Department of Electrical and Computer Engineering at the University of Minnesota, Minneapolis. His research has been focused on developing optimization theory and algorithms for applications in signal processing and machine learning, and most recently applying these techniques for foundation model training, finetuning and alignment. He is an Associate Editor for IEEE Transactions on Signal Processing. His work has received two IEEE Signal Processing Society (SPS) Best Paper Awards (2021, 2022), an International Consortium of Chinese Mathematicians Best Paper Award (2020), and a few Best Student Paper Awards in signal processing and machine learning conferences. He is an Amazon Scholar, and he is the recipient of an IBM Faculty Award, a Meta research award, a Cisco research award, and the 2022 Pierre-Simon Laplace Early Career Technical Achievement Award from IEEE SPS.

Ruth Misener is Professor of Computational Optimisation at Imperial College where she holds a BASF / Royal Academy of Engineering (RAEng) Research Chair in Data-Driven Optimisation. In 2017, Ruth received the MacFarlane Medal as overall winner of the RAEng Young Engineer Trust Engineer of the Year competition. Ruth also received the 2023 Roger Needham Award from the British Computing Society. Ruth's best paper awards are from the: Journal of Global Optimization (2013), International Conference on Autonomous Agents & Multi-Agent Systems (AAMAS 2020, Best Innovative Demo), Conference on the Integration of Constraint Programming, Artificial Intelligence, & Operations Research (CPAIOR 2021), Optimization & Engineering (2021), and Computers & Chemical Engineering (2023). She has given named lectures at Princeton University (2023 Saville Lecture) and Georgia Institute of Technology (2018 Mellichamp Lecture). Ruth leads a team of research engineers in developing the Optimization & Machine Learning Toolkit (OMLT), which won the 2022 COIN-OR Cup as the best contribution to open-source operations research software development. Ruth is associate editor for INFORMS Journal on Computing and Operations Research. She is also a NeurIPS Area Chair.

Anthony Man-Cho So is currently Dean of the Graduate School, Deputy Master of Morningside College, and Professor in the Department of Systems Engineering and Engineering Management of The Chinese University of Hong Kong (CUHK). His research focuses on the theory and applications of optimization in various areas of science and engineering, including computational geometry, machine learning, signal processing, and statistics. Dr. So has been a Fellow of IEEE since 2023 and an Outstanding Fellow of the Faculty of Engineering at CUHK since 2019. He currently serves on the editorial boards of Journal of Global Optimization, Mathematical Programming, Mathematics of Operations Research, Open Journal of Mathematical Optimization, Optimization Methods and Software, and SIAM Journal on Optimization. He has also served as the Lead Guest Editor of IEEE Signal Processing Magazine Special Issue on Non-Convex Optimization for Signal Processing and Machine Learning. Dr. So has received a number of research and teaching awards, including the 2024 SIAM Review SIGEST Award, the 2018 IEEE Signal Processing Society Best Paper Award, the 2015 IEEE Signal Processing Society Signal Processing Magazine Best Paper Award, the 2014 IEEE Communications Society Asia-Pacific Outstanding Paper Award, and the 2010 INFORMS Optimization Society Optimization Prize for Young Researchers, as well as the 2022 University Grants Committee Teaching Award (General Faculty Members Category), the 2022 University Education Award, and the 2013 CUHK Vice-Chancellor’s Exemplary Teaching Award.

Angelika Wiegele is Professor at the Mathematics Department at Alpen-Adria-Universität Klagenfurt and is currently a member of the Global Faculty of the University of Cologne. She received her Ph.D. in Mathematics at the Alpen-Adria-Universität Klagenfurt in 2006 and was a researcher and lecturer at TU Graz and at Alpen-Adria-Universität Klagenfurt. She was also a researcher at IASI-CNR in Rome and at the University of Cologne, and visiting professor at the Università degli Studi di Roma "Tor Vergata". 

Her research interests lie in the field of semidefinite optimization, in particular, in the application of semidefinite methods for solving mixed-integer nonlinear optimization problems. Her research projects have been funded by the Austrian Science Fund FWF and by the European Union's Horizon 2020 program. She is associate editor of the Open Journal of Mathematical Optimization, OR Spectrum and TOP (Transaction in Operations Research) and is currently guest editor of Mathematical Programming B.