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• Abstract

  TBD
• Biography

  Tal Raviv is an associate professor of industrial engineering. He holds a BA from the Eitan Berglas School of Economics, Tel Aviv University (1993), an MBA from the Recanati School of Business, Tel Aviv University (1997), and a PhD in Operations Research from the William Davidson Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa (2003). He spent two years (2004-2006) as a postdoctoral fellow in the Sauder School of Business at the University of British Columbia, Vancouver, Canada. He joined the department of industrial engineering at Tel Aviv University in 2006. Tal published many papers in the operations research literature and served as an advisor for start-up companies. His current primary research interest is in transportation and logistics with a focus on smart transportation and sustainable logistics, and he is co-heading the transportation and logistics lab in the faculty of engineering. Tal serves as the head of the Shlomo Shmeltzer institute for smart transportation in Tel Aviv University.

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• Abstract

  TBD
• Biography

  Rafael Martinelli has been a professor at the Teaching and Research Board of the Department of Industrial Engineering since 2016. His research interests are Combinatorial Optimization, Mathematical Programming, Heuristics, Metaheuristics, Algorithm Analysis and Computation Complexity, aligned with the “Combinatorial Optimization” projects and “Optimization under Uncertainty” from the “Algorithms and Optimization” research line, from the “Operational Research” concentration area, within the department’s academic postgraduate program in Production Engineering, and aligned with the “Programming of Logistics, Distribution and Transport Systems”, from the research line of “Programming and Control of Logistics Transport Systems”, from the “Logistics” concentration area of the department’s professional master’s program in Logistics. He currently has a PQ Level 2 productivity scholarship from CNPq since 2018 and a Young Scientist from FAPERJ since 2021. He is deputy coordinator of the academic postgraduate program in Production Engineering and infrastructure coordinator of the department. He co-founded and is co-coordinator of the FROG laboratory – Forecasting and Resource Optimization Group. He is a member of SOBRAPO – Brazilian Society of Operational Research, ABEPRO – Brazilian Association of Production Engineering, and ANPEPRO – National Association of Postgraduate Programs and Research in Production Engineering. 

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• Abstract

  Optimal decision making in finance and insurance includes a statistical part (estimation of the random scenario process),  a modeling part (designing the decision objective and the constraints) and an algorithmic part (mathematical optimization). It is evident that errors in the statistical part lead directly to errors in the decision found in the optimization part. In order to incorporate the model uncertainty into the whole decision process, we define confidence regions for the distribution of the estimated scenario process by methods of nonparametric statistics and find minimax (i.e. saddle point solutions) for the final decisons, which are good compromises between optimality and distributional robustness. We define the quantities: costs for ambiguity (COA) and reward for distributional robustness (RDR), which caracterize the quality of the decision making and extend the notions of expected value of perfect information (EVPI) and value of stochastic solution. We illustrate the presented concepts giving examples from financial management, pension fund managenent and insurance contract design.   
• Biography

  Born in 1951, Georg Pflug studied Law, Mathematics and Statistics at the University of Vienna. He was Professor at the University of Giessen, Germany and is Full Professor emeritus at the Faculty of Business, Economics and Statistics of the University of Vienna He is also part time research scholar at the International Institute of Applied Systems Analysis, Luxenburg, Austria. Georg Pflug's interests include Stochastic Modeling, Stochastic Optimization, Measuring and Managing of Risks and Applications in Finance (including Pension Funds), Insurance and Energy.

  Georg Pflug held visiting positions University of Bayreuth, Michigan State University, University of California at Davis, Université de Rennes, Technion Haifa and Princeton University. He was lecturing courses in Universität Kiel, Financial University Moscow, University Fondacao Getulio Vargas in Rio de Janeiro, University degli studi di Bergamo.

  He was/is Associate Editor of 10 international journals and author of 5 books, editor of 8 books, and more than 160 publications in refereed journals.

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• Abstract

  TBD
• Biography

  David E. Bernal Neira is an Assistant Professor at the Davidson School of Chemical Engineering at Purdue University. He specializes in applying mathematical and computer science tools to address problems relevant to science and engineering, for example, chemical, process, and energy systems engineering. In particular, he works in nonlinear discrete optimization, where, besides applications, he has been working in theory, algorithms, and software. He has complemented this work with research in Quantum Computing. David is the co-director of the Quantum Computing committee from INFORMS and a former member of the Quantum and AI Laboratory at NASA Ames.

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• Abstract

  TBD
• Biography

  Veronica Piccialli is Full Professor in Operations Research at DIAG Sapienza University of Rome since September 2021. Before she was first Assistant Professor and then Associate Professor at University of Rome Tor Vergata. Since 2019 she is Associate Editor in the area "Design and Analysis of Algorithms: Continuous" for INFORMS Journal on Computing, since 2021 she is Associate Editor of EURO Journal on Computational Optimization, since 2023 she is Associate Editor of TOP- Transactions in Operations Research. Her research interests are: Semidefinite Programming, Mixed Integer Nonlinear Programming, Machine Learning and Optimization, Interpretability of Machine Learning Algorithms, Global Optimization. Since 2015 she has collaborated with LRGP and LORIA at Universitè de Lorraine on Process Engineering Optimization.

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• Abstract

  The concept of Digital Twin has evolved from a theoretical framework into a key technology driving digital transformation across industries. This session will explore the fundamentals of Digital Twin, transformative algorithms, data-driven modeling techniques, and practical applications across various industrial domains, focusing on the role of Operations Research (OR) in Digital Twin systems.

  We will discuss how mathematical optimization, machine learning, deep learning, graph analytics, quantum computing, high-performance computing, and simulation models are integrated into Digital Twin systems to enable real-time decision-making, predictive analytics, and operational optimization. Through case studies on smart infrastructure, including factories and industrial facilities, we will showcase how Digital Twin technology transforms operations, enhances efficiency, and drives innovation.
• Biography

  Katsuki Fujisawa is a Professor at the Institute of Innovative Research at the Tokyo Institute of Science in Japan. Fujisawa has made significant contributions to the development of high-speed optimization solvers, AI-driven decision-making systems, and Digital Twin technologies. His research integrates Operations Research (OR), machine learning, deep learning, and quantum computing to address complex real-world challenges, including mobility optimization and supply chain management.

  He has collaborated with numerous industry partners, government agencies, and academic institutions to drive innovation in data-driven optimization and computational intelligence. His work has been recognized internationally, and he has led several large-scale national and industrial projects in Japan.

  Prof. Fujisawa is also actively involved in advancing high-performance computing (HPC) applications, pushing the boundaries of computational optimization by leveraging supercomputing environments such as Fugaku and TSUBAME. From 2014 to 2024, his project team secured 1st place in the Graph500 benchmark for shortest path computation on extremely large graphs in the 8th, 10th through 18th, and 20th through 26th rankings.

  Xun Shen is an Associate Professor at the Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology in Japan, where he is the principal investigator of a research lab focusing on optimization and learning-based decision-making. His research focuses on control theory, reinforcement learning, and optimization, with applications in modeling and decision-making under uncertainty. He has made significant contributions to the study of constrained Markov decision processes, chance-constrained optimization, uncertainty quantification, and robust control. His work integrates theoretical foundations with practical applications, addressing challenges in autonomous systems and data-driven decision-making. He has collaborated with industry partners and academic institutions to develop computationally efficient methods for optimization-based modeling and control for uncertain systems. His research has been published in top-tier conferences and journals, and he has been actively involved in advancing the intersection of artificial intelligence and control engineering.

  Pedro Galileo Romo is the CEO and Chief Quantum Officer (CQO) of PGR, Inc., leading deep tech innovations in smart manufacturing, autonomous vehicles, and intelligent cities. Previously, he was a Senior Technical Program Manager at Google, optimizing data center assembly, and held key roles at Boeing, managing electrical systems and manufacturing for the 787 Dreamliner, saving over $100M in production costs. He holds an MBA from the University of North Carolina at Chapel Hill and a Master’s in Organizational Leadership from Gonzaga University.

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• Abstract

  The pace of innovation for mixed-integer nonlinear optimization technology has sped up over the past decade, driven by modern applications in finance, statistics, control, energy, among others. The improvements have led to astonishing progress in certain settings, with practical problems that can be solved comfortably at large scales, but has been lackluster in other areas. In this talk we review the state-of-the-art of practical mixed-integer nonlinear optimization, present recent improvements but also discuss major challenges hampering the widespread implementation of successful mixed-integer nonlinear optimization solvers. 
• Biography

  Andrés Gómez received his B.S. in Mathematics and B.S. in Computer Science from the Universidad de los Andes (Colombia), and obtained his M.S. and Ph.D. in Industrial Engineering and Operations Research from the University of California Berkeley. He currently is a faculty in the Department of Industrial and Systems Engineering at the University of Southern California. Dr. Gómez research focuses on developing new theory and tools for challenging optimization problems and their applications in statistics and machine learning. His research has been supported by numerous grants from the National Science Foundation, the Air Force Office of Scientific Research (including a YIP award), Google and Meta

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• Abstract

  Combinatorial optimization augmented machine learning (COAML) is a novel and rapidly growing field that integrates methods from machine learning and operations research to tackle data-driven problems that involve both uncertainty and combinatorics. These problems arise frequently in industrial processes, where firms seek to leverage large and noisy data sets to better optimize their operations. COAML typically involves embedding combinatorial optimization layers into neural networks and training them with decision-aware learning techniques. This talk provides an overview of the field, covering its main applications, algorithms, and theoretical foundations. We also demonstrate the effectiveness of COAML on contextual and dynamic stochastic optimization problems.
• Biography

  Axel Parmentier has been a researcher at Ecole des Ponts since 2016, where he founded and holds the chair of artificial intelligence for air transport with Air France. His research is at the intersection of operational research and machine learning. He recently received the Robert Faure award, which is given every three years by the French society of operational research (ROADEF) to a researcher under 35 for their contributions to the field.