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Dag HauglandView profile

Professor

• Combinatorial Optimization
• Global Optimization
• Network Flow Models
• +7 more

Dag Haugland is a Professor in the Department of Informatics at the University of Bergen, Norway, where he conducts research and teaches in optimization, particularly combinatorial and global optimization with applications in network flows, energy systems, and logistics. He is affiliated with the Optimization research group and maintains an active research profile with recent publications in top-tier journals. Research Interests: His work focuses on Combinatorial Optimization , Global Optimization , and Network Flow Models , applying mathematical programming techniques to real-world problems in offshore wind energy, gas pipeline transportation, wireless networks, and vehicle routing. His research emphasizes integer programming, polyhedral analysis, and algorithmic design. Recent Research Trends: His latest publications (2023–2024) address tighter bounds in broadcast time problems and hydropower scheduling, reflecting a continued focus on theoretical and applied optimization in communication and energy networks. Earlier works (2016–2020) explore pooling problems, offshore wind farm cable layouts, and portfolio optimization, demonstrating interdisciplinary reach. Scientific Contributions: Extensive publication record in optimization and operations research. Supervision of multiple PhD and master’s students. Active involvement in conference proceedings and technical reports. Advising and Grants: Dag Haugland has supervised numerous students, including Marika Ivanova and Arne Klein, on topics such as offshore wind farm optimization and multicast tree problems. His work has been supported by Norwegian research funding, including the Research Council of Norway (project reference 249994). He contributes to academic service through conference organization (e.g., Norsk Informatikkonferanse) and collaborative research. Labs and Teams: He is a key member of the Optimization research group at the Department of Informatics, University of Bergen, which focuses on algorithmic and mathematical approaches to complex decision problems in engineering and industry.

• Mixed-Integer Nonlinear Optimization
• Cutting Plane Theory and Practice
• Convex Analysis and Discrete Geometry
• +4 more

Robert Hildebrand is an Assistant Professor in the Industrial and Systems Engineering Department at Virginia Tech's College of Engineering. His research bridges theoretical optimization with practical applications in redistricting, robotics, and healthcare. Previously, he held postdoctoral positions at IBM Watson Research Center as a Goldstine Fellow and at ETH Zurich's Institute for Operations Research. His educational background includes: Ph.D. in Applied Mathematics, University of California, Davis (2013) B.Sc. in Mathematics, University of Puget Sound (2008) Hildebrand specializes in mixed-integer nonlinear optimization and convex geometry, with significant contributions to cutting plane theory and computational complexity. His work integrates operations research with machine learning to solve problems ranging from political redistricting to autonomous fleet optimization. Recent projects demonstrate his focus on translating theoretical advances into real-world solutions for gerrymandering analysis and space exploration robotics. His publication trend reveals deepening expertise in non-convex integer programming, evidenced by 2024 works on reverse convex sets and binary program idealness proofs. Concurrently, he applies these methods to interdisciplinary challenges like veterans' healthcare access and invasive species prevention. Scientific recognition includes: Goldstine Fellowship (IBM Research) Air Force Office of Scientific Research Young Investigator Program Hildebrand actively mentors graduate researchers and secures substantial funding: Advising: Supervised PhD completions by Benjamin Beach (2022) and Jamie Fravel (2024), plus MS graduate Brannon King (2024) Grants: $40,000 Whole Health Consortium seed grant (2024), ICTAS EFO-O Seed Grant for space exploration robotics (2023), Minnesota Aquatic Invasive Species Research Center continuation grant He collaborates extensively through Virginia Tech's FASER lab and the Whole Health Consortium, developing optimization frameworks for geospatial healthcare analysis and autonomous fleet coordination in space exploration contexts.

Samuel A. BurerView profile

Professor

• Optimization
• Operations Research
• Analytics
• +4 more

Samuel A. Burer is the Tippie-Rollins Professor and Departmental Executive Officer in Business Analytics at the University of Iowa's Tippie College of Business. He earned his Ph.D. in Algorithms, Combinatorics, and Optimization from Georgia Institute of Technology and a B.S. in Mathematics from the University of Georgia. Research Interests: Optimization, operations research, management sciences, discrete and continuous optimization, decision making under uncertainty. Editorial Roles: Area Editor for Operations Research (2020-2026), Associate Editor for SIAM Journal on Optimization, Mathematical Programming, and others. Teaching: Teaches across all business education levels and received multiple teaching awards, including the University of Iowa President & Provost Award for Teaching Excellence. Scientific Awards: INFORMS Computing Paper Prize (2020) SIAM Optimization Test of Time Award (2023) President & Provost Award for Teaching Excellence (2022) Collegiate Teaching Award (2020) Optimization Prize for Young Researchers (2002) Grants: Principal Investigator for NSF CAREER grant (2006-2012) and collaborative NSF grants (2002-2005) focused on nonconvex quadratic and conic optimization theory. Projects: Developed optimization algorithms for Trader Joe's warehouse location analysis, college football rankings, and created software tools like QuadProgBB and OPTDNN for solving semidefinite programs.

• Mixed Integer Nonlinear Optimization
• Competitive Location
• Supervised Classification
• +5 more

Amaya Nogales Gomez is a Researcher affiliated with the Department of Integrated Sciences at the University of Huelva, associated with the Center for Advanced Studies in Physics, Mathematics, and Computing. She is a member of the research group TEP952: Vision, Prediction, Optimization, and Control Systems. Education: Doctorate from the University of Seville (2015), thesis titled 'Mixed Integer Nonlinear Optimization. Applications to Competitive Location and Supervised Classification.' Research Interests: Focus on mathematical optimization, machine learning, and operations research, with applications in spatial location modeling and classification algorithms. Her work bridges theoretical advancements in nonlinear programming with practical implementations in prediction and control systems.

Matthieu BarreauView profile

Assistant Professor

• Physics Informed Neural Networks
• Traffic Systems
• Time-Delay Systems
• +5 more

Matthieu Barreau is an Assistant Professor within the Division of Decision and Control Systems at KTH Royal Institute of Technology, Stockholm, Sweden, since September 2023. His academic journey includes a PhD in Control Systems from LAAS-CNRS, Toulouse (2019), a Master's degree in Space Engineering from KTH (2016), and an Engineering degree in Aeronautical Engineering from ISAE-ENSICA, Toulouse (2016). Education: PhD in Control Systems, LAAS-CNRS, Toulouse (2019) Master's in Space Engineering, KTH (2016) Engineering degree in Aeronautical Engineering, ISAE-ENSICA, Toulouse (2016) Dr. Barreau's research focuses on the intersection of system theory and machine learning, particularly on physics-informed neural networks (PINNs) applied to traffic systems, time-delay systems, and infinite-dimensional systems. His methodology addresses three key questions: what can be expected from data given measurement constraints, how to effectively train PINNs under specific constraints, and which tools are needed to certify the quality of trained models. His work combines robust control theory (Lyapunov functions, Integral Quadratic Constraints) with modern machine learning techniques, creating a bridge between traditional control theory and data-driven approaches. His recent publications demonstrate a clear trend toward applying PINNs to traffic flow modeling, power system monitoring, and stability analysis of complex dynamical systems. The research spans theoretical foundations of stability analysis for infinite-dimensional systems to practical applications in traffic control and power transformer monitoring. A significant portion of his work addresses the challenge of training physics-informed neural networks under constrained optimization frameworks, developing methods to ensure stability and performance guarantees. Scientific Awards: Best French Ph.D. thesis award from GdR MACS and Club EEA in 2020 Dr. Barreau actively supervises master's students for internships and thesis projects, though specific students aren't listed in the available information. His research is funded by multiple prestigious sources including the Human Horizon project Ultimate, the Swedish Foundation for Strategic Research, the Swedish Research Council, and Knut and Alice Wallenberg Foundation. His international collaborations include researchers from The University of Sheffield, Gipsa-Lab, and other institutions across Europe and the US. He leads projects on traffic applications under the supervision of Karl-Henrik Johansson and collaborates on stability analysis of infinite-dimensional systems with Alexandre Seuret, Frederic Gouaisbaut, and Carsten Scherer. Dr. Barreau has developed notable open-source contributions including the tf2-bfgs package for implementing BFGS optimization in TensorFlow 2, demonstrating his commitment to making research tools accessible to the broader community.

• Combinatorial Optimization
• Meta-heuristics
• Mixed Integer Programming
• +7 more

Luigi De Giovanni is an Associate Professor of Operations Research at the Department of Mathematics, University of Padua, where he has been faculty since 2007 (promoted to Associate Professor in 2018). He holds the Italian academic qualification as Full Professor since 2020. His research focuses on Combinatorial Optimization, Meta-heuristics, Mixed Integer Programming, and their applications in Transportation and Logistics, Air traffic and Airport Optimization, Telecommunication Network Design, and Production Scheduling. His work bridges theoretical optimization methods with practical industrial applications across various sectors including transportation, manufacturing, and telecommunications. Professor De Giovanni has led and participated in several significant research projects including: European Research Project 'OptiFrame - An Optimization Framework for Trajectory Based Operations' (H2020-SESAR-2015-1, 2016-2018) PRIN Project 'Nonlinear and Combinatorial Aspects of Complex Networks' (2017-2020) European Research Project 'AAS - Integrated Airport Apron Safety Fleet Management' (2008-2011) European Research Project 'SCOOP - Sheet Cutting and Process Optimization for Furniture Enterprises' (2006-2008) His extensive publication record spans from 2002 to 2024, with numerous articles in high-impact journals such as Transportation Science, IEEE Access, and European Journal of Operational Research. Recent publications show a strong emphasis on data-driven approaches to air traffic management, vehicle routing and logistics optimization, and graph theory applications, demonstrating continued research productivity and relevance. Professor De Giovanni teaches courses in Operations Research, Methods and Models for Combinatorial Optimization, and Stochastic Optimization at the University of Padua. His office hours are on Thursdays from 10:30 am to 12:30 pm by appointment.

Christian AlmederView profile

Professor

• Supply Chain Management
• Operations Research
• Production Planning and Scheduling
• +3 more

Prof. Dr. Christian Almeder serves as Professor and Head of the Chair of Supply Chain Management within the Faculty of Business Administration and Economics at Viadrina European University (Frankfurt (Oder), Germany). His research focuses on operations research applications in production planning, logistics, and supply chain optimization, with particular expertise in lot sizing, scheduling, and perishable goods management. He maintains active research output with publications spanning from 1997 to 2023. Almeder's research centers on mathematical modeling of complex production and logistics systems. His primary contributions involve developing heuristic and metaheuristic solutions for capacitated lot sizing problems, multi-level scheduling, and integrated production-distribution planning. Key specialties include handling perishability constraints, lead time uncertainties, and batch processing requirements using genetic programming, simulation-based optimization, and clearing function approaches. His work bridges theoretical operations research with industrial applications in supply chain management. Analysis of his 15 most recent publications (2013-2023) reveals a consistent focus on lot sizing and scheduling, with increasing emphasis on integrated supply chain problems and perishable goods logistics. Methodologically, he combines metaheuristics (genetic programming, simulated annealing) with simulation techniques to address real-world complexities like stochastic processing times and limited buffers. His work demonstrates strong application in production planning parameter tuning, vehicle routing integration, and robust operational planning under uncertainty.

Santanu DeyView profile

Professor

• Non-convex Optimization
• Mixed Integer Programming
• Operations Research
• +5 more

Dr. Santanu S. Dey is a Professor and Anderson-Interface Chair at the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. He holds a Ph.D. in Industrial Engineering from Purdue University (2007) and previously worked as a postdoctoral fellow at CORE, Catholic University of Louvain. Research Interests: Non-convex optimization, mixed-integer programming, energy systems optimization, and algorithm development for engineering problems. Leadership: Director of Doctoral Recruiting and Admissions at Georgia Tech ISyE. Served on editorial boards of Mathematical Programming and SIAM Journal on Optimization . Grants: Funded by DOE, NSF, ONR, Argonne National Lab, Sandia National Lab, and ExxonMobil for projects in power systems, decomposition algorithms, and sparse optimization. Scientific Awards include INFORMS Optimization Society Balas Prize (2020), NSF CAREER Award (2012), and IBM Faculty Award (2009). His work has been recognized with professorships and teaching fellowships at Georgia Tech.

Nikolaos SahinidisView profile

Professor

• Global optimization of mixed-integer nonlinear programs
• Informatics problems in chemistry and biology
• Process and energy systems engineering
• +10 more

Nikolaos Sahinidis is the Gary C. Butler Family Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the School of Chemical and Biomolecular Engineering at Georgia Institute of Technology. His research bridges computer science and operations research with applications across engineering and scientific domains, focusing on developing rigorous optimization methods for complex real-world problems. Dr. Sahinidis's research spans global optimization of mixed-integer nonlinear programs, informatics problems in chemistry and biology, process and energy systems engineering, and chemical product design. His work integrates theoretical algorithm development with practical applications in medical diagnosis, protein structure analysis, and environmentally benign chemical design. He has made significant contributions to inverse imaging problems in X-ray crystallography, biochemical network design, and black-box optimization. His recent publications demonstrate a clear trajectory toward integrating machine learning with traditional optimization approaches, particularly in derivative-free optimization, global optimization of nonconvex problems, and mixed-integer nonlinear programming. His work increasingly focuses on sustainable engineering applications, including rare earth element recovery, water network optimization, and perovskite solar cell design, reflecting a strong commitment to addressing contemporary engineering challenges. NSF CAREER award INFORMS Computing Society Prize Beale-Orchard-Hays Prize from the Mathematical Optimization Society Computing in Chemical Engineering Award Constantin Carathéodory Prize National Award and Gold Medal from the Hellenic Operational Research Society Member of the U.S. National Academy of Engineering Fellow of AIChE Fellow of INFORMS Dr. Sahinidis has secured substantial funding from the National Science Foundation, U.S. Environmental Protection Agency, and industry partners for his research. His group has developed several influential software tools including CMOS for protein structure alignment, GPU-BLAST for accelerated sequence alignment, R3 for protein side-chain conformation prediction, and SAS-Pro for protein structural alignment. The Sahinidis Optimization Group maintains active openings for graduate students and researchers nearly every year, fostering the next generation of optimization scientists. The Sahinidis Optimization Group at Georgia Tech is a leading research center in mathematical optimization and its applications. The group maintains strong collaborations with researchers across multiple disciplines and institutions, including the Hauptman-Woodward Medical Research Institute. Their work spans theoretical algorithm development to practical implementations in chemical engineering, bioinformatics, and materials science, with a consistent focus on developing rigorous, efficient methods for challenging optimization problems.

Jean-Philippe RichardView profile

Professor

• Mixed-integer linear programming (MILP)
• Mixed-integer nonlinear programming (MINLP)
• Convexification and polyhedral approaches
• +6 more

Jean-Philippe (JP) Richard is Professor of Industrial and Systems Engineering at the University of Minnesota, Twin Cities, and concurrently serves as Director of Faculty and Academic Affairs for the department. From 2018 onward he has held these roles, having previously been Professor (and PhD coordinator) at the University of Florida (2008-2018) and Assistant/Associate Professor at Purdue University (2002-2008). Education Ph.D. in Algorithms, Combinatorics, and Optimization, Georgia Institute of Technology, 2002 Bachelor of Engineering in Applied Mathematics, Université Catholique de Louvain, 1998 Research Interests Dr. Richard works at the intersection of theory and computation for mixed-integer linear and nonlinear optimization . His methodological focus spans convexification techniques , polyhedral approaches , lifting and cutting-plane methods , and global optimization algorithms . He translates these advances into impactful applications in data analytics , healthcare (radiation therapy & surgery scheduling) , railroad logistics , and infrastructure protection . Scientific Awards & Honors University of Florida Term Professorship (2017–2019) IIE Transactions Best Application Paper Award (2008) NSF CAREER Award (2004) Sigma Xi Best Ph.D. Thesis Award, Georgia Tech (2003) Russell J. Penrose ISyE Excellence in Teaching Award (2023) Grants & Funding Leadership Richard has been PI or co-PI on more than ten NSF awards totaling over $2 million, including recent grants on network interdiction, complementarity constraints, and opioid-policy analytics. Industry partnerships with Union Pacific Railroad and CSX Transportation have funded applied projects exceeding $150,000. Student Mentorship & Lab Culture He has (co-)advised 19 PhD dissertations and numerous Master’s and undergraduate researchers. Current advisees include Bouchra Er-Rabbany and Chengwenjian Wang; recent graduates hold positions at Google, ExxonMobil, Bayer, and leading universities worldwide. His group emphasizes rigorous theory coupled with real-world impact.

Marcel MongeauView profile

Professor

• Operations Research
• Global Optimization
• Numerical Optimization
• +5 more

Marcel Mongeau is a Professor in Operations Research at the École Nationale de l'Aviation Civile (ENAC) in Toulouse, France, affiliated with the OPTIM team under the MORO axis . His research focuses on operational research, global optimization, numerical optimization, and mixed-integer programming, with applications in aeronautics, chemical engineering, life sciences, and other industries. Education PhD in Combinatorics & Optimization (1991), University of Waterloo, Canada MSc in Mathematics (1987), University of Montreal, Canada BSc in Mathematics (1985), University of Montreal, Canada Habilitation to Direct Research (2003), Paul Sabatier University, France Research Interests : Mongeau's work addresses complex optimization problems through mathematical modeling and algorithm development. Key areas include aircraft trajectory planning, contrail avoidance, stochastic programming, and multi-modal transportation optimization. His methodologies integrate exact optimization, heuristic approaches, and computational modeling. Publications Trends : Recent publications emphasize air traffic flow management , contrail-safe trajectories , stochastic aircraft scheduling , and multi-modal transportation synchronization . Applications span environmental impact minimization, machining efficiency, and climate-aware aviation planning. Scientific Recognition : He is a Member of the Academy of Technologies , reflecting his contributions to applied mathematical sciences. Advising & Grants : Mongeau has supervised PhD students and postdoctoral fellows, particularly in optimization and operational research. His research has been supported through collaborations with institutions like IBM, INRIA, and CNRS. Labs & Teams : He is associated with the OPTIM team at ENAC and previously worked with the MOGISA group at LAAS-CNRS and the Optimisation & Interactions group at Paul Sabatier University.

Ionela ProdanView profile

Associate Professor

• Control Engineering
• Applied Mathematics
• Model Predictive Control
• +9 more

Ionela Prodan is an Associate Professor at the Laboratory of System Design and Integration (LCIS) , affiliated with Grenoble Institute of Technology, Université Grenoble Alpes, France. She is part of the CO4SYS research team and serves as deputy director of LCIS-Grenoble INP since 2021. Ph.D. in Control Engineering (2012, Supélec, France) HDR (Habilitation to Conduct Research, 2020) B.S. in Automation and Applied Informatics (2009, Univ. Politehnica of Bucharest, Romania) Research Interests focus on multi-disciplinary domains with expertise in control systems and applied mathematics. Her work spans: Control Theory: Model Predictive Control (MPC), distributed/decentralized approaches Optimization: Mixed-integer programming, set-theoretic methods Applications: Motion planning for autonomous vehicles, energy management in DC microgrids Teaching Activities include: Doctoral courses on constrained optimization-based control (English, EEATS doctoral school) Formation for engineers (Eramet Ideas, France) Courses in English/French for 3rd to 5th year students at Esisar and Ho Chi Minh Institute of Technology Leadership Roles: Vice-chair of the 2022 IFAC CAO organizing committee Associate Editor for EUCA European Control Conference (2022–present) and IEEE Transactions on Control Systems Technology (2023–present) Member of IFAC Technical Committee 6.3 Power and Energy Systems (2020–present) Co-animator of the CT CPNL French research group on Nonlinear Model Predictive Control (2021–present)

Marco LübbeckeView profile

Professor

• Operations Research
• Mixed Integer Programming
• Branch-and-Price Algorithms
• +3 more

Marco Lübbecke is a Professor at the Faculty of Business and Economics of RWTH Aachen University , Germany. He serves as the chairholder of the Lehrstuhl für Operations Research and holds the position of Studiendekan (Dean of Studies) for his faculty. His professional contact is marco.luebbecke@rwth-aachen.de, with additional administrative contact at luebbecke@or.rwth-aachen.de. Current academic rank: Professor Faculty: Business and Economics Department: Operations Research Marco Lübbecke's research focuses on Operations Research and Mixed Integer Programming . He develops and analyzes algorithms like Branch-and-Price , Dantzig-Wolfe Reformulation , and Decomposition Methods to solve complex optimization problems in industrial, transportation, and political science applications. Core research areas: Combinatorial Optimization, Mathematical Programming Application domains: Logistics, Rail Transport, Redistricting, Manufacturing Systems Methodological interests: Column Generation, Cutting Planes, Algorithm Engineering His recent publications highlight advancements in optimization software frameworks like the SCIP Optimization Suite and algorithmic techniques for solving large-scale linear and integer programs. Key research trends include automated decomposition methods, structural analysis of MIPs, and hybrid approaches combining classical optimization techniques with machine learning insights. Marco Lübbecke actively participates in academic conferences and serves as a co-author in numerous technical reports and journal publications, contributing to the development of efficient optimization algorithms and their practical implementation.

Robert HildebrandView profile

Assistant Professor

• Mixed Integer Nonlinear Programming
• Integer Programming
• Operations Research
• +6 more

Robert Hildebrand is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. in Applied Mathematics from the University of California, Davis (2013). His research focuses on Mixed-Integer Nonlinear Programming, Integer Programming, Complexity Theory, and Redistricting Analytics, with applications in Operations Research and Discrete Geometry. He has received grants from the Air Force Office of Scientific Research and the Office of Naval Research, and the 2019 Sporn Teaching Award. Education: Ph.D., Applied Mathematics (UC Davis, 2013); B.Sc., Mathematics (University of Puget Sound, 2008). Professional history includes postdoctoral roles at ETH Zurich (2013–2015), IBM Watson Research (2015–2017), and a Simons Institute Fellowship (2017). Current roles include Associate Editor for Discrete Optimization and service on INFORMS committees. Research interests emphasize theoretical and computational aspects of optimization, with recent work on gerrymandering analysis, robotic assembly optimization, and algorithmic complexity bounds. Notable publications include advancements in integer programming formulations and scheduling algorithms for autonomous systems. Grants include Virginia Tech's Whole Health Consortium (2024) for veterans' healthcare optimization and an ICTAS Seed Grant for autonomous fleet algorithms (2023). He advises students on topics like rectangle packing and robotic trajectory planning, with former students advancing to doctoral and industry roles. Labs/Teams: Active in Virginia Tech's FASER Lab (robotics optimization) and collaborates with interdisciplinary groups on redistricting analytics and space exploration technologies.

George PapafotiouView profile

Professor

• Power Electronics
• Model Predictive Control (MPC)
• Renewable Energy Systems
• +3 more

George Papafotiou is a Full Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e) and Head of the Power Electronics Lab. His research focuses on computational intelligence, particularly Model Predictive Control (MPC), applied to power electronics systems for applications in renewable energy, high-efficiency power conversion, and grid integration. He holds affiliations with the EIRES Research Institute and the Cyber-Physical Systems Center in Eindhoven. Academic Background: Papafotiou earned his PhD in Electrical Engineering from Aristotle University of Thessaloniki (2002) and completed a postdoc at ETH Zurich (Switzerland). He spent 15 years at ABB in roles including Scientist, Global R&D Manager for SW/Control HW, and Quality Manager, leading projects on MPC-based control systems for industrial drives and energy management. Research Interests: His work centers on advancing power electronics through MPC for microsecond-scale control, enabling reliable systems critical to a carbon-free future. Key areas include modular multilevel converters (MMCs), soft-switching techniques, grid-tied power electronics, and harmonics mitigation in high-voltage systems. Projects: Current projects include RelSST: Developing reliable solid-state transformers for smart grids. HiVECAF: Designing versatile, efficient active filters for power quality improvement. Teaching: He teaches Electric Circuits Analysis for Energy Conversion Electric Drive Systems Grants & Labs: Leads the Power Electronics Lab, focusing on applied research in sustainable energy systems and industrial automation.