Massimo Pappalardo is a Full Professor in the Department of Computer Science at the University of Pisa. His research focuses on Operations Research, particularly in logistics optimization, mathematical programming, and equilibrium problems. He is actively involved in teaching, including courses on Operations Research for Computer Engineering and logistics applications for Master's programs in Management and Control of Logistics Systems. His work bridges theoretical advancements and practical applications in optimization algorithms, variational inequalities, and stochastic modeling. His academic contributions span over three decades, emphasizing nonlinear programming, variational analysis, and equilibrium models. He has authored or co-authored numerous papers in top journals and conference proceedings, addressing topics like lexicographic multi-objective optimization, grossone methodology, and network equilibrium models. His research frequently intersects with computational methods and real-world problems in logistics and energy systems. Teaching materials and reception schedules are managed via the university's teaching teams portal. No specific awards are mentioned in the provided texts, but his extensive publication record reflects significant scholarly impact in optimization and operations research.
Kimberly Yu is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal’s Faculty of Arts and Sciences. Her research focuses on nonlinear programming, theoretical computer science, and optimization theory. She leads major research projects on mixed-integer nonlinear programming, funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). Her work bridges foundational algorithmic research and real-world applications, particularly in submodular optimization and computational methods. Dr. Yu’s academic background positions her at the intersection of computer science and mathematics. She teaches advanced courses such as IFT1575 (Operational Research Models) and IFT6551 (Integer Programming). Her research explores cutting-edge topics like DR-submodular minimization, integral invariants in computer vision, and polyhedral approaches to combinatorial optimization. Her research grants emphasize innovation in discrete optimization, with projects like "Theory and Algorithms for Mixed-Integer Nonlinear Programming" (2024–2030) advancing computational methods for complex decision-making systems. Kimberly Yu collaborates actively with researchers in computer science and operations research, contributing to both theoretical advancements and practical applications in fields like algorithm design and machine learning.
Mihai Anitescu is a Senior Computational Mathematician in the Laboratory for Advanced Numerical Software (LANS) within the Mathematics and Computer Science Division at Argonne National Laboratory, a position he has held since 2002. He is also a part-time Professor in the Department of Statistics at the University of Chicago since 2009 and an adjunct Associate Professor in the Mathematics Department at the University of Pittsburgh. Additionally, he is a Senior Fellow of the Computation Institute, a joint Argonne-University of Chicago initiative. He leads the MACSER (MultiTimescale Control of Electric Power Systems) project and previously led the M2ACS project. Ph.D., Applied Mathematical and Computational Sciences, University of Iowa, 1997 Electrical Engineer, Polytechnic University of Bucharest, Romania, 1992 Dr. Anitescu’s research focuses on numerical optimization, uncertainty quantification, and numerical analysis, with applications spanning nuclear engineering, electric power grids, chemical engineering, materials science, biology, mechanical engineering, and robotics. His work develops scalable computational methods for complex systems, particularly leveraging high-performance computing. He has made significant contributions to optimization under uncertainty, stochastic programming, Gaussian process modeling, and simulation of multibody dynamics with contact and friction using differential variational inequalities. His recent publications (2020–2023) highlight a strong and consistent trend in applying advanced mathematical and computational techniques to critical energy infrastructure, particularly the electric power grid. Key themes include stochastic optimization for optimal power flow under uncertainty, risk assessment through extreme event simulation, frequency prediction and estimation using spatiotemporal and Bayesian methods, and the simulation of cascading failures. His work bridges core mathematical advances in optimization, sensitivity analysis, and scalable Gaussian process computation with high-impact applications in grid stability, reliability, and control. Dr. Anitescu is a senior editor of Optimization Methods and Software and a member of the editorial boards of Mathematical Programming and the SIAM Journal on Optimization . He has previously served on the editorial boards of the SIAM Journal on Scientific Computing and the SIAM/ASA Journal on Uncertainty Quantification . He is a dedicated mentor, having advised numerous postdoctoral fellows, Ph.D. students, and M.S. students at Argonne, the University of Chicago, and the University of Pittsburgh. His advisees have gone on to successful careers in national laboratories, academia (e.g., UC Santa Barbara, Purdue, University of Wisconsin), and industry (e.g., Amazon, Citibank, Morgan Stanley, IBM). He has secured and led significant research grants through projects like MACSER and M2ACS, which focus on the mathematical challenges of managing complex, uncertain energy systems. His work is highly collaborative, involving partnerships across institutions and disciplines. Dr. Anitescu leads the MACSER project, a major research initiative focused on developing mathematical and computational tools for the multi-timescale control of electric power systems. This work is central to ensuring the stability and reliability of modern power grids, especially as they integrate increasing amounts of renewable energy.
Andrew C. Trapp is a Full Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI), with courtesy appointments in Mathematical Sciences, Data Science, and Computer Science. He holds a Ph.D. in Industrial Engineering from the University of Pittsburgh. His research focuses on applying optimization, machine learning, and data science to address societal challenges such as refugee resettlement, human trafficking prevention, and child welfare. His work is supported by NSF grants and private foundations. Education: PhD in Industrial Engineering, University of Pittsburgh (2011) Research Interests: Integer optimization, machine learning, refugee placement systems, human trafficking intervention, healthcare operations, and nonprofit resource sharing. Trapp leads the ARCHES initiative, developing tools like Annie ™ MOORE for refugee resettlement and SWAP for nonprofit resource exchange. His work has been featured in Operations Research , Production and Operations Management , and Nature -affiliated journals. He is a past president of the INFORMS Public Sector Operations Research section and has received accolades such as the Chief Data Officer Magazine’s Academic Data Leader (2021). His grants include NSF awards for refugee data collection (CMMI-2233377), nonprofit resource sharing (FW-HTF-2222713), and societal impact (CIVIC-PG-2431414). He advises students in PhD, MS, and undergraduate programs, emphasizing real-world impact and interdisciplinary collaboration.
Mahdi Moeini is an Associate Professor in Operations Research and Machine Learning at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise), affiliated with the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. His research focuses on optimization techniques, including DC programming, combinatorial optimization, and their applications in logistics, healthcare, and finance. Academic Roles: Associate Professor (2022–present), Adjunct Lecturer (2014–2022) at TU Kaiserslautern, Germany. Research Expertise: Portfolio optimization, vehicle routing with drones, emergency medical systems, and metaheuristics. Key Contributions: Published 14 journal papers, 24 conference chapters, and 8 technical reports, with a Habilitation in 2018. Research Interests: Combinatorial optimization, mathematical programming, sustainability in digitization, and machine learning applications. Teaching: Courses include Operations Research, Data Science, Computational Intelligence, and Financial Data Analysis.
Magnus Stålhane is a Professor at the Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU). His research focuses on Operations Research , Maritime Logistics , Vehicle Routing , and Optimization , with applications in offshore wind farms, liner shipping, and military logistics. He has published extensively in journals like Transportation Science , European Journal of Operational Research , and Journal of Heuristics . Recent works include Electric Vehicle Routing with heterogeneous recharging technologies, Maritime Fleet Composition under emission restrictions, and Inventory Routing with time-varying demands. His methodologies span Branch-Price-and-Cut , Matheuristics , and Stochastic Programming . Collaborations include researchers from NTNU, Norwegian institutions, and international teams.
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.
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. 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.
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 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.
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.
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.
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 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.
