جست‌وجوی استاد و پژوهشگر

Jean-Philippe Richardمشاهده پروفایل

استاد

• Mixed-integer linear programming (MILP)
• Mixed-integer nonlinear programming (MINLP)
• Convexification and polyhedral approaches
• +۶ مورد دیگر

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.

Sonia Cafériمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Nonlinear Optimization
• +۳ مورد دیگر

Sonia Caféri is a Professor at the French Civil Aviation University (ENAC), where she is affiliated with the OPTIM research team within the MORO (Models and Optimization) research axis. Her research expertise spans Operations Research and Optimization, with specialized focus on Continuous and Mixed Integer Nonlinear Optimization and Global Optimization techniques. She applies these methodologies to solve complex problems in Traffic Management and Air Transport systems, particularly addressing air traffic management challenges through advanced mathematical modeling. Professor Caféri's work within ENAC's MORO axis contributes to the institution's mission in civil aviation innovation, developing optimization frameworks that enhance efficiency and safety in transportation networks through rigorous operational research approaches.

Marcel Mongeauمشاهده پروفایل

استاد

• Operations Research
• Global Optimization
• Numerical Optimization
• +۵ مورد دیگر

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 Prodanمشاهده پروفایل

دانشیار

• Control Engineering
• Applied Mathematics
• Model Predictive Control
• +۹ مورد دیگر

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 Lübbeckeمشاهده پروفایل

استاد

• Operations Research
• Mixed Integer Programming
• Branch-and-Price Algorithms
• +۳ مورد دیگر

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 Hildebrandمشاهده پروفایل

استادیار

• Mixed Integer Nonlinear Programming
• Integer Programming
• Operations Research
• +۶ مورد دیگر

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 Papafotiouمشاهده پروفایل

استاد

• Power Electronics
• Model Predictive Control (MPC)
• Renewable Energy Systems
• +۳ مورد دیگر

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.

Chandra Mouli R. Madhuranthakamمشاهده پروفایل

دانشیار مدعو

• Polymer Reaction Engineering
• Process Modeling and Optimization
• Static Mixers and Microreactor Technology
• +۴ مورد دیگر

Chandra Mouli R. Madhuranthakam is an Adjunct Associate Professor and Lecturer in the Department of Chemical Engineering at the University of Waterloo. He is a Professional Engineer in Ontario and has taught at the UWaterloo campus in the United Arab Emirates. His research focuses on polymer reaction engineering, process optimization, static mixers, and microreactor technology, with additional interests in waste minimization and applied statistics. Education & Background: Holds a PhD in Chemical Engineering, evidenced by his 2007 doctoral thesis on static mixer reactor design. His academic career includes roles in both teaching and applied research. Research Interests: Dr. Madhuranthakam explores microfluidic reactors for biodiesel synthesis, mixed-integer nonlinear programming, and optimal control of biochemical systems. His work bridges theoretical modeling with industrial applications, emphasizing sustainability through green engineering practices. Publication Trends: His articles from 2016–2007 highlight advancements in emulsion copolymerization, Bayesian experimental design, and polymer nanocomposite optimization. Recent studies focus on sustainable processes and reactor performance validation. Advising & Grants: Currently accepting graduate students but no listed advisees. No specific grants mentioned, though his research aligns with industrial process improvement themes. Labs/Teams: Collaborates with interdisciplinary teams in polymer engineering and process modeling, though specific lab affiliations are not detailed.

Matthias Walterمشاهده پروفایل

استادیار

• Operations Research
• Optimization
• Mathematical Programming
• +۵ مورد دیگر

Matthias Walter is an Assistant Professor in the Department of Mathematics of Operations Research. His research focuses on optimization, mathematical programming, and algorithm design, with applications in transportation, combinatorial optimization, and logistics. He contributes to the SCIP Optimization Suite and has developed the Combinatorial Matrix Recognition Library. His work spans topics such as linear programming relaxations, pseudo-Boolean solving, and multilinear optimization. Key research interests include operations research, integer programming, and algorithmic solutions for complex optimization problems. He has collaborated on projects like optimizing parcel transportation and analyzing congestion games. His recent publications address challenges in flow shop scheduling, graph realization, and pseudo-Boolean functions. Walter actively participates in academic activities, organizing events like the Mathematical Olympiad in Saxony-Anhalt and presenting invited talks on topics such as McCormick relaxations. His contributions to computational tools and theoretical advancements highlight his role in advancing optimization methodologies.

Shaoning Hanمشاهده پروفایل

استادیار

• Mathematical Optimization
• Mixed-Integer Nonlinear Programming
• Nonsmooth Optimization
• +۳ مورد دیگر

Shaoning Han is a Presidential Young Professor (Assistant Professor) in the Department of Mathematics and the Institute of Operations Research and Analytics (IORA) at the National University of Singapore (NUS). He previously held a postdoctoral position at the University of Southern California (USC), working under Prof. Jong-Shi Pang. His research focuses on mathematical optimization, including mixed-integer nonlinear programming, nonsmooth optimization, and their applications in operations research and data science. Han earned his Ph.D. in Industrial Engineering from USC under Prof. Andrés Gómez and a B.S. in Mathematics from the University of Science and Technology of China (USTC). His work emphasizes advancing optimization methodologies, particularly for problems involving complementarity constraints, indicator variables, and sparsity. Recent contributions include progressive MIP methods for indefinite QPs, real-time solutions for banded matrix problems, and convexification techniques for nonconvex optimization. Han is actively recruiting PhD students interested in mathematical optimization. Research Highlights: His articles span theoretical advances (e.g., epi-stationarity analysis, parametric variational inequalities) and applied frameworks (e.g., robust SVMs, binarized neural networks). Key themes include polynomial-time solvability of MRFs, convex relaxations for low-rank functions, and sparse signal estimation. Awards: Third Place, INFORMS JFIG Paper Competition (2023) Journal of Global Optimization Best Paper Honorable Mention (2023) Teaching: Courses include MA5243 (Advanced Mathematical Programming) and MA3252 (Linear and Network Optimization). Labs/Teams: Active member of IORA, NUS’s Smart Nation Research Cluster.

• Operations Research
• Optimization
• Mathematical Programming
• +۵ مورد دیگر

Marianna De Santis is an Associate Professor in the Department of Information Engineering (Dipartimento di Ingegneria dell’Informazione) at the University of Florence, Italy. She previously held positions at Sapienza University of Rome and other institutions. Her research focuses on optimization, particularly in multiobjective programming, mixed-integer programming, and algorithm design. She has authored numerous articles in top journals and developed software tools like BBMOQIP and MOMIX for solving complex optimization problems. Education : PhD in Operations Research, Sapienza University of Rome (2012) MSc in Applied Mathematics (2007), BSc in Mathematics (2005) Research Interests : Dr. De Santis specializes in optimization theory and applications, including multiobjective optimization, integer programming, and algorithm design. Her work bridges theoretical advancements with practical implementations, addressing challenges in machine learning, bioinformatics, and operations research. Key areas include semidefinite programming, robust optimization, and exact methods for complex combinatorial problems. Articles Trends : Her recent work emphasizes multiobjective optimization, with contributions to hyperparameter optimization, robust combinatorial methods, and sustainable modeling. Publications span journals like Journal of Global Optimization and Mathematical Programming , reflecting her expertise in algorithmic innovation and mathematical rigor. Grants & Teaching : She leads projects on augmented Lagrangian methods and multiobjective integer optimization. Teaching includes courses on operations research, optimization, and data science at the undergraduate and graduate levels. She has also organized international workshops and served on conference committees. Labs/Teams : Her research group develops optimization software (e.g., BBMOQIP) and collaborates internationally on projects involving algorithm design and real-world applications.

• Operations Research
• Game Theory
• Optimization
• +۳ مورد دیگر

Julio González Díaz holds the position of General Coordinator of Doctorate and USC at the University of Santiago de Compostela , within the Department of Statistics, Mathematical Analysis and Optimization . His work focuses on advancing mathematical methodologies in optimization, game theory, and operations research, with notable contributions to polynomial optimization, energy network modeling, and algorithmic development. He oversees doctoral programs and contributes to academic governance at the university level. His research interests span game theory (especially cooperative and non-cooperative frameworks), optimization techniques (including global and polynomial optimization), and applied modeling in energy systems and supply chain networks. He has developed algorithms like RAPOSa for polynomial optimization and explored applications such as gas transmission networks and portfolio design. Publications emphasize computational efficiency in optimization, fairness in resource allocation , and interdisciplinary applications of game theory. His work bridges theoretical foundations with practical implementations, as seen in his contributions to open-source solvers and algorithmic advancements. He has received institutional recognition, including an Excellence Award in doctoral program administration. His academic leadership includes coordinating master's and PhD programs in statistical techniques and operations research, fostering educational innovation and international collaborations.

Amaya Nogales Gómezمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Machine Learning
• Telecommunications
• +۳ مورد دیگر

Amaya Nogales Gómez is an Associate Professor at the Interdisciplinary Institute for Artificial Intelligence (3IA) at Université Côte d’Azur and a member of the Wimmics Research Team. Her research focuses on combinatorial optimization, machine learning, and their applications in telecommunications and operations research. She holds a Ph.D. in Mathematics from the University of Seville (2015) and has held roles including Postdoctoral researcher at I3S and researcher at Huawei France (2014–2019). Education: PhD in Mathematics, University of Seville (2015); Postdoctoral studies at I3S, Sophia Antipolis. Her research interests include: Optimization algorithms for resource allocation and network design Machine learning techniques for pattern recognition and classification Applications in mobile video streaming and radio resource management Key research areas span telecommunications optimization, data-driven decision making, and mathematical modeling. She has contributed to patents and publications in top-tier conferences/journals. Current openings: See internship on Fairness-aware NLP here .

• Applied Mathematics
• Graph Theory
• Combinatorics
• +۳ مورد دیگر

Prof. Angelika Wiegele is a full Professor at the Department of Mathematics at Alpen-Adria-Universität Klagenfurt. She serves as a member of the university senate and head of the Institut für Mathematik. Her research focuses on semidefinite optimization, combinatorial optimization, and their applications in graph theory and operations research. She holds a Dipl.-Ing. (2001) and Dr. techn. (2006) from Alpen-Adria-Universität Klagenfurt, with thesis supervision by Franz Rendl. Her academic career includes positions at TU Graz, IASI-CNR Rome, and Universität zu Köln, as well as a visiting professorship at Università di Roma Tor Vergata. Her educational background includes studies at City University London (Erasmus) and TU Eindhoven (Leonardo project). Recent work emphasizes SDP-based methods for graph partitioning, edge expansion, and combinatorial problems, with a focus on high-performance computing solutions. Her research bridges theoretical optimization with practical applications in network design and algorithmic development. Publications from 2021–2024 highlight advancements in semidefinite programming for graph partitioning, SDP-based bounds for cutwidth, and exact solvers for clustering problems. She contributed to the BiqBin and SOS-SDP solvers for quadratic optimization and co-edited special journal issues on mixed-integer nonlinear optimization. Her work often appears in top operations research journals and conference proceedings. Prof. Wiegele has held global faculty positions at the University of Cologne (2022–2024) and is active in international research collaborations through PRACE high-performance computing initiatives. She maintains an ORCID profile and a university webpage with teaching and research materials.

Yossiri Adulyasakمشاهده پروفایل

استاد

• Supply Chain Analytics
• Optimization under Uncertainty
• Large-Scale Optimization
• +۴ مورد دیگر

Yossiri Adulyasak is a Full Professor in the Department of Logistics and Operations Management at HEC Montréal, holding the Canada Research Chair in Supply Chain Analytics. He is also an Adjunct Professor at the University of Montreal's Department of Computer Science and Operations Research and a scientific advisor at IVADO Labs. His research focuses on supply chain optimization, data-driven decision-making, and robust optimization under uncertainty, with applications in retail, transportation, and humanitarian logistics. Education: PhD in Operations and Logistics Management (HEC Montréal), Master of Civil Engineering in Transportation Engineering (Chulalongkorn University). Research Interests : Supply chain analytics, large-scale optimization algorithms, stochastic programming, machine learning integration, and resilient supply chain design. His work emphasizes practical applications in logistics networks, inventory management, and dynamic resource allocation under uncertainty. Recent Articles : Focus on AI-driven retail analytics, robust optimization for lot-sizing and facility location, and humanitarian logistics. Key topics include pandemic response strategies, electric vehicle routing, and drone delivery systems. Awards : Recipient of the 2021 Prix de recherche Chenelière Éducation/Gaëtan Morin for excellence in scientific publications. Supervision : Directed 3 PhD dissertations and over 40 master’s and project supervisions, collaborating with companies like Bombardier, Pratt & Whitney Canada, and JDA. Active in industrial partnerships for supply chain optimization solutions. Lab/Teams : Member of GERAD (Group for Research in Decision Analysis) and IVADO Labs, contributing to cross-disciplinary AI and operations research initiatives.