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Madeleine UdellView profile

Assistant Professor

• Optimization
• Machine Learning
• Data Analysis
• +4 more

Madeleine Udell is an Assistant Professor of Management Science and Engineering at Stanford University, with affiliations at the Institute for Computational and Mathematical Engineering (ICME) and a courtesy appointment in Electrical Engineering. She holds a PhD from Stanford University in Computational and Mathematical Engineering and completed a postdoc at Caltech. Her research focuses on accelerating optimization and machine learning, with applications in healthcare, finance, and engineering. Udell's work bridges algorithmic efficiency, interpretability, and practical deployment in data-driven systems. Education: BS in Mathematics and Physics, Yale University (2009) PhD in Computational and Mathematical Engineering, Stanford University (2015) Research Interests: Her research emphasizes optimization algorithms, scalable machine learning techniques, and their applications in real-world domains. Key areas include low-rank matrix methods, automated machine learning (AutoML), and interpretable models for healthcare. She develops tools like OptiMUS for natural language-based optimization modeling and explores second-order methods for improving deep learning efficiency. Awards: Kavli Fellowship (2023) Alfred P. Sloan Research Fellowship (2021) NSF CAREER Award (2020) ONR Young Investigator Award (2020) Grants & Advising: Udell leads projects funded by ONR and NSF, focusing on randomized numerical linear algebra and automated machine learning. She advises PhD students in optimization modeling, deep learning, and causal inference. Her group collaborates on tools like PINNs for scientific computing and OpenReviewer for bias-aware academic reviews. Labs & Teams: She directs the Udell Research Group at Stanford, emphasizing interdisciplinary work in optimization, machine learning, and data science. Collaborations span academia and industry, including work with Aurora Solar on photovoltaic array design and Google Brain on scalable AutoML.

• Operations Research
• Optimization
• Mathematical Programming
• +5 more

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.

Maurizio BocciaView profile

Associate Professor

• Operations Research
• Optimization
• Logistics
• +5 more

Maurizio Boccia is an Associate Professor at the University of Naples Federico II, affiliated with the Department of Electrical Engineering and Information Technologies. His research focuses on optimization methodologies and their applications in logistics, transportation systems, and healthcare management. He holds a Ph.D. in Operations Research and has contributed extensively to the development of exact and heuristic algorithms for complex scheduling and routing problems. Key research interests include combinatorial optimization, vehicle routing problems (e.g., TSP variants with drones), automated guided vehicle (AGV) scheduling, and optimization in healthcare settings. He has also explored educational strategies, such as the OPS4Math project, which integrates optimization techniques into mathematics education. His work frequently involves mixed-integer linear programming (MILP), branch-and-cut algorithms, and matheuristics. Notable applications include railway dispatching for the Union Pacific Railroad, operating room planning in hospitals, and logistics network design. He has collaborated on real-world projects, such as improving exam scheduling in Norway and optimizing postal collection routes in rural areas. Boccia’s publications span over two decades, with contributions to both theoretical advancements (e.g., lifted cover inequalities, Fenchel cuts) and practical implementations (e.g., web cache location models, traffic sensor placement). His research often bridges theoretical optimization frameworks with real-world operational challenges.

Jacques DesrosiersView profile

Professor

• Operations Research
• Transportation
• Optimization
• +3 more

Jacques Desrosiers is a Full Professor at HEC Montréal in the Department of Management Science since 1989. He has been with the institution since 1978 and is a founding member of the Group for Research in Decision Analysis (GERAD). His research focuses on optimization, transportation systems, and algorithmic methods for solving complex linear and combinatorial problems. He has received prestigious recognitions, including the Grand Prize for Excellence in Research (2015) and Fellowship in the Royal Society of Canada (2001). His work spans contributions to column generation techniques, degenerate linear programming, and vehicle routing problems. Notable achievements include advancing the minimum mean cycle-canceling algorithm, vector space decomposition for large-scale problems, and developing efficient heuristics for logistics challenges. His research bridges theoretical advancements and practical applications, influencing transportation and operations management globally. Desrosiers has authored over 150 peer-reviewed articles, with recent focus on algorithmic optimization, primal degeneracy handling, and decomposition methods. His contributions to software tools like COIN-OR’s CLP further highlight his commitment to advancing computational methods in operations research. Awards: Royal Society of Canada Fellowship (2001), HEC Montréal Grand Prize (2015) Key Research Themes: Column Generation, Network Flow Algorithms, Linear Programming, Vehicle Routing, Degeneracy Resolution Labs/Teams: Co-founder and active member of GERAD, collaborating on optimization projects

• Operations Research
• Game Theory
• Optimization Theory
• +5 more

Immanuel Bomze is a Full Professor of Applied Mathematics and Statistics at the University of Vienna, affiliated with the Institute of Statistics and Operations Research and the Data Science Research Center. His research focuses on Operations Research, Game Theory, Optimization, and their applications in areas like astronomy and logistics. He holds the EurOpt Fellow distinction (2014) and has received multiple best paper awards. Bomze has authored over 120 publications, including work on quadratic optimization, evolutionary game dynamics, and stochastic modeling. He serves as Editor-in-Chief of the EURO Journal of Computational Optimization and has held leadership roles in organizations like EURO. His teaching includes advanced optimization courses and PhD seminars. Notable contributions include methods for cluster detection in networks and robust optimization frameworks. Education 1982: Dr. rer. nat (PhD equivalent) in Mathematics, University of Vienna 1981: Mag. rer. nat (MSc equivalent) in Mathematics and Physics, University of Vienna Research Interests include: Optimization Theory, Game Theory applications, Stochastic Modelling, Dynamical Systems, and Data Science. His work bridges theoretical mathematics with real-world problems in logistics, astronomy, and decision theory. Key Achievements: Developed evolutionary game dynamics models for equilibrium analysis. Contributed to copositive programming and robust optimization methodologies. Co-founded the GAMENET European Network for Game Theory. Recipient of the Best Paper Award in Mathematical Methods of Operations Research (2020) and other journals. Grants & Collaborations: Extensive collaborations with institutions worldwide, including the International Institute for Applied Systems Analysis (IIASA). Research funded by grants from NSF, NWO, and others. Labs/Teams: Active in the Vienna Center for Operations Research (VCOR) and the Data Science Research Group at the University of Vienna.

Fabrizio RossiView profile

Professor

• Integer Programming
• Combinatorial Optimization
• Network Optimization
• +8 more

Fabrizio Rossi is a Full Professor of Operations Research at the University of L'Aquila, Department of Information Engineering, Computer Science and Mathematics. He has been a member of the Board of Administration at the university since 2019 and previously from 2010 to 2012. His academic career spans over two decades, including roles as Associate Professor (2005–2019) and Assistant Professor (1997–2002). Education: Ph.D. in Operations Research, University of Rome 'La Sapienza' (1996) Laurea Degree in Electrical Engineering, University of Rome 'La Sapienza' (1992) Visiting Student in IEOR at Columbia University, New York (1996) Operations Research School, Scuola di Matematica Interuniversitaria (1996) Research Interests: Fabrizio Rossi specializes in large-scale optimization methodologies, including Integer Programming and Combinatorial Optimization. His work addresses complex applications in telecommunications network design, manufacturing process optimization, logistics systems, and healthcare. Recent innovations include algorithms for DNA sequence optimization and tumor classification using machine learning. He focuses on practical solutions through advanced techniques like lift-and-project operators and robust optimization frameworks, with significant contributions to scheduling and resource allocation in call centers and satellite missions. Scientific Awards: Informs Computing Society Prize (2014) (collaborative with Jim Ostrowski, Jeff Linderoth, Stefano Smriglio) Finalist, Euro Excellence in Practice Award (2006) for research on terrestrial broadcasting migration Advising and Grants: As a project leader, he coordinated major initiatives such as: PRIN 2010–2012 : Integer Programming methods for radio transmission networks 2008–2009 : Analog-to-digital broadcasting migration optimization IST SAILOR Project (2002–2005) : Satellite UMTS emulation systems He also contributed to European Space Agency's MAS Project (2004–2006) and collaborated with Italian regulatory bodies on telecommunication projects. His research portfolio includes over 50 projects since 1993, emphasizing real-world applications in manufacturing, logistics, and healthcare systems. Labs/Teams: His research is conducted within the Department's optimization groups and industry partnerships. Collaborations include ESA satellite scheduling, RAIWay frequency assignment, and healthcare institutions for treatment planning systems.

Chase MurrayView profile

Associate Professor

• Operations Research
• Autonomous Vehicles
• Vehicle Routing & Logistics
• +5 more

Chase Murray is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (SUNY). He directs the SOAR Drone Facility and Optimator Lab , focusing on operations research applications for autonomous systems. His work includes drone logistics, routing optimization, and AI integration in delivery networks. Education: PhD, Industrial & Systems Engineering, University at Buffalo, 2010 ME, Industrial Engineering, Texas A&M University, 2000 BS, Industrial Engineering, Texas A&M University, 1998 Research Interests: Dr. Murray’s research emphasizes leveraging autonomous vehicles for logistics and surveillance. Key areas include: - Routing algorithms for drone-truck systems - Weather and traffic impact modeling - Integer programming for problem classification - FAA-certified drone pilot with hands-on SOAR facility testing . Publications: Over 30 peer-reviewed articles focus on drone coordination, vehicle routing, and optimization frameworks. Recent work includes graph-based ILP classification and weather-aware delivery systems. Labs & Software: SOAR : Drone test facility with 3D digital twin simulations Optimator Lab : Develops tools like VeRoViz (routing visualization) and tex2solver (LaTeX-to-code translator) Open-source projects: Ground Control Station software, ROS-enabled web interfaces Grants & Advising: Advised PhD students Ritwik Raj (traffic congestion modeling) and Lan Peng (weather-impact scheduling). Active in collaborative projects on AI-driven logistics systems.

Yves LucetView profile

Professor

• Computational Mathematics
• Optimization
• Convex Analysis
• +6 more

Yves Lucet is a Professor in the Department of Computer Science, Mathematics, Physics and Statistics at the University of British Columbia Okanagan. He holds a PhD from the University of Toulouse. His research spans computational mathematics, optimization, and convex analysis, with a focus on algorithm development for computer-aided convex analysis and road design optimization. He leads the development of the CCA (Computational Convex Analysis) toolbox under Scilab, emphasizing hybrid symbolic-numerical algorithms. His work includes visualizing set-valued operators using Virtual Reality (VR) and collaborating on large-scale road design projects funded by NSERC, aiming to minimize construction costs while meeting safety and environmental constraints. Lucet advises graduate students and contributes to interdisciplinary teams involving mathematics, statistics, and engineering. His research also explores applications in computational geometry, image processing, and network communication. Education: PhD in Mathematics from the University of Toulouse. Research Interests: Computational Convex Analysis Algorithm Design for Optimization VR Visualization of Mathematical Operators Multi-objective Road Design Optimization Data-Driven Process Monitoring Key Projects: Development of the CCA toolbox, road design optimization funded by NSERC, and reinforcement learning approaches for prescriptive process monitoring. His work bridges mathematics and computer science, with applications in engineering and transportation.

• Discrete Optimization
• Non-Convex Optimization
• Operational Research
• +3 more

Akshay Gupte is a Professor in the School of Mathematics at the University of Edinburgh, specializing in Optimization and Operational Research. His research focuses on mathematical and algorithmic aspects of discrete and non-convex optimization, with applications in engineering, finance, and decision-making systems. He holds a PhD in Operational Research from Georgia Tech and teaches advanced courses such as Optimization Methods in Finance for MSc students. Education: BEng in Industrial Engineering from the University of Bombay MSc in Operational Research from the University of Arizona PhD in Operational Research from Georgia Tech Research Interests: Optimization problems involving discrete choices and non-convexity, with a focus on algorithm design and computational strategies. His work bridges applied mathematics, computer science, and engineering, addressing challenges in logistics, finance, and large-scale decision systems. Articles Trends: Recent publications emphasize algorithmic advancements in non-convex optimization (e.g., spatial branch-and-bound, semidefinite programming), multi-objective decision-making (e.g., biobjective programming), and stochastic frameworks for real-world problems like facility location and home service scheduling. Awards & Grants: No specific awards listed, but his research has been supported by collaborative grants in mixed-integer programming and optimization algorithms. He advises students through his teaching and research collaborations within the Optimization Group at the University of Edinburgh.

Purushothaman DamodaranView profile

Professor

• Industrial Engineering
• Systems Engineering
• Operations Research
• +7 more

Purushothaman Damodaran is a Presidential Teaching Professor at the Department of Industrial and Systems Engineering at Northern Illinois University . With expertise in large-scale optimization , logistics , simulation , scheduling , Lean Manufacturing , and Six Sigma , he develops mixed-integer linear formulations and novel algorithms for real-world applications in electronics manufacturing, healthcare, and logistics. Ph.D. , Industrial Engineering, Texas A&M University (2002) M.S. , Industrial Engineering, Northern Illinois University (1997) B.E. , Mechanical Engineering, University of Madras (1994) His research focuses on optimization models for batch processing machines , parallel machine scheduling , and simulation-based decision support systems . He has collaborated with industries like 3M , Caterpillar , Royal Caribbean Cruise Lines , and IBM , addressing challenges in manufacturing systems , warehouse design , and service operations . Recent publications highlight heuristic approaches (e.g., GRASP , Simulated Annealing , Particle Swarm Optimization ) for flow shop scheduling , job shop optimization , and material slitting problems . His work often integrates Lean Six Sigma principles and simulation-optimization for real-time manufacturing improvements . Scientific Awards : Best Paper Award, IISE Annual Conference (2021) National Science Foundation Grant (2008-2009) for server manufacturing research As a faculty teaching mentor at NIU, he teaches undergraduate and graduate courses in Six Sigma , Warehousing , and Advanced Lean Manufacturing , emphasizing real-life projects and student engagement .

• Operations Research
• Healthcare Logistics
• Transportation Systems
• +4 more

Onur Ozturk is an Associate Professor at the Telfer School of Management, University of Ottawa. He holds a PhD in Industrial Engineering from Grenoble Institute of Technology (2012), followed by a postdoctoral fellowship at Ivey Business School (2012–2014). Prior to his current position, he worked as an Assistant Professor at ESIEE Paris (2014–2017). His research focuses on large-scale optimization in healthcare and transport sectors, leveraging scheduling theory, simulation modeling, and stochastic processes. He has contributed to projects such as the Efficacity Institute’s urban freight transport optimization in Île-de-France. Research interests include: Integer linear programming and operations research applications Scheduling and simulation modeling for healthcare logistics and transportation Optimization of pandemic-related operations (e.g., staff scheduling in hospitals, patient admissions) Bi-criteria optimization in medical sterilization and manufacturing systems Recent funded research includes a NSERC grant (2019–2026; $169,000) for batch scheduling methods, and multiple internal grants addressing healthcare routing (2021), medical sterilization (2021), and pandemic staffing (2020–2021). His work emphasizes practical solutions for urban logistics, healthcare capacity planning, and crisis management. No scientific awards are explicitly mentioned in the text. His teaching focuses on business analytics, operations management, and stochastic modeling.

• Operations Research
• Robust Optimization
• Queueing Theory
• +7 more

Chaithanya Bandi is an Associate Professor at the National University of Singapore (NUS) in the Analytics and Operations Department of the NUS Business School, with a joint appointment in the Department of Mathematics. His research focuses on decision-making under uncertainty, robust optimization, and their applications in operations management, healthcare, e-commerce, and energy systems. He develops robust optimization models for queueing control, risk optimization, and mechanism design. Key research areas include robust queue inference, two-stage distributionally robust optimization, and multi-item auction mechanisms. He has contributed to operational challenges in healthcare (e.g., patient re-entry scheduling), energy systems (electricity generation optimization), and e-commerce (price optimization for fashion products). His work integrates theoretical advancements with practical implementations in large-scale systems. Recent publications emphasize adversarial evaluation of large language models, dynamic scheduling algorithms, and robust policies for uncertain environments. His methodologies often involve novel optimization frameworks and scalable computational approaches. Dr. Bandi holds a PhD in Operations Research and has collaborated with industry leaders like Flipkart and healthcare providers to apply his models in real-world settings. His contributions bridge theoretical rigor and practical applicability in complex operational systems.

William CookView profile

Professor

• Combinatorial Optimization
• Operations Research
• Discrete Mathematics
• +5 more

William Cook is a University Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research focuses on combinatorial optimization, computational discrete optimization, and the traveling salesman problem (TSP). He is renowned for co-developing the Concorde TSP Solver , a leading software for solving TSP instances. Cook has held editorial roles in top journals such as Mathematical Programming Computation and Mathematical Programming , and served as Chair of the Mathematical Optimization Society and Vice Chair of INFORMS Computing Society. His research interests include algorithm design, operations research, graph theory, and computational methods for NP-hard problems. Notable contributions include advancing cutting-plane methods, branch-and-bound algorithms, and hybrid optimization techniques. Cook has authored influential books like In Pursuit of the Traveling Salesman and The Traveling Salesman Problem: A Computational Study . Awards include membership in the National Academy of Engineering, SIAM Fellowship, INFORMS Fellowship, and the $100,000 Amazon Last Mile Routing Research Challenge prize (2021). He has advised numerous projects in computational optimization, including studies on deep learning applications and combinatorial algorithm development. His work bridges theoretical advancements with practical software tools like QSopt and QSopt_ex .

Rolf FagerbergView profile

Professor

• Algorithms
• Data Structures
• Computational Biology
• +7 more

Rolf Fagerberg is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU). His research centers on algorithms, data structures, and their applications in computational biology and cheminformatics. His research interests include: Algorithms and data structures, particularly dynamic and geometric data structures Graph theory with emphasis on subgraphs, Yao graphs, and hypergraphs Algorithmic cheminformatics and modeling of chemical reaction networks Computational biology, including metabolic pathway analysis Theoretical computer science and discrete mathematics Recent publications highlight a strong trend toward interdisciplinary research combining computer science with chemistry and biology, particularly in modeling chemical reaction networks using hypergraphs and mixed-integer linear programming. His work also includes algorithmic solutions for palindromic subsequence problems and efficient extraction of reaction rules from large databases, reflecting a blend of theoretical and applied algorithm design. Rolf Fagerberg has served as senior coordinator on multiple research projects, including 'Algorithmic Cheminformatics' and 'Fundamental Data Structures', funded by the Danish Ministry of Higher Education and Research. He has also contributed to peer review and editorial work for major conferences such as the ACM Symposium on Parallelism in Algorithms and Architectures and the International Symposium on Experimental Algorithms. He has supervised PhD students and is actively involved in academic service, including membership in assessment committees at Aarhus University and IT University of Copenhagen. His research has received media attention, including coverage of a 'mathematical breakthrough' and applications in understanding intestinal systems in obesity.

• Large Scale Optimization
• Heuristic Methods
• Flexible Manufacturing Systems
• +3 more

Gökalp Erbeyoglu is a Lecturer in the Industrial Engineering Department at Özyeğin University. He earned his BS, MS, and PhD in Industrial Engineering from Boğaziçi University, completing his doctorate in 2019. His research focuses on large-scale optimization, heuristic methods, flexible manufacturing systems, and humanitarian logistics. Education: PhD in Industrial Engineering, Boğaziçi University (2019) MS in Industrial Engineering, Boğaziçi University (2013) BS in Industrial Engineering, Boğaziçi University (2010) His work in humanitarian logistics addresses fairness concerns during disaster relief operations, as evidenced by his 2019 PhD thesis on network optimization models. In industrial engineering, he has applied metaheuristics like Particle Swarm Optimization and Simulated Annealing to bilinear programming problems, particularly the pooling problem, during his MS studies. Recent publications highlight his focus on disaster preparedness models balancing effectiveness and fairness, alongside methodological advancements in heuristic algorithms. He has presented findings at INFORMS, EURO2018, and Turkish National Operations Research conferences. At Özyeğin University, he teaches undergraduate courses in operations research, mathematical modeling, computer-integrated manufacturing, and scheduling. Previously, he contributed to research at the BUFAIM Laboratory, focusing on flexible automation and shop floor control techniques.