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Lucas WaddellView profile

Associate Professor

• Discrete Optimization
• Operations Research
• Nonlinear Combinatorial Optimization
• +4 more

Lucas Waddell is an Associate Professor of Mathematics at Bucknell University, joining the faculty in 2018. Prior to Bucknell, he worked as an Operations Research Analyst at Sandia National Laboratories in Albuquerque, NM, addressing large-scale decision support problems for national security applications. His academic background includes a Ph.D. and M.S. in Mathematical Sciences from Clemson University and a B.S. in Mathematics from Grove City College. Ph.D. in Mathematical Sciences, Clemson University M.S. in Mathematical Sciences, Clemson University B.S. in Mathematics, Grove City College Waddell's research focuses on discrete optimization, particularly nonlinear combinatorial optimization problems like the Quadratic Assignment Problem and Cubic Knapsack Problem. He develops theoretical frameworks and practical tools to solve computationally challenging problems, including applications in autonomous systems, fleet modernization, and educational scheduling. His work with Hopeful Journeys Educational Center demonstrates his commitment to applying optimization techniques to real-world challenges. His publications highlight advancements in linearization techniques, reformulation-linearization theory, and stochastic programming for security systems. Keywords across his work include Operations Research, Combinatorial Optimization, Integer Programming, and Computational Complexity. He has also contributed to applied projects involving national security and educational resource allocation. Contact: law038@bucknell.edu | Office: 477 Olin Science Building | Phone: 570-577-1902

Anna Livia CroellaView profile

Assistant Professor

• Combinatorial Optimization
• Mixed Integer Programming
• Train Dispatching
• +8 more

Anna Livia Croella is an Assistant Professor at La Sapienza University of Rome, with a tenure-track position at Universitas Mercatorum's Faculty of Technological and Innovation Sciences. She earned her PhD in Operations Research (MAT-09) in May 2022 and MSc in Management Engineering in October 2017 from La Sapienza. She belongs to the Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG) at the Faculty of Information Engineering, Informatics and Statistics. PhD in Operations Research (La Sapienza, 2022) MSc in Management Engineering (La Sapienza, 2017) Research Interests : Croella specializes in Combinatorial Optimization and Mixed Integer Programming , with applications to train dispatching , waste management , and clustering . Her work addresses Job Shop Scheduling and Railway Systems Optimization , particularly in real-time train scheduling and disruption management. Recent research includes fair clustering algorithms and circular economy modeling for municipal waste systems. Publication Trends : Her 15 most recent articles focus on railway optimization (4 papers), waste/biomethane production (3 papers), scheduling (3 papers), and healthcare applications (2 papers). Key methodologies include Mixed Integer Programming , MaxSAT algorithms , and combinatorial modeling . These works span journals like Computers & Operations Research , Computers & Industrial Engineering , and Transportation Science . Scientific Contributions : 400 Outstanding Students Recognition, Sapienza University (2018) AIROYoung Best Thesis Finalist (2022) Honorable Mention, INFORMS RAS Student Paper Competition (2022) Young Women 4 OR Prize, EURO WISDOM Forum (2025) Laboratory Affiliation : Member of DIAG (Dipartimento di Ingegneria Informatica, Automatica e Gestionale) research team, contributing to optimization modeling and algorithm development.

Erik ElfgrenView profile

Associate Professor

• Energy Engineering
• Sustainable Energy Systems
• Municipal Energy Optimization
• +2 more

Erik Elfgren is an Associate Professor and Distinguished University Teacher at Luleå University of Technology, Department of Engineering Sciences and Mathematics, specializing in Energy Engineering within the Energy Science division. His work focuses on optimizing energy systems with significant contributions to sustainable municipal energy solutions in Nordic regions. Elfgren earned his MSc in Engineering Physics from Luleå University of Technology in 2000, followed by an MSc in Physics from the University of Montréal in 2002. He completed his Licentiate degree in 2005 and received his PhD in January 2008, all from Luleå University of Technology. His primary research interests center on sustainable energy systems, particularly optimization of municipal energy systems in Nordic environments. Elfgren investigates energy efficiency, CO2 emission reduction strategies, and the integration of electricity, heating, and transport sectors. His work addresses challenges posed by cold climates, large-scale industries, and high electric heating shares in northern regions. Earlier in his career, he conducted research on cosmic phenomena including the impact of early dust on the Cosmic Microwave Background and stellar evolution. Elfgren has received recognition as a Distinguished University Teacher and has made significant contributions to energy research through publications in journals such as Energies. His work demonstrates strong interdisciplinary connections between engineering physics and practical energy solutions. As an academic supervisor, Elfgren has guided research including Robert Fischer's licentiate thesis on sustainable energy systems in Nordic municipalities. His research has been supported by the Interreg Nord project (20200589), focusing on regional development and sustainable energy solutions. Within the Energy Science division, Elfgren collaborates on optimizing the Luleå energy system as a whole, working with major local energy stakeholders including the SSAB steel plant, LuleKraft heat producer, and Luleå Energi heat distributor. His current work integrates Exergy into the MILP (Mixed Integer Linear Programming) program reMIND to enhance energy system modeling capabilities.

Ilke BakirView profile

Associate Professor

• Transportation and Logistics
• Supply Chain Management
• Stochastic Optimization
• +4 more

Ilke Bakir is an Associate Professor in the Department of Operations at the Faculty of Economics and Business, University of Groningen. She has been part of the faculty since 2018, holding roles as Assistant Professor (2018–2023) and currently as Associate Professor. Her expertise spans Operations Research, Transportation and Logistics, Supply Chain Management, and Stochastic Optimization. Education: Ph.D. in Industrial Engineering, Georgia Institute of Technology (2017) M.Sc. in Industrial Engineering, North Carolina State University (2011) B.Sc. in Industrial Engineering, Bogazici University (2009) Research Interests: Her work focuses on optimizing complex systems in transportation and energy sectors, including fleet management, wind farm operations, and data-driven maintenance strategies. She also explores digital transformation in supply chain logistics and stochastic optimization frameworks. Recent projects include the SMiLES initiative (NWO-funded) and hospital logistics planning under pandemic constraints. Grants & Projects: SMiLES: Shared connectivity in Mobility and Logistics Enable Sustainability (2019–2024) – NWO, TKI Dinalog, Ministry of Infrastructure Hospital logistics in the 1.5-meter society (2020) – ZonMw-funded Advising: Supervises four PhD students focusing on logistics optimization, dynamic service systems, and collaborative carrier networks. Teaching: Courses include 'Introduction to Operations Research', 'Supply Chain Network Design', and 'Supply Chain Analytics' at both BSc and MSc levels.

• Computational Mechanics
• Finite Element Analysis
• Structural Dynamics
• +9 more

Prof. Duc Nguyen is a Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU), affiliated with the Batten College of Engineering and Technology. His expertise spans Computational Mechanics, Finite Element Analysis (FEA), Structural Dynamics, and Parallel Computing. He has held numerous prestigious roles, including NASA-ASEE Fellowships and the 2010 ODU Shining Star Award. Nguyen holds a Ph.D. from the University of Iowa (1982), M.S. from the University of California (1976), and B.S. from Northeastern University (1974). Research focuses on large-scale algorithms for parallel supercomputing, design sensitivity analysis, sparse linear solvers, and multiphysics CAD-based optimization. He has led grants totaling over $2 million, including projects on numerical methods, engineering education, and structural analysis. Notable publications address parallel finite element methods, structural/acoustic coupling, and optimization using genetic algorithms. Awarded honors include recognition for citation impact (2004), teaching excellence (2001), and computational performance (1989). His work bridges computational efficiency with engineering applications, emphasizing interdisciplinary collaboration. Current research explores MPI-enabled algorithms, multi-hazard risk assessment, and coastal infrastructure resilience.

Fatma Kilinc-KarzanView profile

Professor

• Operations Research
• Analytics
• Machine Learning
• +8 more

Fatma Kilinc-Karzan is a Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University, and holds the Frank A. and Helen E. Risch Faculty Development Chair. She is also an Associate Professor of Computer Science (by courtesy) and affiliated with the Algorithms, Combinatorics, and Optimization (ACO) PhD Program. Her career includes visiting roles at institutions like the Simons Institute at UC Berkeley and extensive professional service on editorial boards and conference committees. PhD in Industrial and Systems Engineering (minor in Mathematics) from Georgia Institute of Technology B.S. and M.S. in Industrial Engineering (minor in Information Systems) from Middle East Technical University Research Interests : Her work focuses on convex optimization , structured nonconvex optimization , and their applications in optimization under uncertainty (robust optimization, chance constraints), machine learning (preference learning from limited data), and business analytics . She explores theoretical aspects like semidefinite programming (SDP) relaxations, convex hull characterizations, and algorithmic efficiency for large-scale problems. Article Trends : Her recent publications emphasize semidefinite programs , rank-one function optimization , and chance-constrained programming with applications in portfolio optimization , healthcare , and recommender systems . Key methodologies include perspective reformulation , submodularity , and first-order algorithms . Scientific Awards : 2015 INFORMS Optimization Society Prize for Young Researchers 2014 INFORMS JFIG Best Paper Award Advising and Grants : She has advised over a dozen PhD students, many of whom won awards like the INFORMS Optimization Society Best Student Paper Prize. Her research is supported by grants including an NSF CAREER Award , ONR grant , and AFOSR grant . She collaborates with institutions like IBM and the Simons Institute.

Sarang KulkarniView profile

Research Fellow

• Logistics and Supply Chain Management
• Operations Research
• Optimization
• +3 more

Sarang Kulkarni is a Senior Research Fellow at Curtin University's School of Elec Eng, Comp and Math Sci (EECMS), within the Faculty of Science and Engineering. He holds a PhD and works at Curtin's Perth campus in the Centre for Optimisation and Decision Science. His research focuses on real-life large-scale optimisation problems, including scheduling, mathematical programming, and mixed-integer programming. Education: PhD, MEng (Industrial Engineering and Management), BEng (Production) Research Interests: Logistics and Supply Chain Management, Operations Research, Optimisation techniques, and heuristic development Specialty: Decompositions and heuristics for complex optimisation challenges in transportation, logistics, and supply chain management His publications span vehicle scheduling, blast design optimisation, and hybrid neural network approaches. Notable works include benchmark datasets for multi-depot vehicle scheduling (2019) and autonomous drill-based blast optimisation (2024). He collaborates with industry partners in India and across Curtin's global campuses (Australia, Dubai, Malaysia). Labs/Teams: Active member of the Centre for Optimisation and Decision Science, contributing to both academic and industrial applications of optimisation technologies.

• Discrete Optimization
• Multilevel Programming
• Network Flows
• +5 more

Leonardo Lozano is an Associate Professor in the Department of Operations, Business Analytics, and Information Systems at the University of Cincinnati's Lindner College of Business. He holds a Ph.D. in Industrial Engineering from Clemson University (2017), an M.Sc. from the University of Florida (2014), and both an M.Sc. and B.Sc. from Universidad de los Andes (2011, 2009). His research integrates theoretical and applied optimization, focusing on discrete optimization, multilevel programming, and network flows to address challenges in vehicle routing, scheduling, and security. Dr. Lozano's work emphasizes algorithmic innovation for complex decision-making under uncertainty. His recent publications explore neural network optimization, robust logistics, and stochastic modeling, reflecting a consistent focus on scalable computational methods. Key methodologies include decision diagrams, bilevel programming, and exact algorithms for large-scale networks. Awards & Honors : Glover-Klingman Best Paper Award (2020), Lindner Research Excellence Award (2024), Daniel J. Westerbeck Teaching Award (2022), and multiple Dean's Teaching Excellence recognitions. Grants & Projects : Co-led a $418K Air Force project (2022-2025) on UAV surveillance optimization and a $300K Office of Naval Research grant (2019-2021) for robust optimization. Current work includes autonomous trucking network optimization. He serves as Faculty Advisor for the INFORMS Student Chapter and actively contributes to departmental leadership, including roles as Assistant Department Head and committee chair. Dr. Lozano reviews for top journals like Operations Research , Mathematical Programming , and INFORMS Journal on Computing .

Jiří FinkView profile

Assistant Professor

• Artificial Intelligence
• Combinatorics
• Graph Theory
• +3 more

Jiří Fink serves as an Assistant Professor in the Department of Theoretical Computer Science and Mathematical Logic at the Faculty of Mathematics and Physics, Charles University, Prague. His academic profile centers on theoretical computer science with applications in artificial intelligence and combinatorial mathematics. His primary research areas include: Artificial Intelligence Combinatorics Graph Theory Optimization Theoretical Computer Science Mathematical Logic Dr. Fink teaches courses such as Large Scale Optimization (covering exact methods for linear/integer programming and Lagrange relaxation), Data Structures, and Introduction to Artificial Intelligence. He supervises bachelor's and master's theses in AI and mathematical topics, and is a principal organizer of the annual Spring School of Artificial Intelligence held in Patejdlovka since 2022, which combines student research presentations with recreational activities like hiking and skiing. No scientific awards are mentioned in the provided information. Regarding advising, Dr. Fink actively supervises theses in combinatorial mathematics and AI applications. The text contains no details about research grants or funding sources. His professional activities emphasize educational leadership through course development and community-building academic events. He maintains an active departmental presence with office location at Malá Strana, floor 3, room 305 (Malostranské náměstí 2/25, Prague 1) and can be contacted via email fink@ktiml.mff.cuni.cz.

Aleksandar KarteljView profile

Associate Professor

• Optimization
• Data mining
• Computational biology
• +4 more

Aleksandar Kartelj is an Associate professor at the Department for Computer Science, Faculty of Mathematics, University of Belgrade. He has been working at the Faculty of Mathematics since 2008, starting as a Teaching assistant, then becoming Assistant professor (2015-2023), and currently Associate professor (since October 2023). He also serves as a Guest professor at the Faculty of Natural Science and Mathematics, University of Banjaluka since 2018. Education: B.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2005-2008, GPA 9.94/10.00) M.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2008-2010, GPA 9.92/10.00) PhD in Computer Science from Faculty of Mathematics, University of Belgrade (2010-2014, GPA 10.00/10.00) M.Sc. in Quantitative Finance from Faculty of Economics, University of Belgrade (2010-present) Aleksandar Kartelj's research primarily focuses on optimization and data mining. He is a member of the "Modelling and optimization group" at the Faculty of Mathematics. His work involves designing exact and non-exact optimization techniques such as integer (linear) programming models, branch & bound, beam search, variable neighborhood search, and genetic algorithms. These techniques are applied to solve NP-hard problems in computational biology, transportation, logistics, social networks, and for improving machine learning algorithms. His recent publications (2021-2025) span diverse areas including symbolic regression, constrained longest common subsequence problems, graph protection algorithms, Roman domination in graphs, and applications in computational biology and epidemiology. His work frequently appears in high-impact journals such as Journal of Big Data, Expert Systems with Applications, and Knowledge-Based Systems, with many publications classified as M21a (Q1). Scientific Awards: Best paper award at the 2023 Second Serbian International Conference on Applied Artificial Intelligence (SICAAI) for "Integrating Top-level Constraints into a Symbolic Regression Search Algorithm" Aleksandar Kartelj has been involved in several research projects, including being a Research member on the project "Mathematical Models and Optimization Methods for Large-Scale Systems" (2011-2020) and a Research leader on the project "Combinatorial optimization for cancer progression inference and comparison" (2019-present). He has also developed numerous software applications for various organizations including the UN FAO, International Monetary Fund, and several private companies. He has been actively involved in teaching, serving as a Computer science teacher at Computer Gymnasium and Mathematical Grammar School in Belgrade, in addition to his university teaching responsibilities.

• Optimization
• Transportation
• Logistics
• +3 more

Dr. Burak Boyaci is a Senior Lecturer in the Department of Management Science at Lancaster University, affiliated with the Data Science Institute and the Centre for Transport & Logistics (CENTRAL). His research focuses on optimizing real-life problems using mathematical models. Specializes in large-scale mixed integer linear programming Applies optimization tools to complex systems in transportation and logistics Research Interests: Public and shared-use transport systems, offshore windfarm logistics, airport slot allocation, waste routing, and hazardous material transport. He supervises PhD students in topics related to mathematical modeling, (meta)heuristics, simulation, and sustainable mobility. Scientific Contributions: Developed optimization frameworks for carsharing systems and tropical regression models in biological data analysis. Active in vehicle routing optimization and decision-making frameworks for sustainable transport. Transportation Science Meritorious Service Award EURO Award for Best EJOR Paper on Innovative Applications of OR PhD Students: Georgios Aliatimis, Rebecca Hamm, Theodoti Kerama, Davood Mohammadi

Michael C. FerrisView profile

Professor

• Optimization
• Mathematical Programming
• Algorithm Development
• +4 more

Michael C. Ferris is the John P. Morgridge Chair and Jacques-Louis Lions Professor of Computer Sciences at the University of Wisconsin–Madison, with courtesy appointments in Mathematics and Industrial & Systems Engineering. He serves as Director of Hub Central, Wisconsin Institutes for Discovery. PhD in Mathematics from University of Cambridge (1989) Research focuses on optimization algorithms, mathematical programming, and applications spanning energy systems, transportation, environmental engineering, and biomedical domains His scholarly work includes 15+ recent publications on topics such as: Stochastic optimization for energy markets Complementarity problems in ecological systems Power flow formulations with sparse matrices Decision support tools for Great Lakes restoration Game-theoretic models of hydro-thermal electricity systems On-chip network optimization As a SIAM and INFORMS Fellow , he has received prestigious honors including the Beale-Orchard-Hays Prize, NSF Presidential Young Investigator Award, and Guggenheim Fellowship. His methodological contributions include software tools like PATH , NLPEC , and EMP for large-scale optimization problems, with applications ranging from cancer treatment planning to smart grid development .

Jacek GondzioView profile

Professor

• Large Scale Optimization
• Interior Point Methods
• Sparse Matrix Methods
• +3 more

Jacek Gondzio is a Professor in the School of Mathematics at the University of Edinburgh. He received his M.Eng. in Electronics (1983) and PhD in Automatic Control and Robotics (1989) from Warsaw University of Technology. His career includes positions at the Polish Academy of Sciences (1989–1993), University of Geneva (1993–1998), and the University of Edinburgh since 1998, where he progressed from Lecturer to Professor. Research Interests: Gondzio's work spans large-scale optimization techniques, including interior point methods, sparse matrix computations, parallel algorithms, and applications in finance and engineering. Key focus areas include: Development of efficient solvers (HOPDM, OOPS) for linear/quadratic/nonlinear programming Matrix-free methods and preconditioning for massive-scale problems Applications in quantum information, tomography, structural design, and financial planning Publication Trends: His recent articles emphasize scalable algorithms for optimization, including proximal methods for semidefinite programming, interior-point innovations, and applications in medical imaging and transport. Work frequently integrates regularization, decomposition techniques, and structure-exploiting linear algebra. Awards: EUROPT Fellow (2019) for contributions to continuous optimization Grants & Advising: Current projects include EPSRC-funded work on building structure optimization (EP/N019652/1), Google-funded LP solvers, and risk modeling with Standard Life Investments. He has supervised 16+ PhD students on topics ranging from interior point methods to machine learning optimization. Software includes HOPDM, PDCGM, and the parallel solver OOPS. Leadership: Organizes workshops on optimization (e.g., COA, Advances in Preconditioners series) and serves on editorial boards for Mathematical Programming Computation , Computational Optimization and Applications , and other leading journals.

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.