Find Academic Supervisors

Ahmet CürebalView profile

Researcher

• Operations Research
• Linear Programming
• Mixed-Integer Programming
• +5 more

Ahmet Cürebal is an External Doctoral Researcher at the Institute of Information Systems, part of the Business School at the University of Hamburg. He holds B.S. and M.S. degrees in Industrial Engineering from Kirikkale University, Turkey, and is currently pursuing his Ph.D. His research focuses on Operations Research with specializations in Linear Programming (LP), Mixed-Integer Programming (MIP), Combinatorial Optimization, Scheduling, Assignment, and Routing problems within Supply Chain contexts. Key research interests include solving real-world optimization challenges such as staff scheduling in retail sectors, driver workload balancing in logistics firms, and pandemic-era security personnel allocation. His work often involves applying metaheuristic methods like Variable Neighborhood Search (VNS) and Fixed Set Search (FSS) to complex operational problems. Notable publications explore applications such as straddle carrier routing in container terminals, bus route optimization for high-speed rail integration, and competency-based scheduling during crises. His methodologies frequently integrate Goal Programming and Analytical Network Process (ANP) for multi-criteria decision-making scenarios. Cürebal’s research demonstrates a strong emphasis on practical implementations across diverse industries, including healthcare, logistics, and retail, with a focus on balancing efficiency and real-world constraints through advanced mathematical modeling and algorithmic approaches.

Natalya Pya ArnqvistView profile

Associate Professor

• Statistical Regression Modelling
• Functional Data Analysis
• Shape Preserving Smoothing
• +7 more

Natalya Pya Arnqvist is an Associate Professor in Mathematical Statistics at Umeå University, Sweden. She is affiliated with the Department of Mathematics and Mathematical Statistics and has active roles in research groups focused on Functional Data Analysis, Semiparametric Regression, and Statistical Learning for Spatio-Temporal Data. Her career includes prior positions at Nazarbayev University (2015-2020), University of Bath (2011-2015), and KIMEP University (2000-2005). Education : PhD in Statistics (University of Bath, UK, 2007-2010), CSc in Physical and Mathematical Sciences (Institute for Mathematics, Kazakhstan, 2000-2005). Her research centers on statistical regression modelling and functional data analysis , with significant contributions to shape constrained additive models (SCAMs) and model-based functional clustering . She has developed R packages such as scam , fdaMocca , and nilde for these methodologies. Recent publications highlight applications in applied demography , defect detection , and nonlinear state space modeling , reflecting her focus on bridging statistical theory with industrial and ecological challenges. She teaches undergraduate and graduate courses in probability, regression, and statistical learning.

Ricardo FukasawaView profile

Professor

• Mixed-Integer Programming
• Computational Optimization
• Operations Research
• +3 more

Ricardo Fukasawa is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Algorithms, Combinatorics, and Optimization from Georgia Institute of Technology and has held postdoctoral and faculty positions at IBM Research and the University of Waterloo. His research focuses on cutting planes for mixed-integer programming, computational optimization, operations research, and routing problems, with applications in logistics and scheduling. Education: PhD in Algorithms, Combinatorics, and Optimization (GeorgiaTech, 2008); MEng and BEng in Electrical Engineering (PUC-Rio, 2002 and 2000). He has received awards such as the IBM Herman Goldstine Fellowship and the Early Researcher Award. Research interests include: Mixed-Integer Programming, Computational Optimization, Routing Problems, Stochastic Optimization, and Polyhedral Combinatorics. His work often addresses real-world challenges in scheduling and vehicle routing, leveraging algorithmic and computational methods. Publications span topics like scheduling policies, cutting planes, and vehicle routing under uncertainty. Notable contributions include dynamic scheduling frameworks and branch-and-cut algorithms for combinatorial optimization. Grants: Recipient of NSERC Discovery Grants, Waterloo Institute for Nanotechnology funding, and collaborative industry grants. Students: Supervised numerous PhD and Master’s students in optimization and operations research. Service: Editor for Operations Research and Operations Research Letters ; organizer of conferences like IPCO and ISMP. Labs and collaborations include interdisciplinary projects with industrial partners, focusing on optimizing large-scale systems and scheduling algorithms.

• Mathematical Optimization
• Algorithmic Game Theory
• Multi-Agent Systems
• +3 more

Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with the Department of Mathematics within Faculty II – Mathematics and Natural Sciences. He holds a PhD in Mathematics from TU Berlin (2012) and previously served as an Assistant Professor for Operations Research at Humboldt-Universität zu Berlin (2014–2016) and led the Junior Research Group for Optimization under Uncertainty at the Einstein-Center for Mathematics (2014–2016). His research focuses on algorithmic game theory, mathematical optimization of multi-agent systems in traffic, telecommunications, and economics. Key areas include network flows, congestion games, mechanism design, and infrastructure networks. He actively contributes to academic activities as an associate editor for journals like International Journal of Game Theory and Operations Research Forum , and has served on program committees for conferences such as AAAI, EC, and WINE. Recent work includes studies on evolutionary road network reconstruction, robustness of potential-based flows, and algorithmic solutions for knapsack and assignment problems. His research bridges theoretical foundations with practical applications in transportation and resource allocation.

Ismail AliView profile

Lecturer

• Optimization Problems
• Evolutionary Algorithms
• Combinatorial Optimization
• +7 more

Dr. Ismail Ali is a Lecturer at the School of Systems and Computing at UNSW Canberra, where he is affiliated with the Capability Systems Centre. He specializes in optimization problems, evolutionary algorithms, and simulation-based optimization techniques, with applications in production systems, supply networks, and defense-related problems. Dr. Ali's educational background includes: Bachelor's degree in Computer and Information from Zagazig University, Egypt (2010) Master by Research in Computer Science from UNSW Canberra (2014-2016) PhD in Computer Science from UNSW Canberra (2016-2020) Dr. Ali's research focuses on developing advanced optimization algorithms, particularly differential evolution techniques, for solving complex combinatorial optimization problems. His work spans multiple domains including production systems, supply chain management, and defense applications. He has made significant contributions to the adaptation of evolutionary algorithms for discrete optimization problems such as the traveling salesman problem and knapsack problems. His recent research has expanded into applications involving swarm robotics, carbon policy modeling, and secure simulation frameworks for defense applications. Analysis of Dr. Ali's publication record shows a consistent progression from theoretical algorithm development to practical applications in manufacturing, supply chains, and defense systems. A notable trend is the increasing integration of simulation techniques with optimization algorithms to address complex, multi-objective problems in dynamic environments. His work demonstrates strong interdisciplinary connections between computer science, operations research, and applied engineering domains. Dr. Ali has received significant recognition for his academic work: The Stephen Fester Prize for Outstanding Master's Thesis (2016) The Stephen Fester Prize for Outstanding PhD Thesis (2020) Dean's Award for Outstanding PhD Thesis at UNSW Canberra (2021) As an academic supervisor, Dr. Ali mentors PhD students and final-year graduation projects at UNSW Canberra. He has secured research funding through the Rapid Research Funding program ($16,500 in 2023) and the Faculty Seed Fund ($38,000 in 2024), supporting his work on optimization algorithms and their applications. His teaching responsibilities include courses in simulation, reliability engineering fundamentals, reliability program management, and reliability engineering assurance. As a member of the Capability Systems Centre at UNSW Canberra, Dr. Ali collaborates with researchers working on complex systems analysis, defense applications, and advanced computational methods. His work contributes to the Centre's mission of addressing challenging problems in defense and security through innovative systems approaches.

• Optimization
• Mixed-Integer Programming
• Lattice Theory
• +5 more

Professor Timm Oertel is a leading academic at the Department of Data Science (DDS) at Friedrich Alexander University Erlangen-Nuremberg, where he holds the Chair of Analytics & Mixed-Integer Optimization. His research has established him as a significant contributor to the field of mathematical optimization, with a particular focus on integer programming and its applications. Professor Oertel's research interests center on mathematical optimization , specifically mixed-integer programming , lattice theory , semigroups , and combinatorial optimization . His work explores fundamental questions about sparsity in integer solutions, properties of lattice points in polyhedra, and the theoretical foundations of optimization algorithms. His research bridges pure mathematics with practical applications in operations research and computer science. An analysis of Professor Oertel's publication record reveals a strong thematic consistency with evolving depth. His work consistently addresses sparsity phenomena in integer programming, structural properties of lattices and semigroups, and computational complexity aspects of optimization problems. The publications demonstrate increasing sophistication in handling block-structured problems and parametric optimization scenarios, with recent work extending to colorful variants of classical geometric lemmas applied to integer programming. Professor Oertel has established productive collaborations with leading researchers including Iskander Aliev, Robert Weismantel, and Joseph Paat. His research has been consistently published in top-tier venues including Mathematical Programming , SIAM Journal on Optimization , and the prestigious IPCO (Integer Programming and Combinatorial Optimization) conference series. These publications reflect rigorous theoretical contributions with potential applications across operations research, computer science, and discrete mathematics.

• Probability Theory
• Discrete Optimization
• Mathematical Statistics
• +2 more

Odysseas Kanavetas is an Assistant Professor in the Mathematical Institute at Leiden University's Faculty of Science, specializing in Probability Theory and discrete optimization. His research interests span Probability Theory, Discrete Optimization, Mathematical Statistics, Algorithm Design, and Decision Sciences. His work bridges theoretical mathematics with practical applications in social sciences and operations research. Dr. Kanavetas has published significant research on Ameso optimization, which provides a relaxation framework for discrete midpoint convexity with applications to problems like the knapsack problem. He has also conducted research on recidivism factors in Boston using statistical methods including multinomial logistic regression and chi-square analysis. His publications demonstrate expertise in both theoretical mathematical frameworks and their application to real-world decision problems. Dr. Kanavetas maintains an active research profile with publications in reputable journals including Discrete Applied Mathematics and the International Journal of Applied Decision Sciences.

• Decision Analysis
• Fairness in Resource Allocation
• Multi-Dimensional Inequality
• +3 more

Nikolaos Argyris is a Senior Lecturer in Operational Research at Loughborough Business School, Loughborough University. He holds a BSc in Economics from Aristotle University, followed by an MSc and PhD in Operational Research from the London School of Economics (LSE). Prior to his current role, he was a Research Fellow at the University of Warwick’s Department of Statistics and held post-doctoral positions at the LSE. His research focuses on decision analysis, fairness in resource allocation, multi-dimensional inequality, and emergency management, with applications in public health, education, and crisis response. Argyris has collaborated extensively with public sector organizations, including Public Health England (on radiological emergency decision support), the NHS (for participative resource allocation methods), and the former UK Department for Education (assessing school efficiency). His work bridges theoretical operational research with practical policy applications, emphasizing the integration of inequality aversion principles and stakeholder engagement in decision-making processes. His research portfolio highlights trends in spatial risk analysis, welfare-based optimization, and behavioral OR. Notable projects include developing frameworks for communicating geographical uncertainty in crises and advancing methodologies for fair resource distribution. Collaborations have spanned interdisciplinary teams, addressing both technical and political dimensions of decision analysis. Argyris has contributed to projects on nuclear emergency management, emphasizing how precedent and international guidelines shape response strategies. His work often intersects with political processes, exploring how decision support systems can navigate complex stakeholder environments. While no specific awards are listed, his active engagement in applied research demonstrates a commitment to real-world impact. Grants and advising details are not explicitly detailed, but his career reflects a focus on collaborative, policy-driven research. He has been affiliated with the Management Science and Operations research group at Loughborough, contributing to both academic and applied initiatives in operational research.

• Operations Research
• Machine Learning
• Healthcare
• +6 more

Dr. Esra Büyüktahtakιn Toy is a Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech, directing the Systems Optimization and Machine Learning Lab (SysOptiMaL). Her research focuses on integrating machine learning (ML), optimization, and artificial intelligence (AI) to address high-stakes decision-making in healthcare, environmental sustainability, and biosecurity. She has secured over $3 million in external funding from NSF, USDA, DOD, and others, and has authored 45 peer-reviewed publications, including 23 in A/A-ranked journals. Education: Ph.D. in Industrial and Systems Engineering, University of Florida (2009) M.S. in Management Science, Lehigh University (2007) M.S. in Industrial Engineering, Bilkent University (2005) Her research interests span multi-stage stochastic programming, reinforcement learning, and epidemic modeling. Recent work addresses resource allocation for disease outbreaks (e.g., Ebola, HIV), invasive species control (emerald ash borer), and AI-driven logistics optimization. She has graduated 8 Ph.D. students now in academic/industry roles and currently advises 8 more. Awards: 2016 NSF CAREER Award Five INFORMS Best Publication Awards Dr. Toy has pioneered the integration of epidemic modeling with logistics systems and contributed scenario dominance algorithms for stochastic optimization. She serves as Associate Editor for the INFORMS Journal on Computing and previously led the INFORMS Junior Faculty Interest Group (JFIG). Lab & Collaborations: The SysOptiMaL Lab develops scalable algorithms combining optimization theory and deep learning. Current projects include AI-driven multi-stage optimization frameworks for healthcare and environmental systems, supported by grants from NSF, USDA, and U.S. Forest Service.

• Integer Programming
• Stochastic Programming
• High-Performance Computing
• +3 more

Jeffrey Linderoth is a Professor in the Department of Industrial & Systems Engineering at the University of Wisconsin-Madison, within the College of Engineering. His research focuses on large-scale optimization problems, including integer programming and stochastic programming, with an emphasis on high-performance algorithms and software development. He holds a PhD from Georgia Institute of Technology (1998) and has been recognized with numerous awards, including INFORMS Fellow (2016) and multiple best paper awards in optimization journals. Linderoth is actively involved in teaching optimization courses such as COMP SCI 524 and I SY E 524, and mentors pre-dissertation and thesis research. Education: PhD (1998), MS (1994) – Georgia Tech; BS (1992) – University of Illinois Research Interests: Linderoth specializes in high-performance computing for optimization, numerical methods, and distributed algorithms. His work addresses challenges in computational grids, combinatorial optimization, and applications in fields like energy systems and computational biology. He has developed influential software tools like Minotaur for mixed-integer nonlinear programming. Recent Achievements: His 2024 work on stochastic integer programming and dual decomposition methods, as well as contributions to autocatalytic networks in origin-of-life studies, highlight his interdisciplinary impact. Linderoth has also been honored multiple times for teaching excellence, including the University Housing Honored Instructor Award (2019, 2016, 2008). Awards: Over 20 awards spanning best papers, teaching excellence, and early career recognition Grants/Projects: Includes Department of Energy funding and collaborations on optimizing power systems and minimizing cascading failures Linderoth leads research teams and contributes to academic organizations, advancing both theoretical and applied aspects of optimization science.

Christophe GiraudView profile

Professor

• Statistics
• Machine Learning
• High-Dimensional Statistics
• +3 more

Christophe Giraud is a Professor of Mathematics at Université Paris-Saclay (Orsay) since 2012, with an adjunct position at Cornell University since 2016. He holds a PhD in Mathematics from Université Paris VI (2001) and a Habilitation from Université Paris XI (2011). His research focuses on high-dimensional statistics, machine learning, and their applications in biology and ecology. He leads the Master's program in Mathematics and Applications at Paris-Saclay and is Deputy Director of the Graduate School of Mathematics there. Giraud has authored Introduction to High-Dimensional Statistics (2015, 2021) and serves as an Action Editor for JMLR and JEMS . His work bridges theoretical statistics and practical algorithms, addressing challenges like clustering, bandit algorithms, and fair online learning. He has supervised numerous PhD students and contributes to software such as GGMselect. His publications span journals like Annals of Statistics , NeurIPS , and Journal of Machine Learning Research .

Mohammad HajiesmailiView profile

Associate Professor

• Theoretical Computer Science
• Machine Learning
• Networking and Distributed Systems
• +5 more

Mohammad Hajiesmaili is an Associate Professor at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He leads the SOLAR Lab, focusing on computational decarbonization, data-driven optimization, and sustainable systems. His research bridges theoretical computer science with practical applications in energy systems (e.g., electric vehicles) and networking. Educations: PhD in Computer Science, University of Tehran MSc in Computer Science, University of Tehran BSc in Computer Science, Sharif University of Technology Research Interests: Hajiesmaili’s work centers on designing algorithms for systems operating under uncertainty, particularly in energy-efficient computing, renewable energy integration, and carbon-aware scheduling. His lab develops tools for carbon-intelligent computing, including battery management for data centers and equitable decarbonization strategies for ridesharing platforms. He emphasizes interdisciplinary approaches combining optimization, machine learning, and theoretical analysis. Grants & Awards: NSF CAREER Award (2021) Google Faculty Research Award (2019) Amazon Research Award (2022) Popular Science Brilliant 10 (2022) Multiple ACM eEnergy best paper finalists (2015–2023) His research is supported by grants from NSF, VMWare, Adobe, and Amazon. Lab & Collaborations: The SOLAR Lab collaborates on projects like energy-efficient video streaming (SODA, BONES), carbon-aware scheduling (CarbonClipper), and equitable decarbonization in transportation (LEAD). Hajiesmaili also contributes to the Center for Data Science and Theoretical Computer Science Group at UMass Amherst.

Yann DisserView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Graph Exploration
• +6 more

Yann Disser is a Professor (W2) at the Department of Mathematics, Technische Universität Darmstadt. His research focuses on combinatorial optimization, online algorithms, and graph theory. He holds a PhD in Theoretical Computer Science from ETH Zurich (2011) and has held academic positions at TU Berlin and ETH Zurich. Disser has led numerous DFG-funded research projects, including work on incremental maximization and potential-based flow networks. His teaching includes courses on discrete optimization, algorithmic discrete mathematics, and online optimization. He supervises a research group with members such as Júlia Baligács and Annette Lutz, and has advised multiple PhD students including David Weckbecker. His recent work explores competitive analysis, algorithmic lower bounds, and graph exploration. Education: PhD in Theoretical Computer Science (ETH Zurich, 2011), MSc in Physics (TU Darmstadt), Diplom in Computer Science (TU Darmstadt). Research Interests: Combinatorial Optimization, Online Algorithms, Graph Exploration, Computational Complexity, Incremental Algorithms, Approximation Algorithms. Grants: DFG TRR 154 (2022–2026), DFG Individual Grants (2019–2023), EU EFRE Project (2016–2019). Labs: Deep Learning Lab (TU Darmstadt), Algorithms Lab (ETH Zurich).

• Operations Research
• Combinatorial Optimization
• Network Design
• +3 more

Ulrich Pferschy is a Professor affiliated with the Department of Algorithms and Complexity at Technische Universität Wien (TU Wien). His research focuses on Operations Research, Combinatorial Optimization, and Network Design, with particular expertise in algorithms for knapsack problems, Steiner tree problems, and network optimization. He has supervised numerous PhD students, including M. Ruthmair, A. M. Chwatal, and M. Leitner, among others. His work combines theoretical insights with practical applications in telecommunications infrastructure and algorithm design. Pferschy has contributed to leading journals and conferences, publishing influential papers on topics such as multidimensional knapsack problems and survivable network design. Despite his extensive contributions, no scientific awards are explicitly mentioned in the provided text.

Axel ParmentierView profile

Lecturer

• Operations Research
• Machine Learning
• Data-Driven Optimization
• +5 more

Axel Parmentier is a Lecturer and researcher at the École Nationale des Ponts et Chaussées, where he founded the AI for Air Transport industry research chair with Air France. His work focuses on the intersection of operations research and machine learning, particularly in data-driven combinatorial optimization and stochastic optimization, with industrial applications in air transportation, supply chain, and predictive maintenance. He holds a Ph.D. and has been recognized with awards including the AMIES Dissertation Award (2017) for applied mathematics with industrial impact and the Robert Faure Prize (under 35) from ROADEF. His research also includes contributions to structured reinforcement learning, optimization layers in machine learning, and explainable AI for operational decisions. Awards: AMIES Dissertation Award, Robert Faure Prize Labs/Teams: CERMICS laboratory, AI for Air Transport Chair (collaboration with Air France) Grants/Projects: Continent-scale inventory routing solutions, Renault’s logistics optimization His advising includes students like Victor Cohen, whose work on predictive maintenance was featured on France Culture.