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Pieter LeymanView profile

Assistant Professor

• Combinatorial Optimization
• Metaheuristics
• Scheduling
• +5 more

Pieter Leyman serves as an Assistant Professor in Sustainable Production Processes within the Industrial Systems Engineering (ISyE) research group at Ghent University's Faculty of Engineering and Architecture. He maintains an affiliate membership with Flanders Make, the strategic research center for the Flemish manufacturing industry. Education: PhD in Applied Economic Sciences from Ghent University (2016) Postdoctoral research at KU Leuven and University of Antwerp Research stay at Leiden University, Netherlands Research Focus: Dr. Leyman specializes in developing metaheuristic algorithms for NP-hard combinatorial optimization problems, integrating them with machine learning techniques to enhance performance. His work centers on energy-aware production scheduling to drive sustainability, with core expertise in optimization, explainable AI, and instance hardness analysis. Current projects address carbon-aware scheduling for GHG reduction and counterfactual explanations in AI systems. Publication Trends: Recent work (2022-2025) demonstrates a clear trajectory toward sustainable manufacturing solutions, with 60% of publications focusing on carbon-aware scheduling and energy optimization. His research bridges combinatorial optimization (particularly knapsack problem variants) with machine learning, yielding novel approaches like the NICE algorithm for counterfactual explanations. The work consistently targets industrial applications in manufacturing and transportation systems. Collaborations and Infrastructure: As part of ISyE and Flanders Make, Leyman engages in industry-academia partnerships focused on manufacturing innovation. His research leverages computational infrastructure for large-scale optimization and AI integration, with ongoing projects in net-zero manufacturing and competence-aware scheduling systems.

Bruce ShepherdView profile

Professor

• Optimization
• Algorithms
• Convex Geometry & Polyhedral Combinatorics
• +5 more

Bruce Shepherd is a Professor in the Department of Computer Science at the University of British Columbia. Previously, he held roles at the London School of Economics (LSE), Bell Laboratories, and McGill University. His academic journey includes a BSc from the University of Victoria, followed by an MSc and PhD in Combinatorics and Optimization from the University of Waterloo under supervision of Dan Younger and Bill Pulleyblank. He has held prestigious fellowships, including NATO Postdoctoral and Von Humboldt, and served as acting director of the Institute of Applied Mathematics at UBC (2019-2020). Research focuses on optimization, algorithms, graph theory, and network design , with applications in real-time systems, scheduling, and pricing. Key contributions include work on multiflow topologies, Gomory-Hu trees, and resilient network design. He has authored over 80 papers and served on editorial boards for journals like Mathematical Programming and SIAM-MPS Series . Awards and Recognition : NATO Postdoctoral Fellowship (1990s) Von Humboldt Fellowship (1990s) Nuffield Foundation Award (1990s) James McGill Professor (McGill University, 2007-2017) Professional Activities : Co-editor of Mathematical Programming Series A (2007-2016). Visiting positions at Microsoft Research, University of Washington, and the Fields Institute. Organized workshops on discrete optimization, data science, and robust network design.

• Quantum Algorithms for Optimization Problems
• Quantum Software-Hardware Co-Design
• Quantum Error Correction
• +2 more

Lilly Palackal is a Researcher and External PhD candidate at the Technical University of Munich (TUM) School of CIT, affiliated with the Department of Computer Science. She collaborates closely with Infineon Technologies AG on quantum computing research. Her work focuses on quantum algorithms for optimization problems, co-design of quantum software/hardware, and quantum error correction/mitigation. Education: BSc (2018) and MSc (2021) in Mathematics from TUM, with thesis topics in quantum error correction and interactive quantum communication. Current PhD research bridges academic and industrial quantum computing challenges. Research interests include developing quantum solutions for logistics (e.g., vehicle routing), optimization (e.g., knapsack problems), and hardware-aware algorithm design. She supervises multiple student projects across mathematics, physics, and engineering disciplines at TUM and partner institutions. Recent publications explore hybrid quantum-classical methods, QAOA applications, and efficient algorithm encodings for quantum systems. Her work emphasizes practical implementations of quantum technologies in real-world scenarios.

• Computational Complexity
• Graph Algorithms
• Distributed Algorithms
• +4 more

Roles and Affiliations: Aristides Pagourtzis is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), leading the Computer Science Division. He also serves as Lead Researcher at the Archimedes Research Center (Athena RC) and heads the Computation and Reasoning Laboratory (CORELab). His academic career includes positions at institutions like the University of Liverpool and ETH Zürich. Education: Ph.D. in Electrical and Computer Engineering (1999) and Diploma in Electrical Engineering (1989), both from NTUA. Research Interests: Computational Complexity, Graph Algorithms, Distributed Algorithms, Cryptography, and Network Algorithms. Teaching: Teaches courses such as Algorithms and Complexity, Cryptography, and Advanced Topics in Theoretical Computer Science. Courses span undergraduate and postgraduate levels, emphasizing foundational and applied computer science concepts. Publications & Grants: Over 80 publications in journals like Theoretical Computer Science and conferences like CIAC and FCT. His research has been funded by grants from the US, EU, and national resources. Notable contributions include work on approximation algorithms, distributed computing, and cryptographic protocols. Service & Leadership: Served as co-chair for CIAC 2017 and FCT 2021. Active in academic service, organizing workshops and participating in committees for conferences such as IWOCA and ACM. Labs & Teams: Leads the CORELab, focusing on theoretical computer science and its applications. Collaborates on projects involving algorithm design, complexity analysis, and secure systems.

William YoungView profile

Professor

• Operations Management
• Operations Research
• Predictive Analytics
• +7 more

Dr. William A. Young II is the Charles G. O’Bleness Full Professor of Business Analytics at Ohio University’s College of Business, specializing in the Department of Analytics and Information Systems. He holds a Ph.D. (2010), M.S. (2005), and B.S. (2002) in Electrical and Systems Engineering from Ohio University. His research focuses on operations management, predictive analytics, and decision support systems, with notable work in supply chain optimization, healthcare analytics, and educational technology. Education: Ph.D., Mechanical and Systems Engineering, Ohio University (2010) M.S., Electrical Engineering, Ohio University (2005) B.S., Electrical Engineering, Ohio University (2002) Research Interests: Dr. Young’s work bridges operations research and business analytics, emphasizing practical applications in logistics, healthcare, and education. His studies often employ artificial neural networks and optimization algorithms to address real-world challenges such as inventory management, energy efficiency, and student success prediction. Recent projects include viral marketing incentive models and pandemic risk mitigation in warehouse operations. Publications: His 100+ articles span journals like *European Journal of Operational Research*, *Applied Soft Computing*, and *Expert Systems with Applications*. Key themes include multi-objective optimization, predictive modeling, and decision support systems for complex systems. Awards: 2023 Faculty Excellence Award for Teaching and Learning 2020 University Professor Award Multiple College of Business Research Awards (2017, 2018) 2015 Cincinnati-Dayton Outstanding Young OR/MS Award Grants & Collaborations: He has secured funding from organizations like the National Science Foundation and General Electric Aviation. His interdisciplinary teams address challenges in manufacturing, environmental systems, and educational technologies. Labs & Initiatives: Leads Ohio University’s analytics research efforts, collaborating with industry partners to develop innovative solutions in supply chain, healthcare, and education. His work often integrates artificial intelligence with domain-specific knowledge extraction techniques.

• Operational Research
• Optimization
• Combinatorial Optimization
• +3 more

Roberto Montemanni is a Full Professor in the Department of Engineering Sciences and Methods at the University of Modena and Reggio Emilia . He is affiliated with the Operations Research Group and specializes in the MATH-06/A Operational Research disciplinary sector. His role involves teaching and research, with office hours available by appointment via email. He focuses on advancing optimization methodologies for complex logistical, transportation, and healthcare challenges. His research interests include combinatorial optimization , algorithm design , and constraint programming . Key areas of exploration involve conflict-constrained problems (e.g., assignment, knapsack, and spanning tree), probabilistic routing scenarios, and drone-assisted delivery systems. He also investigates real-world applications in home healthcare logistics, warehouse management, and supply chain optimization, often blending heuristic methods with exact algorithms. Recent publications emphasize trends in exact algorithm development for hazardous routing, drone scheduling in heterogeneous vehicle routing, and model-based approaches for time-bomb knapsack and orienteering problems. These works highlight interdisciplinary solutions for industries and healthcare systems. While no scientific awards are listed, his contributions to optimization theory and practice are substantial. His advising and grant activities likely align with his research group’s focus on operational challenges, though specific student names or grant details are not provided. He collaborates actively through the Operations Research Group , addressing issues like network-based data interpretation and intelligent manufacturing systems.

• Operations Research
• Decision Support Systems
• Optimization Algorithms
• +5 more

Daniele Pretolani is an Associate Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia. He specializes in Operations Research (MATH-06/A) with a focus on decision support systems, multi-objective optimization, and directed hypergraph algorithms. Teaches Methods and Algorithms for Optimization in Digital and Creative Industry (Management Engineering) Teaches Models and Methods for Decision Support (Management Engineering) His research explores: Stochastic time-dependent network routing Multi-criteria decision analysis for supplier selection Hypergraph reductions for satisfiability problems Visual decision support tools for composite indexes Recent work includes developing a decision support system for global service providers (2024) that improved supplier selection by 25% over company practices, and foundational contributions to the SDEWES sustainability index (2018-2020). No specific awards or student advising details were mentioned in the provided texts.

• Algorithmic Game Theory
• Mechanism Design
• Approximation Algorithms
• +6 more

Itai Feigenbaum is an Associate Professor in the Department of Computer Science at Lehman College and the Computer Science Program at the Graduate Center, both part of the City University of New York (CUNY) system. His academic career focuses on bridging theoretical computer science with practical applications in economic and social systems. Dr. Feigenbaum's educational background includes: Ph.D. in Operations Research from Columbia University (2016) M.Sc. in Operations Research from Columbia University (2012) B.Sc. in Mathematics from Rutgers University-New Brunswick (2011), with minors in Computer Science and Operations Research His research spans multiple interconnected domains at the intersection of computer science, economics, and operations research. Dr. Feigenbaum specializes in algorithmic game theory and mechanism design , developing theoretically sound yet practically applicable solutions to complex allocation problems. His work in causal inference has produced significant contributions to understanding the theoretical foundations of causal discovery algorithms. In combinatorial optimization , he has tackled challenging problems in kidney exchange and school choice systems, creating algorithms that balance efficiency, fairness, and strategic considerations. His recent work increasingly integrates machine learning techniques with traditional optimization approaches, particularly in the causal inference space. Analysis of Dr. Feigenbaum's publication record reveals a consistent focus on strategic decision-making in constrained environments. His work demonstrates a progression from theoretical foundations in mechanism design to increasingly complex real-world applications, particularly in healthcare (kidney exchange) and education (school choice). The recent emphasis on causal inference represents a natural extension of his expertise in algorithmic decision-making under uncertainty. His publications appear in top venues across computer science, operations research, and economics, reflecting the interdisciplinary nature of his contributions. Dr. Feigenbaum has established productive collaborations with researchers across multiple institutions, particularly with colleagues at Columbia University (where he completed his PhD under Jay Sethuraman) and various AI research labs. His work on causal inference has involved collaborations with researchers from major tech companies, as evidenced by publications like the Salesforce CausalAI Library framework.

• Supply Chain Management (SCM)
• Generative Artificial Intelligence (GenAI)
• Quantum Computing in Logistics
• +3 more

Prof. Joachim Ehrenthal is a Professor of Business Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), specializing in digital supply chain technologies. He holds a PhD from the University of St.Gallen and an MSc from Mannheim University. His research bridges generative AI, quantum computing, and real-world supply chain challenges, with a focus on educational integration and industry collaboration. Teaching activities include tech-centric courses in Digital Supply Chain Management at FHNW’s Bachelor and Master levels, enriched with industry co-developed case studies. He co-leads FHNW’s Competence Center Digital Supply Chain and collaborates with industry partners on GenAI and Quantum initiatives. Research highlights include integrating GenAI with the SCOR model for educational purposes and exploring quantum computing applications in logistics optimization. His work spans multi-agent systems, incident management, and digital twin implementations. He has published extensively in peer-reviewed journals and conferences since 2011, addressing topics like retail out-of-stocks, service-dominant logic, and supply chain resilience. Notable projects include a Swiss National Science Foundation (Innosuisse)-funded study on real-time railway resource disposition and digital transformation case studies in chocolate manufacturing and clinical trials. His expertise combines academic rigor with practical industry engagement, shaping next-generation supply chain practices through innovation.

• Theoretical Computer Science
• Algorithms
• Online Algorithms
• +3 more

Hans-Joachim Böckenhauer is a Lecturer at the Department of Computer Science at ETH Zürich, specializing in theoretical computer science with a focus on algorithms, online algorithms, and computational complexity. His research explores algorithmic optimization in problems like knapsack, graph exploration, and parameterized complexity. Affiliation: ETH Zürich, Department of Computer Science Research Interests: Online Algorithms, Graph Theory, Algorithm Design, Computational Complexity His work emphasizes the theoretical foundations of algorithms, including advice complexity and reoptimization strategies. Recent contributions address online knapsack variants, graph exploration with limited memory, and parameterized problem-solving.

• Online Algorithms
• Resource Allocation
• Energy Systems Optimization
• +4 more

Bo Sun is a Postdoctoral Scholar and Sessional Lecturer at the University of Waterloo. His research focuses on online algorithms, resource allocation, energy systems optimization, and machine learning applications. He is affiliated with the Post Doctoral Scholars, Visitors, and Sessional Lecturers groups at the institution. His work bridges theoretical computer science with practical challenges in energy networks, telecommunications, and distributed systems. Key research interests include developing fair and efficient algorithms for dynamic resource allocation problems, integrating machine learning predictions into competitive online algorithms, and optimizing spatiotemporal workloads with carbon-aware strategies. His publications address topics like group fairness in multi-class systems, carbon footprint reduction in workload management, and energy-efficient resource allocation in 5G networks. Recent work has explored Pareto-optimal algorithms for energy markets, robust online mechanisms with switching costs, and uncertainty-quantified predictions in algorithm design. Despite prolific publication activity, no scientific awards or grants are explicitly mentioned in the provided information. Bo Sun advises no formally listed students but has contributed to collaborative projects involving electric vehicle networks, battery swapping stations, and microgrid energy management. His research often involves interdisciplinary approaches combining operations research, computer science, and applied mathematics to solve real-world optimization challenges.

Ahmad AbdiView profile

Associate Professor

• Combinatorial Optimization
• Integer Programming
• Linear Programming
• +3 more

Ahmad Abdi is an Associate Professor in the Department of Mathematics at the London School of Economics and Political Science (LSE). He joined LSE as a tenure-track Assistant Professor in 2018 and was promoted to Associate Professor in 2023. He holds a PhD in Mathematics from the University of Waterloo, supervised by Bertrand Guenin, and completed a postdoctoral fellowship at Carnegie Mellon University under Gérard Cornuéjols. His research focuses on Combinatorial Optimization, Integer and Linear Programming, Matroid Theory, and Graph Theory, particularly the study of Ideal Clutters and their applications to open conjectures like Woodall’s and the Generalized Berge-Fulkerson conjecture. Education: PhD in Mathematics, University of Waterloo (2018) Postdoctoral Fellow, Carnegie Mellon University (2018–2019) Research Interests: Combinatorial Optimization and its applications to conjectures in Graph Theory (e.g., Woodall’s conjecture) Ideal Clutters and their connections to Linear Programming Matroid Theory and structural graph properties Grants: EPSRC New Investigator Award (2023–2026) for research on Woodall’s conjecture and the Generalized Berge-Fulkerson conjecture Students and Postdocs: PhD Students: Ruilan Wang (since 2019), Mahsa Dalirrooyfard (since 2022) Postdoctoral Fellows: Meike Neuwohner (since 2024), Tamás Schwarz (since 2025) Teaching: MA431 Spectral Graph Theory (2021/22) Combinatorial Optimization: Packing, Partitioning, and Covering (2023) Workshops: Co-organized the Cargese Workshop on Combinatorial Optimization (2022, 2024) and ACO@CMU Workshop (2020).

• Machine Learning
• Statistics
• Probability Theory
• +3 more

Dr. Steffen Grünewälder is a Senior Lecturer in Statistics at the University of York, Department of Statistics. Previously, he held roles as Senior Lecturer at Newcastle University (2022-2024) and Lecturer at Lancaster University (2014-2022). His research focuses on statistical learning theory, kernel methods, and reinforcement learning. He has contributed to advancements in Gaussian processes, Markov processes, and fairness in machine learning. His work spans theoretical developments and practical applications, including compressed empirical measures, stochastic processes, and algorithmic fairness. Key research interests include Bayesian methods, computational statistics, and optimization techniques in machine learning. Dr. Grünewälder’s recent publications explore topics such as deep Gaussian processes, Markov chain analysis, and reinforcement learning algorithms. He has collaborated on interdisciplinary projects, including ecological studies on animal behavior. He can be contacted via email at steffen.grunewalder@york.ac.uk .

Marek EliasView profile

Assistant Professor

• Algorithms
• Online Algorithms
• Differential Privacy
• +9 more

Marek Elias is an Assistant Professor at Bocconi University in Milan, where he is affiliated with the Theory Group . Prior to joining Bocconi, he held postdoctoral positions at CWI (Amsterdam) and EPFL (Lausanne), following a PhD at TU Eindhoven under the supervision of Nikhil Bansal. Earlier, he studied at Charles University in Prague with advisor Jiří Matoušek. His research focuses on algorithms for optimization under uncertainty , bridging online algorithms , differential privacy , and machine learning-augmented systems . Key areas include Learning-Augmented Algorithms Online Metric Algorithms Combinatorial Optimization Privacy-Preserving Mechanisms The trends in his recent publications highlight advancements in online optimization with applications in machine learning and data structures , particularly in problems like the k-server system , Steiner tree approximation , and differentially private clustering . Papers often explore the interplay between algorithmic robustness and predictive modeling to enhance efficiency in uncertain environments. He has contributed to major conferences including ICML, NeurIPS, SODA, and FOCS, with a consistent emphasis on theoretical guarantees and practical applications . While specific awards or grants are not detailed in the provided texts, his work is supported through institutional affiliations and collaborative research networks. Marek actively engages students in research, particularly in Bocconi's Theory Group , encouraging thesis projects on learning-augmented algorithms and prediction-based optimization . His laboratory environment fosters interdisciplinary collaboration between theoretical computer science and machine learning communities.

Anthony KimView profile

Assistant Professor

• Revenue Management
• Online Decision Making
• Algorithmic Game Theory
• +5 more

Anthony Kim is an Assistant Professor of Decision Sciences at the SKK Graduate School of Business, Sungkyunkwan University. His research focuses on revenue management, advertising, and online decision-making. Previously, he worked as an applied scientist at Amazon’s APMC team and held a postdoctoral position at Columbia Business School’s Decision, Risk, and Operations Division. He earned his Ph.D. in Computer Science from Stanford University (2018) and multiple degrees from MIT, including dual B.S. in Electrical Engineering/Computer Science and Mathematics (2009), and an M.Eng. (2010). Education: Ph.D., Computer Science, Stanford University, 2018 M.Eng., MIT, 2010 B.S. (Dual), MIT, 2009 Research Interests: Dr. Kim explores algorithmic solutions for dynamic pricing, auction mechanisms, and optimization under uncertainty. His work bridges theoretical computer science with practical applications in e-commerce, energy systems, and network resource allocation. Key areas include robust mechanism design, online budget management, and latency minimization in ride-sharing platforms. Professional Contributions: Program committee member for ACM Web Conference (2020–2025) Reviewer for Operations Research , Management Science , and other top journals Recipient of Research Paper Honorable Mention (2018 Web Conference) Grants & Labs: Active in industry collaborations through Amazon’s APMC team and academic research at Columbia’s DRO Division. His work integrates theoretical rigor with real-world marketplace challenges.