Find Academic Supervisors

Ferenc KruzsliczView profile

Associate Professor

• Business Informatics
• Data Mining
• Text Mining
• +3 more

Dr. Ferenc Kruzslicz serves as an Associate Professor in the Department of Business Informatics at the Faculty of Business and Economics, University of Pécs, with office B122 and email kruzslicz.ferenc@ktk.pte.hu. His academic journey began with a theoretical mathematics degree from József Attila University of Szeged, followed by teaching positions at Veszprém and Pécs Universities. He earned his absolutorium from Szeged's Informatics Doctoral School in 1998 and completed doctoral studies in Economics at Pécs' Faculty of Economics. His research spans Business Informatics , Data/Text Mining , and Business Intelligence , with expertise in opinion analysis and intelligent web search engines. Current work focuses on data collection methodologies and optimization problems in logistics. Recent publications reveal an interdisciplinary trajectory: applying computational methods to business contexts (SME competitiveness), advancing cluster labeling techniques through external text integration, and solving biomass logistics scheduling via mathematical programming. As a founding member of Pécs' business informatics programs, he led accreditation efforts and managed full-time curricula. He established the university's Big Data research group, institutionalizing business intelligence education while developing content management systems through applied project management.

• Numerical Optimization
• Mixed-Integer Nonlinear Programming
• Global Optimization
• +14 more

Ruth Misener is a Professor in the Department of Computing at Imperial College London, where she leads the Computational Optimization Group and holds the BASF/RAEng Research Chair in Data-Driven Optimization (2022–2027). She is affiliated with the Faculty of Engineering and contributes to interdisciplinary research institutes including the Data Science Institute, the Institute for Molecular Science and Engineering, and the Sargent Centre for Process Systems Engineering. Her research lies at the intersection of numerical optimization, operations research, and machine learning, with applications in chemical engineering, bioprocess optimization, energy systems, and industrial scheduling. She develops global optimization algorithms for mixed-integer nonlinear programs (MINLP), focusing on real-world challenges such as heat recovery network design, petrochemical process optimization, and robust bioreactor operation. A key innovation is her work on optimizing over machine learning surrogates, including tree ensembles and neural networks, enabling data-driven decision-making under uncertainty. Her recent publications demonstrate a strong trend toward integrating Bayesian optimization with active learning, explainable AI, and industrial applications, particularly in collaboration with BASF, Royal Mail, and Eli Lilly. She develops and maintains open-source optimization tools such as ROmodel, OMLT, and ENTMOOT, which are publicly available on GitHub. STEM for Britain acceptance Runner-Up Presentation Award at PSE@ResearchDayUK Best Quality Poster to Simon Olofsson 1st Poster Prize at UK/Ireland Annual Meeting of the Society for Industrial & Applied Mathematics (2018) 2nd Poster Prize at Centre for Process Systems Engineering Industrial Consortium Meeting (2017) 1st Poster Prize at 2nd PSE@ResearchDayUK (2017) 2nd Presentation Prize at Department of Computing Research Associate Symposium (2017) Runner-Up for May Hicks Award (via student Natasha Page) Ruth supervises a dynamic research team and has examined and mentored numerous PhD students, including Jean Kossaifi, Robert Walecki, Alexander Thebelt, and Toby Boyne. She leads major research grants, including the BASF/RAEng Research Chair and the IConIC Prosperity Partnership, and collaborates with industry partners to advance continuous manufacturing and data-driven process optimization. Her team actively disseminates work through open-access publications, video presentations, and social media.

Evrim DalkiranView profile

Associate Professor

• Global Optimization
• Reformulation-Linearization Technique (RLT)
• Mixed-integer Nonlinear Programming (MINLP)
• +9 more

Dr. Evrim Dalkiran is an Associate Professor in the Department of Industrial and Systems Engineering at Wayne State University's College of Engineering. She holds a Ph.D. in Industrial and Systems Engineering from Virginia Tech (2011), and B.S./M.S. from Bogazici University (2003, 2006). Her work focuses on global optimization and decision analysis with applications in healthcare operations and supply chain management. B.S., Industrial Engineering, Bogazici University, Turkey M.S., Industrial Engineering, Bogazici University, Turkey Ph.D., Industrial and Systems Engineering, Virginia Tech Her research spans Global Optimization , Reformulation-Linearization Technique , Mixed-Integer Programming , and Multi-Objective Optimization , with application domains in Healthcare Operations , Emergency Patient Flow , Supply Chain Management , and Diabetes Risk Modeling . Current research includes real-time coordination tools for emergency departments and inverse optimization models. Her publications demonstrate expertise in polynomial programming , RLT implementations , and healthcare logistics , with recent work on automotive sustainability and smart grid allocation . Articles span journals like European Journal of Operational Research and Operations Research . Best Paper Award, Journal of Global Optimization, 2012 Outstanding Faculty Award, WSU, 2016 Pritsker Dissertation Award Finalist, 2012 IIE Student Paper Competition Advisor, multiple awards As faculty advisor, she mentors students in projects related to emergency department optimization and supply chain modeling . Her grant history includes multiple Veterans Administration projects on patient flow and Goodyear-funded supply chain optimization. She teaches advanced optimization courses and maintains professional affiliations with INFORMS and Mathematical Optimization Society.

• Learning to optimize
• Optimization under uncertainty
• Efficient logistics and transport
• +4 more

Prof. Dr. Kevin Tierney is a Full Professor for Decision and Operation Technologies at Bielefeld University's Faculty of Business Administration and Economics. He also serves at the Department of Management Science & Business Analytics and is affiliated with the Bielefeld Center for Data Science (BiCDaS) and Center for Uncertainty Studies (CeUS). Chair of Business Administration, Decision and Operation Technologies Member of BIGSEM Graduate School PhD (2013) - IT University of Copenhagen Sc.M. (2010) & BS (2008) - Brown & RIT Research Interests His work focuses on: Learning to Optimize: Using deep reinforcement learning to automate solution heuristics for complex problems like routing and scheduling. Optimization under Uncertainty: Developing models that incorporate probabilistic elements for decision-making in unpredictable environments. Efficient Maritime Logistics: Specializing in container shipping, terminal operations, and fleet routing with real-world constraints. Recent publications demonstrate expertise in algorithm configuration, constraint programming, and machine learning applications to logistics challenges. Scientific Recognition Distinguished Paper Award - European Conference on Artificial Intelligence (2020) Projects & Grants Principal Investigator in projects: Self-learning methods with Deep Reinforcement Learning (DFG 2026) itsowl-MOVE (Land NRW 2024) AIPlan4EU Meta-planning engine (EU H2020 2023) Academic Leadership Module responsible for: Quantitative Business Administration Data Science Production and Operations Management

Bart M.L. SmeuldersView profile

Assistant Professor

• Combinatorial Optimization
• Approximation Algorithms
• Operations Research
• +5 more

Bart M.L. Smeulders is an Assistant Professor at Eindhoven University of Technology within the Mathematics and Computer Science school, specializing in Combinatorial Optimization . His research spans: Healthcare policy modeling for organ allocation systems Algorithmic game theory in transplantation markets Robust optimization techniques for medical logistics Key research outputs include: 2025: Policy evaluation simulator for Eurotransplant 2024: Kidney exchange complexity and optimization approaches 2025: Gender disparity analysis in liver allocation His work intersects computer science, operations research, and medical decision-making, contributing to UN Sustainable Development Goal 3 (Good Health and Well-being).

Christina PapadimitriouView profile

Assistant Professor

• Smart Grids
• Integrated Grid Operation
• Renewable Energy Integration
• +7 more

Christina Papadimitriou is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), affiliated with the Intelligent Energy Systems and Electrical Energy Systems research groups. Her work focuses on smart grid operation, integrated energy systems, and grid flexibility for decarbonization. PhD and Diploma in Electrical & Computer Engineering from University of Patras Senior Researcher at National Technical University of Athens (2012-2016) System Engineer at Hellenic Distribution Network Operator (2016-2019) Special Scientist at University of Cyprus (2019-2021) Her research explores: Smart grid resilience through active asset flexibility Demand-side optimization in integrated grids Power-to-X (PtX) technology operation models Energy hub coordination with logistics fleets Multi-energy carrier synergies in residential clusters Recent work demonstrates: Grid capacity enhancement via dynamic charging strategies (IEEE Access 2024) Multi-objective PtX optimization tools (CRC Press 2025) Smart grid maturity assessment frameworks (Electric Power Systems Research 2024) She coordinates the eNeuron project under Horizon2020 and serves as peer reviewer for multiple journals.

David REYView profile

Professor

• Optimization
• Integer Programming
• Bilevel Optimization
• +7 more

David Rey is a Professor at SKEMA Business School in Sophia Antipolis, France. His research focuses on optimization, game theory, and artificial intelligence applied to transportation and logistics. He holds a PhD in Operations Research from Université Grenoble Alpes (2012) and a Habilitation à Diriger des Recherches (2023) from Université de Toulouse. Previously, he was a Senior Lecturer at UNSW Sydney (2016–2021) and has held postdoctoral positions at rCITI UNSW. He leads the URBANE project, exploring green last-mile delivery solutions, and serves on editorial boards like Transportation Letters . Awards include multiple ARC grants and the INFORMS TSL Best Paper Award Committee role (2025). Education: Habilitation à Diriger des Recherches (2023), Operations Research, Université de Toulouse PhD (2012), Operations Research, Université Grenoble Alpes Master (2008), Sciences/Mathematics, Pontifical Catholic University of Rio de Janeiro Engineering Degrees (2004–2005), Electrical Engineering, University of Montpellier Research Interests: David’s work emphasizes optimization techniques (bilevel, stochastic, mixed-integer programming), game theory, and AI applications in transport systems. Key areas include drone delivery, autonomous vehicle integration, congestion pricing, and resilient logistics networks. His methods address real-world challenges like pandemic response, electric vehicle charging infrastructure, and fair resource allocation. Grant Highlights: URBANE (EU Horizon-funded), 2022–present 4 ARC grants as Lead Chief Investigator, including projects on concrete mixes, ethics in transport systems, and workforce logistics optimization Advising & Grants: Directed/co-directed 15+ PhD theses across SKEMA and UNSW Sydney Active in grant writing and editorial roles, including Transportation Science and Logistics Society Workshop (2024–2025) Labs/Teams: Leads the SKEMA Centre for Analytics and Management Science, focusing on analytics-driven solutions for business and urban systems.

• Transportation and Logistics
• Vehicle Sharing Systems
• Vehicle Routing Problems
• +4 more

Ataç Selin is a Researcher at the University of Applied Sciences and Arts Western Switzerland (HES-SO), affiliated with the Interdisciplinary Institute for Business Development (IIDE) at HEIG-VD. She holds a PhD in Transportation Science from EPFL (2023) and focuses on optimizing vehicle sharing systems, logistics, and sustainable urban mobility. Her work integrates simulation-optimization frameworks to address challenges in bike/car sharing systems, electric vehicle infrastructure, and craft beer distribution logistics. Education: PhD in Transportation Science (2023), École Polytechnique Fédérale de Lausanne (EPFL) MSc in Operations Research (2016), Middle East Technical University (METU) BSc in Industrial Engineering (2013), Middle East Technical University (METU) Research Interests: Her research spans vehicle routing optimization, demand forecasting in transportation networks, and decision-making frameworks for sustainable systems. She has pioneered a holistic management framework for vehicle sharing systems, emphasizing rebalancing strategies, clustering algorithms, and environmental impact mitigation. Key Projects: "Craft Beer Distribution Optimization" (2024), funded by Innosuisse, focuses on collaborative logistics platforms for Swiss craft breweries. "Light Electric Vehicle Sharing Systems" (2024), developing decision support tools for EV infrastructure. Awards: Recipient of the EPFL EDCE Mobility Award (2021) for contributions to VSS optimization and the METU Best Graduate Performance Award (2016).

• Robotics
• Cybersecurity
• Automation and Robotics
• +4 more

Wojciech Szynkiewicz is an Associate Professor at the Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology. His research focuses on robotics, cybersecurity, and automation, with a particular emphasis on human-aware navigation, cyber-physical systems security, and sensor data analysis. He has contributed to projects such as the RAPP platform for robotic applications and the National Cybersecurity Platform. Education: Doctor of Technology in Automation and Robotics (1996), Habilitation in Automation and Robotics (2016). Research interests include path planning for cooperative robots, anomaly detection in robotic systems, and deep learning applications in cybersecurity. He has authored over 70 publications, including works on benchmarking social robot navigation (SRPB) and cybersecurity protocols for mobile service robots. Notable achievements include the 1st Team Award for Scientific Achievements (2013-2014) and the 2nd Individual Award (2016) from the Rector of Warsaw University of Technology. His work on multi-agent robotic systems and reconfigurable fixtures has led to advancements in collaborative robotics and industrial automation. Current projects involve developing secure communication frameworks and energy-efficient sensing infrastructure.

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.

• Civil Engineering
• Optimisation
• Applied Mathematics
• +3 more

Aly-Joy Ulusoy is an Imperial College Research Fellow in the Department of Civil and Environmental Engineering at Imperial College London, affiliated with the Faculty of Engineering and the Grantham Institute. Her research focuses on optimisation methods for the design and control of water distribution networks, integrating adaptive systems, hydraulic modeling, and resilience analysis. She holds a Ph.D. (2021) and M.Sc. (2016) from Imperial College London, and an M.Sc. from École Supérieure d’Électricité, France (2016). Previously, she worked as a postdoctoral researcher in Dr. Ivan Stoianov's InfraSense Labs. Education: Ph.D. in Civil and Environmental Engineering, Imperial College London (2021) M.Sc. in Civil and Environmental Engineering, Imperial College London (2016) M.Sc., École Supérieure d’Électricité, France (2016) Her research interests span optimisation algorithms for water network control, adaptive hydraulic systems, data-driven hydraulic modeling, and resilient infrastructure design. Recent work emphasizes dynamically adaptive networks for pressure management, fault localization, and integration of AI for demand forecasting. She explores multi-objective optimization techniques to balance operational efficiency and resilience in water distribution systems. Key research trends include: (1) distributed optimization for real-time control under time-coupling constraints, (2) interpretable AI for water demand prediction, and (3) principled approaches for model maintenance using principal component analysis. Her work bridges theoretical optimisation with practical water infrastructure challenges. Advising and grants: No specific advisees or grants listed. Affiliated with InfraSense Labs, focusing on smart infrastructure and sensor networks. Active in developing methodologies for network resilience under uncertainty. Ongoing projects include adaptive MPC strategies for burst incident management and design-for-control frameworks for dynamically adaptive systems.

• 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.

Ricardo G. SanfeliceView profile

Professor

• Hybrid Dynamical Systems
• Nonlinear Control
• Robust Control
• +9 more

Ricardo G. Sanfelice is Professor and Chair of the Department of Electrical and Computer Engineering at the University of California, Santa Cruz (UCSC), within the Baskin School of Engineering. He is also Director of the Cyber-Physical Systems Research Center and the CITRIS Aviation Initiative. Education: Ph.D., Electrical and Computer Engineering, University of California, Santa Barbara, 2007 M.S., Electrical and Computer Engineering, University of California, Santa Barbara, 2004 B.S., Electronic Engineering, Universidad Nacional de Mar del Plata, Argentina, 2001 His research focuses on modeling, stability, robust control, and observer design for nonlinear and hybrid systems, with applications in robotics, aerospace, power systems, and biological systems. He leads the Hybrid Systems Lab at UCSC and has contributed significantly to the theoretical foundations and practical implementations of hybrid feedback control and cyber-physical systems. The most recent publications highlight a strong trend toward computation-aware control, hybrid model predictive control, and set-valued methods for safety-critical systems. His work bridges theoretical rigor with real-world applications, particularly in autonomous vehicles and networked control systems. Scientific Awards and Honors: Fellow of IEEE ACM Test-of-Time Award (HSCC Conference) SIAM Control and Systems Theory Prize NSF CAREER Award Air Force Young Investigator Award (YIP) IEEE Control Systems Magazine Outstanding Paper Award Educator of the Year for Higher Education Multiple plenary and semiplenary speaking invitations Blavatnik National Award Campus Nominee ASEE Air Force Summer Faculty Fellow Senior Member of IEEE Prof. Sanfelice has advised several graduate students, including Jun Chai and Dawn Hustigs-Schultz, and has been involved in significant research grants and initiatives such as the AFOSR Center of Excellence and CITRIS Aviation Prize. He is Associate Editor for Automatica and has led the IEEE Technical Committee on Hybrid Systems. He leads the Hybrid Systems Lab at UCSC, which focuses on theoretical and applied aspects of hybrid dynamical systems, including simulation tools like SHARC (Simulator for Hardware Architecture and Real-time Control).

Margarida CarvalhoView profile

Associate Professor

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

Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal, where she holds the FRQ-IVADO Research Chair in Data Science for Combinatorial Game Theory. She is also an Associate Academic Member at Mila (Quebec AI Institute), contributing to their research in AI for Humanity. Her academic journey spans from Portugal to Canada, where she has established herself as a leading researcher at the intersection of operations research and game theory. Carvalho earned her bachelor's and master's degrees in mathematics from the Faculty of Sciences of the University of Porto (FCUP), followed by a PhD in Computer Science from the same institution in 2016. Her doctoral work, which focused on game theory applications for kidney exchange programs, earned her the prestigious 2018 EURO Doctoral Dissertation Award, making her the first Portuguese woman to receive this honor. After completing her PhD, she worked as an IVADO Postdoctoral Fellow at Polytechnique Montréal before joining Université de Montréal as an Assistant Professor in 2018. Her research focuses on combinatorial optimization and algorithmic game theory, with applications spanning healthcare (kidney exchange programs, hospital operations), sustainable development (electric vehicle infrastructure, urban planning), and education (school choice systems). She develops novel mathematical programming approaches to model and solve problems involving multiple decision-makers with potentially conflicting objectives. Her work bridges theoretical advances in optimization with practical implementations that address real-world challenges in resource allocation and decision-making under uncertainty. Notably, her research on fairness in kidney exchange programs has contributed to more equitable organ allocation policies. Her 15 most recent publications reveal a strong trend toward integrating game-theoretic concepts with practical optimization challenges, particularly in healthcare and sustainable infrastructure. She has pioneered approaches that balance utilitarian objectives with fairness considerations, developed novel formulations for bilevel and multilevel optimization problems, and created learning-based frameworks for complex decision environments. Her work consistently demonstrates how mathematical rigor can inform practical policy decisions in critical domains. 2018 EURO Doctoral Dissertation Award for her PhD thesis on game theory applications for kidney exchange programs Mathematical Programming 2024 Meritorious Service Award Teaching Excellence Award from Université de Montréal Supervised student Maria Bazotte receiving the Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming Carvalho actively advises graduate students, with Marylou Fauchard (Master's) and William St-Arnaud (PhD) among her current advisees. Her research is supported by grants from Hydro-Québec, the Natural Sciences and Engineering Research Council of Canada (Discovery grant 2017-06054 and Collaborative Research and Development Grant CRDPJ 536757–19), and FRQ-IVADO. She serves as an associate editor for INFORMS Journal on Computing, OR Spectrum, and Dynamic Games and Applications, and is a founding board member and treasurer of the Bilevel Optimization Society. She teaches courses in Mathematical Programming, Operational Research Models, and Discrete Mathematics at Université de Montréal. Carvalho is affiliated with Mila (Quebec AI Institute), where she contributes to research initiatives focused on AI for Humanity, particularly in the areas of algorithmic fairness and sustainable development. Her FRQ-IVADO Research Chair supports her work on combinatorial game theory applications, and she collaborates with researchers across disciplines through the IVADO research community. She has been instrumental in establishing the Bilevel Optimization Society, creating a dedicated forum for researchers working on hierarchical decision-making problems.

• Operations Research
• Combinatorial Optimization
• Bilevel Optimization
• +6 more

Ivana Ljubic is a Full Professor in Operations Research at ESSEC Business School, specializing in combinatorial optimization, bilevel optimization, and network design with applications in telecommunications, logistics, and bioinformatics. She holds a PhD in Computer Science from TU Wien (2004) and a Habilitation from the University of Vienna (2013). Editorial roles: Associate Editor for Operations Research , Transportation Science , and Networks Professional leadership: Chair of INFORMS Telecommunication & Network Analytics Section Her research focuses on mixed-integer programming, metaheuristics, and exact algorithms for problems like Steiner trees, regenerator location, and capacitated vertex separators. Recent work includes applications in community energy storage, social media influence maximization, and sustainable last-mile delivery. She has received multiple awards, including the Glover-Klingman Prize (2021) and Marguerite Frank Award (2022). Her publications span leading journals like Management Science , Operations Research , and Mathematical Programming , with over 70 articles to date.