Find Academic Supervisors

Kees Jan RoodbergenView profile

Professor

• E-commerce Logistics
• Warehouse Optimization
• Vehicle Routing
• +3 more

Prof. dr. Kees Jan Roodbergen is a Professor of Quantitative Logistics at the University of Groningen and Director of the Center for Operational Excellence. He holds a MSc in Econometrics & Operations Research from the University of Groningen and a PhD from Erasmus University Rotterdam. His research focuses on E-commerce Logistics, Warehouse Optimization, Vehicle Routing, and Inventory Management, applying Operations Research techniques to practical challenges. He has led projects like PIONEER (2019), e-global (2017), and 'Towards Virtual Ports' (2015), securing external funding from TKI Dinalog, NWO, and Interreg IVB. He serves on the Scientific Advisory Council of the Global Cold Chain Foundation and has been involved in jury roles for e-commerce awards. His teaching spans courses on Value Chain Management, Facility Design, and Discrete Operations Research. Education: MSc from University of Groningen (Econometrics & OR), PhD from Erasmus University Rotterdam. Ancillary roles include editorial advisory boards for professional magazines and program boards for TRAIL Research School and Graduate Program Operations Management & Logistics. Research contributions emphasize practical applications, such as warehouse optimization methods integrated into management systems. Grants and Projects: Highlighted projects include PIONEER (TKI Dinalog), e-global (NWO), and collaborations with industry. His work addresses sustainable logistics, digital transformation, and operational efficiency. He has published in journals like Operations Research and Transportation Science.

• Operations Research
• Optimization
• Spatial Data Analytics
• +3 more

Derya Demirtas serves as Associate Professor at the University of Twente within the Center for Healthcare Operations Improvement & Research (CHOIR), Digital Society Institute, and TechMed Centre. Her academic home resides in the Department of Industrial Engineering and Business Information Systems under the Faculty of Engineering Technology. Education: BSc in Industrial Engineering & Computer Engineering, Middle East Technical University (2008) MMath in Combinatorics & Optimization, University of Waterloo (2010) PhD in Industrial Engineering, University of Toronto (2016) Dr. Demirtas specializes in applying operations research and optimization techniques to healthcare and humanitarian challenges. Her work integrates spatial data analytics with facility location theory to solve critical problems including emergency response systems, radiotherapy optimization, and disaster recovery logistics. Current research focuses on data-driven decision frameworks that address uncertainty in both demand patterns and resource availability. Her publication portfolio reveals strong emphasis on healthcare operations, with recent work exploring wearable technology for cardiac arrest detection, machine learning applications for nursing burnout prediction, and optimization models for emergency medical services. These contributions demonstrate consistent focus on translating theoretical operations research into life-saving practical implementations. Scientific Recognition: NWO Veni Talent Grant (2019) American Heart Association Young Investigator Award (2015) INFORMS Public Sector OR Best Paper Competition (2nd place, 2012) Best Poster Award, National Association of EMS Physicians (2016) Dr. Demirtas actively shapes academic discourse through editorial roles including Associate Editor for Health Care Management Science and Area Editor for Health Systems. Her supervision portfolio includes four PhD candidates working on defibrillator placement, South African hospital processes, clinical chemistry optimization, and radiotherapy logistics. Current research projects involve collaborations with UMC Amsterdam, HartslagNu, Dutch Heart Foundation, and Georgia Tech, focusing on improving cardiac arrest response systems and healthcare operations across multiple continents. Her laboratory work centers around the CHOIR research group which develops optimization frameworks for healthcare operations, with particular emphasis on emergency response systems and resource allocation under uncertainty. The TechMed Centre affiliation enables cross-disciplinary collaboration between engineering, medical, and data science experts to translate research into clinical practice.

Ilker BirbilView profile

Professor

• Optimization Methods
• Data Science
• Decision Making
• +3 more

Prof. Dr. Ilker Birbil is a Professor of AI & Optimization Techniques for Business & Society at the University of Amsterdam (UvA), affiliated with the Amsterdam Business School's Business Analytics section. He previously held professorships at Erasmus University and Sabancı University, focusing on optimization and data science. His research interests include interpretable machine learning, data privacy, and optimization methods in decision-making. Education: PhD in Operations Research from North Carolina State University Postdoc at Erasmus Research Institute of Management (ERIM), Netherlands Research Interests: Optimization in data science, interpretable AI, privacy-preserving algorithms, and decision-making systems. Recent work focuses on differentially private optimization and meta-learning techniques like LESS. Key Achievements: Recipient of multiple teaching awards at Sabancı University Affiliated researcher at OPTIMAL (Optimization for and with Machine Learning) Organized workshops on ML for optimization and mathematics of ML Grants & Teams: Leads the UvA group on Optimization and Machine Learning at LNMB. Collaborates on projects blending OR and ML, including privacy-aware algorithms and revenue management systems. Labs/Teams: OPTIMAL, UvA Optimization Group, and interdisciplinary teams in data analytics.

Joris WagenaarView profile

Associate Professor

• Transportation Optimization
• Supply Chain Management
• Integer Programming
• +1 more

Joris Wagenaar is Associate Professor of Business Analytics/Operations Research at Tilburg University. His research focuses on optimization problems in transportation systems and humanitarian supply chains, with applications ranging from railway networks to food distribution for the World Food Programme. Education: PhD in Business Processes, Logistics & Information Systems, Erasmus Research Institute of Management (2016) Research integrates combinatorial optimization with real-world logistics challenges. Recent projects include container loading optimization and predictive routing models using satellite data. Supervises PhD candidates Valentijn Stienen and Buis Boone on transportation analytics projects.

• Chemical Engineering
• Sustainable Chemistry
• Process Technology
• +5 more

Aloysius G.J. van der Ham is a Lecturer in Sustainable Process Technology, focusing on chemical engineering and sustainable chemistry. His research bridges fundamental thermodynamics with industrial applications, particularly in renewable energy integration and resource-efficient processes. Research Focus: Dr. van der Ham investigates sustainable chemical transformations, including electrified hydrocarbon production, hydrogen storage via liquid organic carriers, ionic liquid-based metal extraction, and carbon capture optimization. His work emphasizes: Electrification of industrial processes (ethylene/propylene production) Advanced membrane reactor designs Economic feasibility of renewable technologies Thermodynamic modeling of separation systems Publication Trends: Recent articles (2023-2025) demonstrate a consistent focus on decarbonizing chemical manufacturing through electrification, membrane-assisted reactions, and circular resource recovery. Key themes include techno-economic analysis of sustainable processes and thermodynamic optimization for energy systems. Supervision: He has guided 5 research projects, though specific student names or grant details are unavailable in this profile.

K.I. AardalView profile

Professor

• Algorithms
• Optimization
• Integer Programming
• +3 more

K.I. Aardal is a Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology, specializing in the Department of Discrete Mathematics and Optimization. Their research focuses on algorithms, optimization, integer programming, and combinatorial optimization, with significant contributions to operational research and mathematical programming. Recent work explores project scheduling , dynamic resource allocation , and lattice reformulations for integer programming. These studies integrate machine learning techniques with traditional optimization methods, emphasizing exact solutions and algorithm efficiency . Scientific Awards: INFORMS Fellow (2019) K.I. Aardal has supervised 10 formal advisees and serves as Editor for journals such as INFORMS Journal on Computing , RAIRO - Operations Research , and Mathematical Programming , contributing extensively to editorial activity since 2014.

Jasper BakkerView profile

Researcher

• Network Design
• Electric Road Systems
• Fleet Renewal
• +5 more

Jasper Bakker, MSc is an active researcher specializing in sustainable transportation infrastructure with a focus on electric road systems and fleet electrification strategies. His work bridges computer science applications with practical transportation solutions, contributing to UN Sustainable Development Goals related to clean energy and sustainable cities. His research interests center on network design for electric transportation infrastructure, with particular expertise in adoption potential analysis of electric road systems during early development stages. His work examines infrastructure investment strategies, fleet renewal processes, and the integration of electric trucks into existing transportation networks, employing computational methods to optimize system performance. His publication record shows a clear trajectory of increasingly sophisticated research on transportation electrification, with recent work focusing on strategic fleet replacement models using mixed-integer programming approaches. His 2025 paper in Applied Energy represents the most current work in this emerging field. Bakker has established productive collaborations with researchers including Lopez Alvarez, Veldman, and Buijs, suggesting affiliation with an institution focused on operations research and sustainable transportation solutions. His work demonstrates strong methodological rigor with applications to real-world decarbonization challenges in the freight transportation sector.

• Electric Power Distribution
• Load Profile Analysis
• Distribution Systems
• +5 more

Mauricio Salazar Duque serves as a University Researcher in the Department of Electrical Energy Systems within the School of Electrical Engineering at Eindhoven University of Technology (TU/e). His work focuses on modernizing power distribution infrastructure through advanced computational techniques. His research expertise spans critical domains in electrical grid modernization: Electric Power Distribution & Load Profile Analysis Distribution System Optimization & Photovoltaics Integration Community Energy Storage & Reinforcement Learning Applications His publications consistently address UN Sustainable Development Goals related to affordable clean energy and climate action. Recent work demonstrates strong emphasis on data-driven modeling for grid resilience. Analysis of his 2023-2025 publications reveals a strategic shift toward generative AI for load profile synthesis and tensor-based mathematical formulations for multidimensional grid analysis. Key themes include handling PV integration challenges in low-voltage networks and developing visualization techniques for medium-voltage load anomalies. No scientific awards are documented in available records. He contributed to the 2018-2022 project 'Decentrale Optimalisatie en Sturing van Elektriciteitsdistributienetten' as a research member and supervised at least one Master's thesis on transfer learning for medium-voltage network measurements. His research collaborations extend through TU/e's press engagements including National Growth Fund initiatives and mobility systems workshops. Salazar Duque operates within TU/e's Electrical Energy Systems research group, collaborating with Prof. J.G. Slootweg and international partners on smart grid innovation projects.

Renata SotirovView profile

Professor

• Optimization
• Operations Research
• Mathematical Programming
• +3 more

Renata Sotirov is a Full Professor at Tilburg University's Department of Econometrics & Operations Research within the Tilburg School of Economics and Management. She holds a PhD in Mathematics and has held academic positions including Associate Professor (2009–2014) and Assistant Professor (2007–2009). Her research focuses on optimization, operations research, and semidefinite programming with applications in combinatorial optimization and discrete mathematics. She has supervised PhD students such as Dr. Ning Ma and Dr. Uwe Truetsch and collaborates internationally, including with institutions like the University of Melbourne and McMaster University. Her work includes pioneering contributions to semidefinite relaxation techniques, integer SDPs, and algorithmic optimization. Recent projects involve the Chvátal-Gomory procedure for integer SDPs and complex cut polytopes. She has served as a research coordinator, director of graduate studies, and treasurer for her department. Her expertise spans teaching operations research methods, combinatorial optimization, and quantitative business analytics.

• Exploratory Data Analysis
• Dimension Reduction
• Cluster Analysis
• +7 more

Michel van de Velden is an Associate Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. His work bridges statistical methodology and data science, with a focus on exploratory techniques and machine learning. His research interests lie primarily in exploratory data analysis , with a focus on dimension reduction , cluster analysis , and data visualization . He also actively contributes to supervised machine learning , particularly using tree-based methods such as random forests. His methodological work often addresses challenges in categorical data and mixed-type variables . The recent articles highlight a strong trend in developing exact and unbiased statistical methods for clustering and distance computation. His work spans correspondence analysis , hierarchical clustering algorithms , and software tools for mixed data, reflecting a blend of theoretical rigor and practical implementation. He has served on the editorial boards of Computational Statistics and Statistical Papers , contributing to academic peer review and publication quality. Michel van de Velden has supervised academic work and contributed to open datasets and software, indicating active mentorship and collaboration. His research is supported by ongoing publications and editorial activities, suggesting sustained engagement in the academic community. He is involved in developing software such as manystat and datasets related to categorical data visualization, indicating leadership in reproducible and open research practices.

Twan DollevoetView profile

Associate Professor

• Combinatorial Optimization
• Railway Optimization
• Transportation Systems
• +3 more

Twan Dollevoet is an Associate Professor at the Econometric Institute of the Erasmus School of Economics, Erasmus University Rotterdam. His research focuses on combinatorial optimization, with applications in railway systems, transportation networks, and public transport logistics. Research Interests: Dr. Dollevoet investigates optimization challenges in transportation, including rolling stock rescheduling, crew planning fairness, electric vehicle integration, and kidney allocation policies. His work combines mathematical modeling with practical applications in rail networks and sustainable transport. Recent Publications: His 15 most recent articles focus on heuristic frameworks for railway optimization, fairness in crew scheduling, electric bus logistics, and robust timetabling. Key themes include real-time rescheduling under infrastructure constraints, ALNS algorithms for electric fleets, and trade-offs between equity and efficiency in workforce management. Students: He has supervised PhD students including Rowan Hoogervorst (rolling stock rescheduling) and Thomas Breugem (fairness in crew planning).

Eric VerbeekView profile

Researcher

• Process Mining
• Process Models
• Process Discovery
• +3 more

H.M.W. (Eric) Verbeek is an ICT Developer in the Department of Mathematics and Computer Science at Eindhoven University of Technology, specializing in Process Analytics. His work focuses on advancing process mining techniques, standardization of event data (e.g., IEEE XES), and the development of tools like ProM and CPN IDE. He has contributed to over 160 publications since 1986, with recent research emphasizing process discovery algorithms, workflow net synthesis, and context-rich process modeling. Research Interests include Process Mining, Process Models, Process Discovery, Petri Net Analysis, and Workflow Management. He has actively participated in projects like the GDPR compliance toolkit and has been involved in defining standards for event data interoperability. Notable contributions include the IEEE XES standard for process mining, tools for event log generation and analysis, and methods for decomposed conformance checking. His work bridges theoretical advancements in process modeling with practical tool development, supporting both academia and industry applications. Collaborations span international academia, focusing on process mining workflows and compliance monitoring. His research trends highlight a balance between algorithmic innovation and real-world applicability in areas like healthcare, logistics, and service management.

• Mathematical Programming
• Correspondence Analysis
• Hierarchical Clustering
• +7 more

Rick Willemsen is a Lecturer in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. His academic work lies at the intersection of mathematical programming, statistical data analysis, and computational optimization. Research Interests: His primary research focuses on correspondence analysis, hierarchical clustering, and dimension reduction techniques. He employs mathematical and mixed integer programming to develop exact and inverse solutions for statistical problems involving categorical data and contingency tables. His methodological contributions aim to improve the accuracy and interpretability of clustering and dimensionality reduction methods in econometric and data science applications. Publication Trends: Over recent years, Rick has published on exact algorithms for hierarchical clustering, uniqueness conditions in correspondence analysis, and novel optimization approaches for inverse problems. His research consistently integrates theoretical rigor with practical computational implementation, as evidenced by both journal articles and software outputs. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no public information available on students advised or grants received. However, his recent completion of a doctoral thesis suggests he is in the early stage of his independent academic career. Labs and Research Teams: Rick Willemsen collaborates closely with researchers such as Michel van de Velden and Willem van den Heuvel, likely within the Data Science or Quantitative Methods group at Erasmus School of Economics. His work appears to be part of a broader effort in developing optimization-based tools for statistical data analysis.

• Operations Research
• Stochastic Programming
• Mixed-Integer Optimization
• +7 more

Ruben van Beesten is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. He also holds an adjunct professorship at the Norwegian University of Science and Technology since September 2023, reflecting his international academic engagement and collaborative research. His research lies at the intersection of operations research, mathematical optimization, and applied economics. His primary interests include distributionally robust optimization, stochastic programming with integer recourse, error bounds in convex approximations, and energy system modeling. His work combines theoretical rigor with practical applications, particularly in energy markets and decision-making under uncertainty. The recent publications of Ruben van Beesten demonstrate a consistent focus on improving the tractability and accuracy of two-stage mixed-integer recourse models. His work spans theoretical developments in error bounds and parametric analysis, algorithmic improvements for energy system design, and market-level applications such as interconnector capacity incentives. The research trends highlight a strong emphasis on computational optimization, risk-averse decision frameworks, and the integration of robust methods in real-world systems. His scientific contributions are recognized through publications in top-tier journals such as SIAM Journal on Optimization , Computational Optimization and Applications , and Energy . While no formal awards are listed in the provided text, his active publication record and external collaborations indicate a growing academic impact. Ruben van Beesten collaborates with prominent researchers such as Ward Romeijnders, Kees Jan Roodbergen, and Asgeir Tomasgard. His research involves both theoretical grants in optimization methodology and applied projects in energy systems, suggesting involvement in funded interdisciplinary initiatives, though specific grants are not detailed. He has co-authored multiple peer-reviewed articles and a doctoral thesis, indicating a strong research trajectory. He is actively involved in a research network focused on optimization under uncertainty, with strong ties to institutions in Norway and the Netherlands. His work contributes to both academic and practical advancements in energy modeling and stochastic programming, positioning him within a dynamic and impactful research community.

Rob van der MeiView profile

Professor

• Stochastic Operations Research
• Queueing Theory
• Emergency Response Systems
• +17 more

Rob van der Mei is a Distinguished Full Professor (part-time) at Vrije Universiteit Amsterdam and a Principal Researcher and Manager R&D at Centrum Wiskunde & Informatica (CWI), where he leads research at the intersection of theory and application in Stochastic Operations Research. His work spans emergency response systems, logistics, healthcare, telecommunications, and cybersecurity. He is the chair of the European COST Action ACROSS and co-founder of the spin-off Stokhos Emergency Mathematics. PhD in Stochastic Operations Research – Vrije Universiteit Amsterdam Senior Researcher – AT&T Labs, USA (1995–1999) Senior Researcher – KPN Research (1999–2002) Senior Researcher – TNO ICT (2002–2004) His research focuses on applying mathematical modeling, queueing theory, and data analytics to optimize complex systems. Key areas include emergency response, service logistics, healthcare optimization, and machine learning in telecommunications. He has led numerous projects on data-driven decision-making in healthcare, such as suicide prevention helplines and elderly care systems, and in maritime design under energy transition uncertainty. The most recent publications reflect a strong trend toward interdisciplinary and socially impactful research, combining optimization, machine learning, and real-time decision systems. Topics include mobile stroke unit deployment, AI-assisted counseling, cyber anomaly detection, and energy-efficient ship design. These works highlight his expertise in translating theoretical models into practical tools. Harald W. Kuhn Award 2020 (2021) Huibregtsen Award 2021 First prize for logistic innovation in Covid-19 vaccination strategy (2021) Best-paper award, 8th International Conference on Data Analytics (2019) Runner-up Huibregtsen-prijs 2014 Rob van der Mei has secured extensive external funding from NWO, EU COST, and industry partners for projects in healthcare logistics, emergency systems, and digital infrastructure. He advises PhD students and collaborates widely across academia and industry. He leads major initiatives such as DOLCE VITA, COCONUT, and ACROSS, and is an active keynote speaker and invited lecturer on data science and optimization. He is affiliated with the Stochastics group at CWI, where he supervises researchers and contributes to national and international research strategy.