Find Academic Supervisors

• Foundations of Complex Systems
• Abelian Varieties
• Finite Fields
• +5 more

Valentijn Karemaker is an Assistant Professor at the Faculty of Science, Utrecht University , specializing in the Mathematical Institute under the Fundamental Mathematics sub-unit. He focuses on the Foundations of Complex Systems , with particular expertise in Abelian Varieties , Finite Fields , Galois Representations , and Isogeny Classes . His research bridges theoretical mathematics and computational methods, notably contributing to problems like Hilbert's Tenth Problem and the Gauss problem for central leaves . He has published extensively in top-tier journals and co-authored significant surveys on local-global principles. Scientific Awards : NWO-Veni Award (2019) Westerdijk Award (2021-2022) Utrecht Young Academy (2021) NWO-XL Award (2022) NWO-Vidi Award (2023) Grants and Collaborations : He has received competitive NWO awards, hosted international researchers like Chia Fu Yu and Mihran Papikian, and contributed to editorial work for the American Mathematical Society.

Arturo Tejada RuizView profile

Assistant Professor

• Safe Autonomous Vehicles
• Human-Machine Interaction
• Motion Control
• +4 more

Arturo Tejada Ruiz is a Part-time Assistant Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e), specializing in Safe Autonomous and Cooperative Vehicles . He also serves as a Senior Scientist at TNO in the Integrated Vehicle Safety department, supporting OEMs and regulators in certifying automated vehicles. His research integrates human factors , artificial intelligence , and motion control to enable socially acceptable self-driving systems. Education : BSc/MSc/PhD in Electrical Engineering (Pontificia Universidad Católica del Perú, Old Dominion University) Research Focus : Developing reference models of human driving to extract safety requirements for automated vehicles, with emphasis on probabilistic risk evaluation and real-time collision probability estimation. Recent work includes the PRISMA method for data-driven risk measures and novel approaches for multi-circular shape approximations in collision probability estimation. Publications highlight advancements in: Motion planning algorithms for autonomous vehicles Stochastic and robust model predictive control Human-centric safety certification frameworks His work bridges theoretical modeling with industrial applications, particularly in quantifying risk trends and improving computational efficiency for real-world deployment.

Yaoxin WuView profile

Assistant Professor

• Deep Learning
• Combinatorial Optimization
• Integer Programming
• +5 more

Yaoxin Wu is an Assistant Professor at the Eindhoven University of Technology, affiliated with the Department of Industrial Engineering and Innovation Sciences. His research bridges deep learning and combinatorial optimization to solve complex problems in transportation, scheduling, and network design. Education : PhD in Computer Science from Nanyang Technological University (2023). Wu specializes in artificial intelligence and operations research , focusing on graph neural networks, stochastic programming, and multi-objective optimization. His work has significant applications in UAV routing and on-demand delivery systems. His 2025 publications highlight trends in neural combinatorial optimization for stochastic job shop scheduling, ride-hailing, and drone logistics. Key subfields include deep reinforcement learning, preference modeling, and topological graph learning. He has supervised 9 students, including PhD candidates Xia Jiang and Igor Smite, and Master’s students like Venkata Roshan Mannepu and Floor Halkes. Wu's research is funded by projects like LEO (Holland High Tech | TKI HSTM) and SURF Cooperative grants. His educational activities include teaching Fundamentals of Algorithmic Programming and AI-Driven Business Operations , emphasizing data-driven methods for manufacturing processes.

Frits C.R. SpieksmaView profile

Professor

• Combinatorial Optimization
• Operations Research
• Scheduling
• +7 more

Prof. Frits C.R. Spieksma is a full professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e) , where he leads research within the Combinatorial Optimization Group. He has held academic positions at Maastricht University, KU Leuven, and the University of British Columbia, and has been at TU/e since 2018. Education: M.Sc. in Econometrics, University of Groningen (1987) Ph.D. in Operations Research, Maastricht University (1992) Research Focus: His work lies at the intersection of combinatorial optimization and real-world applications . Key themes include: Scheduling and clustering problems, especially in sports tournaments Organ allocation optimization for Eurotransplant Assignment and transportation problems Approximation algorithms and graph-theoretic optimization Scientific Service & Leadership: Founder and ex-Chair, EURO Working Group OR in Sports Member, Steering Committees of MAPSP and MathSports International President, EURO (Association of European Operational Research Societies) Former Vice-President, IFORS Former President, Belgian Society of Operations Research (ORBEL) Editorial Boards: Associate Editor, 4OR (2015–present) Associate Editor, Journal of Quantitative Analysis in Sports (2014–present) Associate Editor, Operations Research Letters (2008–2024) Former Associate Editor, INFORMS Transactions on Education , OMEGA , Computers & Operations Research , IIE Transactions , Naval Research Logistics PhD Supervision & Mentoring: He has supervised more than 25 PhD theses at KU Leuven and TU/e, many of whom now hold academic positions worldwide. Conference & Workshop Organisation: Recent leadership roles include General Chair of IPCO 2022 (Eindhoven), organiser of Benders Day 2024 , and co-organiser of the Dagstuhl Seminar on Fairness in Scheduling and Resource Allocation (March 2025).

Aleida BraaksmaView profile

Lecturer

• Artificial Intelligence
• Health and Well-being
• Reinforcement Learning
• +7 more

Aleida Braaksma is a Lecturer at the University of Twente, affiliated with the TechMed Centre and Mathematics of Operations Research department. Her work bridges Artificial Intelligence with Health and Well-being , focusing on optimizing healthcare systems through Operations Research methodologies. Key Affiliations: Digital Society Institute, TechMed Centre, Mathematics of Operations Research department Research Themes: Reinforcement Learning, Data Mining, Process Mining, and Queueing Theory applications in healthcare logistics Her recent publications highlight advancements in medical diagnostic scheduling , bed allocation , and adaptive clinical trial designs . She has pioneered dynamic robust optimization frameworks for time-sensitive pharmaceutical workflows and developed sampling-based methods for Gittins index approximation in stochastic environments. Scientific contributions include: Optimization of rheumatology outpatient clinics via patient classification algorithms Response-adaptive procedures in clinical trials using constrained Markov decision processes Real-time forecasting systems for pandemic-related hospital capacity planning Computerized decision support for nurse-to-patient assignment

Fabian AkkermanView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Autonomous Agent Systems
• +7 more

Fabian Akkerman is a researcher at the Digital Society Institute within the Industrial Engineering & Business Information Systems department at the University of Twente . His work bridges theoretical advancements in machine learning with practical applications in logistics, energy sustainability, and transportation systems. Primary Affiliation : University of Twente, Industrial Engineering & Business Information Systems Research Focus : Artificial Intelligence, Reinforcement Learning, Autonomous Systems, and Sustainable Logistics Fabian's research specializes in sequential decision-making problems, particularly in dynamic and stochastic environments. A key area of his contributions lies in applying reinforcement learning and optimization techniques to address challenges in: Vehicle routing with uncertain demand Inventory management and warehouse operations Time slot pricing for delivery services Smart transportation and freight logistics Stochastic modeling for supply chain resilience Industry 4.0 adoption in production systems His work demonstrates a strong emphasis on developing algorithmic frameworks (e.g., DynaPlex) that combine statistical modeling with real-world implementation. Recent publications highlight applications in autonomous vehicles, intelligent transport systems, and circular economy strategies. Scientific Awards : 2024 Transportation Science Meritorious Service Award 2025 2nd Place in ISIR Research Challenge for Production-Inventory Planning at ASML

• Computer Science
• Design
• Energy Efficiency
• +8 more

Sabih H. Gerez is an Assistant Professor specializing in Computer Architecture Design and Test for Embedded Systems , affiliated with the Digital Society Institute . With an ORCID ID of 0000-0003-4576-121X, his work focuses on low-power hardware design and energy-efficient computing. Research Interests : Energy efficiency, near-threshold voltage (Near-Vth) computing, flip-flop design, and optimization of digital circuits for embedded systems. Key Contributions : Development of ultra-low-voltage flip-flops, glitch-optimized Booth multipliers, and standard-cell libraries for Near-Vth systems. His work addresses power dissipation, noise margins, and performance metrics. Collaborations : Collaborated with researchers like Anuradha Chathuranga Ranasinghe and Muhammad Adeel Shafique on topics such as approximate MAC accelerators and power-efficient circuit design.

Thomas RothvossView profile

Professor

• Theoretical Computer Science
• Discrete Mathematics
• Approximation Algorithms
• +3 more

Thomas Rothvoss is a Professor at the University of Washington with a joint appointment in the Mathematics and Computer Science departments, conducting pioneering research at the intersection of theoretical computer science and discrete mathematics. His work has fundamentally advanced multiple subfields through breakthrough algorithmic developments. Rothvoss is renowned for solving longstanding problems including exponential lower bounds for extension complexity of combinatorial polytopes, optimal approximation algorithms for Steiner tree and bin packing, and efficient discrepancy minimization techniques. His recent focus on integer programming culminated in the first major running-time improvement in over 30 years, resolving a conjecture by Kannan and Lovász that had stood since 1988. His publication trajectory demonstrates consistent innovation in leveraging geometric and number-theoretic methods to solve NP-hard optimization problems, with recent work bridging lattice theory and algorithmic design to achieve breakthrough complexity results in integer programming. Professor Rothvoss has received top honors in theoretical computer science: Delbert Ray Fulkerson Prize (2018) Gödel Prize (2023) Best Paper Award at FOCS 2023 While specific advising relationships aren't detailed in available sources, his sabbatical collaborations—like the May-June 2025 engagement with CWI's Networks & Optimization group—reflect active mentorship within international research communities. His work continues to attract significant funding, evidenced by support for extended research visits from institutions like CWI. Rothvoss maintains strong ties with European research groups, particularly through his ongoing collaboration with CWI's Networks & Optimization team during sabbatical periods, where he develops lattice-based approaches to combinatorial optimization problems.