Find Academic Supervisors

Carel F.W. PeetersView profile

Associate Professor

• Statistics
• Bayesian theory
• Biostatistics
• +6 more

Carel F.W. Peeters is an Associate Professor of Statistics at the Department of Mathematical & Statistical Methods (Biometris) , Wageningen University & Research . He previously held academic positions at Amsterdam University Medical Centers (AUMC) as Assistant Professor in Biostatistics , and during his PhD studies, he was a research fellow at VUA and research scientist at CITO . Education: M.Sc. in Statistics (with great distinction) from Katholieke Universiteit Leuven (KUL) Ph.D. in Bayesian Statistics from Utrecht University Research Focus: His work operates at the intersection of high-dimensional multivariate statistics , statistical machine learning , and complex data analysis . He develops computational methods for small-sample massive-features data to automate discovery of data-generating mechanisms, with applications in genomics , medical imaging , and social network analysis . Recent Trends: His publications demonstrate expertise in genomic prediction models , graphical modeling , high-dimensional regularization , and cross-domain data integration . Methodological innovations include rags2ridges package development and targeted penalization techniques. Technical Expertise: Bayesian theory Multivariate analysis Machine learning High-dimensional data Network analysis Statistical software development (R packages) Data science Graphical models Current Position: He serves as contact person for Biometris subdivision at Wageningen University, with physical location at Droevendaalsesteeg 1, 6708PB Wageningen, Netherlands. His work continues to bridge theoretical and applied statistics through practical implementation of advanced methodologies.

Jim W. PortegiesView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Applied Mathematics
• +5 more

Jim W. Portegies is an Assistant Professor in the Applied Analysis group at the Eindhoven University of Technology (TU/e). He holds additional roles as EAISI Foundational Assistant Professor and EAISI Mobility Assistant Professor within the TU/e's interdisciplinary initiatives. His research focuses on applying mathematical techniques from analysis, geometry, and measure theory to problems in machine learning and artificial intelligence, with a particular interest in designing algorithms that emulate human learning mechanisms. He obtained his MSc in Industrial and Applied Mathematics and Applied Physics from TU/e before completing his PhD in Mathematics at the Courant Institute of Mathematical Sciences, New York. After postdoctoral work at the Max Planck Institute in Leipzig, he returned to TU/e in 2016. Education: MSc in Industrial and Applied Mathematics & Applied Physics (TU/e, 2009) PhD in Mathematics (Courant Institute, NYU, 2014) Research Interests: Jim's work bridges pure mathematics and applied AI. Key areas include: Geometric and spectral methods for machine learning Universal learning algorithms inspired by human cognition Analysis of dimensionality reduction techniques Stochastic processes and control systems His recent work explores neural stochastic differential equations and interpretable AI mechanisms. Awards: Member of TU/e Young Academy of Engineering (2016–present) Collaborations & Grants: Jim collaborates extensively in interdisciplinary projects, including EU-funded initiatives on AI mobility systems. His research has been supported by grants from NWO (Netherlands Organization for Scientific Research) and industry partnerships with semiconductor and control systems companies. Labs & Teams: He contributes to the TU/e's Center for Analysis, Scientific Computing and Applications (CASA) and leads the Applied Analysis & AI research group, focusing on theoretical foundations of AI and their practical implementations.

Mircea LazarView profile

Associate Professor

• Constrained Control
• Model Predictive Control
• Hybrid Systems
• +4 more

Mircea Lazar is an Associate Professor in the Control Systems group at Eindhoven University of Technology. His research spans constrained control, model predictive control (MPC), and stability analysis of hybrid systems, with applications in power systems, smart grids, and precision mechatronics. Research highlights include: Development of data-driven predictive control methods Stability guarantees for nonlinear and hybrid systems Applications in water networks, power converters, and mechatronics He has received the EECI PhD Award and NWO VENI grant. Recent publications focus on real-time MPC implementations, physics-guided neural networks, and optimization for large-scale systems. Collaborations include ASML, DAF, Philips, and Ford. He chairs the IEEE CSS Technical Committee on Hybrid Systems and has supervised 9 PhD students.

Willem M. SonkeView profile

Researcher

• Applied Geometric Algorithms
• Computational Geometry
• Topological Data Analysis
• +3 more

Willem M. Sonke is a researcher at the Technische Universiteit Eindhoven (TU/e) department of Mathematics and Computer Science, affiliated with the ALGA (Algorithms: Geometry and Applications) cluster and EdIn. His work intersects computational geometry, topological data analysis, and environmental applications. Research Focus: Applied Geometric Algorithms River Network Analysis Topological Persistence Models UN Sustainable Development Goals (SDGs) Publications Trends: His articles show expertise in algorithm optimization for geometric problems with environmental applications, including river network analysis, kinetic terrain modeling, and strip packing efficiency. Collaborations: Active in international academic networks with contributions to Scopus-indexed conferences and journals. Key collaborations include institutions in the Netherlands and Japan.

Mark T. de BergView profile

Professor

• Algorithms
• Computational Geometry
• FPT Algorithms
• +6 more

Mark T. de Berg is a Full Professor at Eindhoven University of Technology (TU/e), leading the TU/e Algorithms Group. He holds positions in the Department of Mathematics and Computer Science and the EAISI Foundational initiative. His research focuses on algorithms for spatial data, geometric networks, and efficient solutions for NP-hard problems. He has published over 400 works and authored influential textbooks in computational geometry. Education: MSc (Computer Science, Utrecht University, 1988), PhD (Utrecht University, 1992). Awards include the NWO Gravitation Grant (2014) and a VICI grant from NWO. He serves on editorial boards of three journals and the Computational-Geometry Steering Committee. Research Interests: Design of efficient algorithms for spatial datasets Geometric networks and FPT algorithms Applications in geographic information science and manufacturing Recent Work Trends: Emphasis on approximation algorithms, geometric data structures, and algorithmic challenges in spatial computing. Over 15 recent publications address topics like dominating sets, geometric separators, and efficient query processing in polygons. Advising & Grants: Supervised 66 students. PI in the Networks Gravitation Program. Active in international conferences and editorial roles. Labs/Teams: Core member of TU/e Algorithms Group and EAISI Foundational, fostering interdisciplinary research in algorithmic foundations.

Yulong PeiView profile

Assistant Professor

• Anomaly Detection
• Adversarial Machine Learning
• Graph Neural Networks
• +7 more

Yulong Pei is an Assistant Professor at the Department of Mathematics and Computer Science, Eindhoven University of Technology. His research focuses on anomaly detection, adversarial machine learning, graph neural networks, and fairness in AI. He has contributed to the development of robust algorithms for network analysis, active learning frameworks, and applications in code quality assessment and financial data analysis. Pei's work addresses challenges such as structural sparsity optimization, bias mitigation in language models, and efficient training techniques for deep neural networks. His research has been recognized with awards like the Best Paper Award of LoG 2022. He has advised 27 students and collaborated on projects involving financial transaction networks, social media data analysis, and healthcare informatics. Key contributions include FAL-CUR (fair active learning), Hop-count based anomaly detection, and frameworks for adversarial example generation. His publications span conferences like ECML PKDD and journals like Expert Systems with Applications, demonstrating interdisciplinary impact across computer science, finance, and healthcare.

Michael KonigView profile

Associate Professor

• Economics of Innovation
• Technical Change
• Social Networks
• +5 more

Michael Konig is an Associate Professor in the Department of Spatial Economics at Vrije Universiteit Amsterdam. He also serves as a research fellow at the Tinbergen Institute, CEPR, and the Swiss Economic Institute (KOF). Previously, he held roles as Senior Research Associate at the University of Zurich and Visiting Scholar at Bocconi University and Stanford University. His research focuses on the economics of innovation, technical change, and networked relationships between economic actors, combining theoretical and empirical methods to evaluate policy instruments. Research interests include analyzing how innovation and technical advancements interact with networks spanning individuals, firms, sectors, and countries. His work bridges economic theory with real-world applications, particularly in policy evaluation. Recent contributions address network rewiring for disease mitigation, real-time economic shock monitoring via company websites, and endogenous technology cycles in R&D networks. Affiliated with leading institutions, Konig has co-authored over 35 research outputs. He teaches Microeconomics for Spatial Economics and organizes workshops like the TI Network Economics Day. His collaborative networks span global institutions, reflecting his interdisciplinary approach to economic challenges.

Leen StougieView profile

Professor

• Operations Research
• Combinatorial Optimization
• Scheduling
• +3 more

Leen Stougie is a part-time Full Professor of Operations Research at Vrije Universiteit Amsterdam and holds positions as Senior Researcher and Head of the Life Sciences and Health group at CWI (Centrum Wiskunde & Informatica). He has been affiliated with TU Eindhoven and Tinbergen Institute. His research focuses on Operations Research, Combinatorial Optimization, Scheduling, Approximation Algorithms, and Computational Biology. Education: PhD in Operations Research from Erasmus Universiteit Rotterdam (1985). Teaching includes the Advanced Linear Programming course via LNMB/Mastermath/DIAMANT. His work contributes to UN Sustainable Development Goals through algorithmic solutions in biology and optimization. Research highlights include phylogenetic network construction, string matching algorithms, and scenario-based scheduling. He has secured grants like the Marie Skłodowska-Curie Actions (2020) and NWO-GROOT (2001). Advised six PhD theses but no student names listed. Active in organizing conferences and reviewing activities.

• Artificial Intelligence
• Robotics
• Knowledge Graphs
• +4 more

Ilaria Tiddi is an Assistant Professor at the Faculty of Science, Department of Computer Science at Vrije Universiteit Amsterdam. She is also affiliated with the Network Institute and the Artificial Intelligence research group. Her work focuses on hybrid intelligence, knowledge graphs, robotic perception, and multi-agent systems. She contributes to the UN Sustainable Development Goals through her research in artificial intelligence and robotics. Her research interests include explainable AI, knowledge representation, robotic knowledge acquisition, and the integration of semantic technologies into robotic systems. She explores how knowledge graphs can enhance robotic perception and decision-making, particularly in dynamic environments. Recent work emphasizes shared understanding in open multi-agent systems and actionable knowledge extraction for robotic tasks. Dr. Tiddi has published extensively on topics like ontology design for robotics, data augmentation in reinforcement learning, and narrative-based societal understanding. Her articles highlight interdisciplinary approaches combining AI with fields like bioinformatics and urban studies. She teaches courses on artificial intelligence and semantic technologies, reflecting her commitment to advancing computational methods for complex problem-solving. Her datasets and software, such as the MUHAI Benchmark and story-generation frameworks, demonstrate her focus on practical applications of AI. Collaborations span international institutions, addressing challenges in robotic perception, ethical AI, and hybrid intelligence systems.

Ali MehrabiView profile

Assistant Professor

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

Ali Mehrabi is an Assistant Professor at the Faculty of Science and the Network Institute of Vrije Universiteit Amsterdam. His primary affiliation is with the Theoretical Computer Science department. He teaches courses including Data Structures and Algorithms for CS, Geometric Algorithms, and Networks and Graphs. His research interests are centered around theoretical computer science, algorithms design, geometric algorithms, and network theory. Education details are not explicitly provided in the available text. His academic roles indicate a strong focus on teaching and research in computational theory and network analysis. No scientific awards, grants, or lab affiliations are mentioned in the provided information. He currently has no listed advisees or students.

• Cybersecurity
• Software Security
• Memory Management
• +3 more

Asia Slowinska is an Assistant Professor at the Faculty of Science, Department of Computer Systems, and affiliated with the Network Institute at Vrije Universiteit Amsterdam. Her research focuses on cybersecurity, software security, and binary analysis. She specializes in areas such as memory management vulnerabilities, data structure detection in binaries, and exploit mitigation techniques. Her work addresses critical issues like buffer overflow detection, compiler-agnostic function identification, and hardware-software co-debugging. Recent contributions include frameworks for analyzing full-scale x86/x64 binaries and practical tools for detecting data-only attacks. Collaborations span international security symposiums, with notable outputs in USENIX Security, IEEE S&P, and ACM conferences. She teaches Binary and Malware Analysis (2024-2025), reflecting her expertise in reverse engineering and exploit analysis.

• Bioinformatics
• Theoretical Computer Science
• Computational Biology
• +3 more

Atze van der Ploeg is an Assistant Professor at the Vrije Universiteit Amsterdam's Faculty of Science, within the Theoretical Computer Science department. He is also affiliated with the Network Institute. His research focuses on interdisciplinary topics at the intersection of computer science and computational biology, with expertise in algorithm design, bioinformatics tools, and programming languages. He holds an MSc and a PhD, though specific academic details about his degrees are not provided in the text. His teaching responsibilities include courses such as Introduction to Programming (PYTHON), Object-Oriented and Functional Programming, and Systems Programming Project. These courses reflect his commitment to foundational programming skills and advanced software development practices. Key research interests include developing tools for bioinformatics (e.g., the PRALINE alignment toolkit), functional reactive programming, and visualization systems. His work emphasizes practical, principled approaches to solving complex computational problems. Though no specific grants or awards are listed, his publications span topics like type-safe dynamic typing, modular language implementation, and algorithm optimization, showcasing contributions to both theoretical and applied computer science.

Matthias WalterView profile

Assistant Professor

• Operations Research
• Optimization
• Mathematical Programming
• +5 more

Matthias Walter is an Assistant Professor in the Department of Mathematics of Operations Research. His research focuses on optimization, mathematical programming, and algorithm design, with applications in transportation, combinatorial optimization, and logistics. He contributes to the SCIP Optimization Suite and has developed the Combinatorial Matrix Recognition Library. His work spans topics such as linear programming relaxations, pseudo-Boolean solving, and multilinear optimization. Key research interests include operations research, integer programming, and algorithmic solutions for complex optimization problems. He has collaborated on projects like optimizing parcel transportation and analyzing congestion games. His recent publications address challenges in flow shop scheduling, graph realization, and pseudo-Boolean functions. Walter actively participates in academic activities, organizing events like the Mathematical Olympiad in Saxony-Anhalt and presenting invited talks on topics such as McCormick relaxations. His contributions to computational tools and theoretical advancements highlight his role in advancing optimization methodologies.

Ruben Pieter HoeksmaView profile

Assistant Professor

• Transportation
• Agent-Based Modeling
• Traffic Optimization
• +3 more

Ruben Pieter Hoeksma is an Assistant Professor affiliated with the Digital Society Institute and the Mathematics of Operations Research department. His research focuses on algorithmic game theory, transportation optimization, agent-based modeling, and mathematical programming. He actively contributes to advancing methodologies in scheduling, stochastic algorithms, and combinatorial optimization. His work bridges theoretical computer science and practical applications, such as logistics optimization for parcel delivery systems (e.g., PostNL) and robust scheduling under uncertainty. Collaborations span international institutions, reflecting his global academic network. Hoeksma has published extensively in top journals and conferences, with recent contributions addressing congestion games, stochastic minimum spanning trees, and succinct predictions in paging algorithms. His research has been cited 225 times, achieving an h-index of 7.

Piek VossenView profile

Professor

• Computational Linguistics
• WordNets
• Natural Language Processing
• +2 more

Prof. Piek Vossen is Full Professor of Computational Lexicology at Vrije Universiteit Amsterdam, heading the Computational Lexicology & Terminology Lab (CLTL). A Spinoza Laureate (2013) and Knight in the Order of the Dutch Lion (2015), his research integrates computational linguistics, WordNets, and conversational AI. He co-founded the Global WordNet Association and leads projects including 'Make Robots Talk', 'Dutch FrameNet', and the Hybrid Intelligence Centre. Key research areas: Multilingual semantic networks and ontologies Cross-lingual language modeling Conversational agents and social robots Event extraction and perspective analysis Computational analysis of discourse and argumentation His recent work focuses on toxicity analysis in online discourse, medical language models, and hybrid intelligence systems that combine human and machine capabilities.