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Christopher HojnyView profile

Assistant Professor

• Mixed-Integer Programming
• Symmetry Handling in Optimization
• Combinatorial Optimization
• +3 more

Christopher Hojny is an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology , specializing in combinatorial optimization. He contributes to the EAISI Foundational group and co-develops the academic solver SCIP . His research focuses on symmetry handling in mixed-integer programming , theoretical properties of integer programs, and algorithm development for combinatorial optimization. Recent work explores applications in graph neural network verification , clustering problems, and network coding through mixed-integer programming frameworks. Key publication trends show expertise in Symmetry detection and mitigation techniques Relaxation complexity theory Applications to machine learning robustness Decision diagram-based scheduling Scientific contributions include Proof systems for symmetry certification Topological bounds tightening in GNNs Stable set problem symmetry handling SCIP solver extensions He supervises PhD students Cédric Roy (NWO project on Local Symmetries) and Sten Wessel (co-supervised with Frits Spieksma), while Jasper van Doornmalen (2019-2023) investigated symmetry propagation algorithms.

• Extreme Value Theory
• Graphical Models
• Multivariate Statistics
• +3 more

Frank Röttger is an Assistant Professor at Eindhoven University of Technology, specializing in Mathematical Statistics. His primary research focuses on graphical models, multivariate extremes, and statistical inference in high-dimensional settings. Research Interests : Extreme value theory, probabilistic graphical models, causal inference in extremes, and data-driven risk modeling. Awards : NWO Prize (Scientific) - 2024 Organized Activities : Eurandom Workshop on Graph Laplacians, Multivariate Extremes, and Algebraic Statistics (2024) Causality in Extremes Workshop (2024) Courses Taught : Dependence Modeling Foundations of Statistics Mathematical Statistics Contact : Email: f.rottger@tue.nl

Herke van HoofView profile

Associate Professor

• Reinforcement Learning
• Modular Reinforcement Learning
• Hierarchical Structures
• +5 more

Herke van Hoof serves as Associate Professor at the University of Amsterdam within the Informatics Institute, where he leads research in the AMLab at Science Park (Lab 42, L4.05). His academic journey spans from AI degrees at Groningen University through doctoral studies at TU Darmstadt under Professor Jan Peters to postdoctoral research at McGill University with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. PhD: TU Darmstadt (2016), supervised by Prof. Jan Peters Postdoc: McGill University, Montreal AI Bachelor & Master: University of Groningen Van Hoof's research focuses on overcoming data inefficiency in reinforcement learning through modular and hierarchical approaches. His work explores how structured representations can enable knowledge transfer between tasks, improve learning efficiency, and facilitate applications in domains with complex state and action spaces. The lab investigates symmetry exploitation in multi-agent systems, gradient estimation techniques for discrete variables, and applications to combinatorial problem solving. His publication record shows consistent contributions to top AI venues including ICML, ICLR, NeurIPS, and JMLR over the past seven years. The research trajectory demonstrates evolution from foundational policy search methods toward increasingly sophisticated modular architectures, with recent emphasis on knowledge-assisted AI for critical infrastructure applications through the AI4REALNET project. Third place in ARC prize (Paper award) Van Hoof actively supervises PhD candidates and postdoctoral researchers, with current projects including human-robot collaboration using brain-computer interfaces (with Maryam Alimardani at VU) and interactive robot learning with flexible human input (through The Hybrid Intelligence Centre). His research receives support from multiple funding initiatives including the AI4REALNET project which focuses on applying AI to critical infrastructure systems. As co-organizer of the BeNeRL 2024 workshop and participant in the AI4REALNET consortium, van Hoof maintains strong connections with the broader European AI research community while directing the AMLab's research on modular reinforcement learning approaches.

• Computational Musicology
• Computational Humanities
• Music Information Retrieval
• +5 more

Peter van Kranenburg is an Assistant Professor in Music Information Computing at Utrecht University (The Netherlands) and a guest researcher at the KNAW Meertens Institute in Amsterdam. His work bridges computer science and musicology with a focus on computational approaches to music analysis. His research spans computational musicology, computational humanities, and music information retrieval. Van Kranenburg specializes in developing computational models for analyzing musical structures, particularly melodic similarity measures, folk song transmission patterns, and large-scale analysis of song traditions. His work often involves interdisciplinary collaboration between computer scientists and musicologists. His recent publications demonstrate consistent research in computational approaches to music analysis, with a focus on melodic similarity, folk song transmission, and computational modeling of musical traditions. His work spans both technical computer science aspects of music information retrieval and substantive musicological applications. Van Kranenburg has been involved in several significant research projects including the H2020 Polifonia-project (2021-2024), which focused on large scale analysis of European song traditions and curation of historic data on musical instruments, particularly pipe organs. His educational background combines technical and musicological expertise, having earned master's degrees in both Electrical Engineering (Delft University of Technology, 2003) and Musicology (Utrecht University, 2004), followed by a PhD from Utrecht University. His doctoral research developed melodic similarity measures and software tools for analysis of audio recordings of religious chant.

• Database Systems
• Big Data
• Graph Databases
• +3 more

Prof. Peter Boncz is a Visiting Professor at the Faculty of Science, Department of Computer Systems at Vrije Universiteit Amsterdam. He also holds visiting fellowships at the Network Institute and High Performance Distributed Computing group. His primary affiliation is with CWI (Centrum Wiskunde & Informatica) as a staff member since 2001. He serves in leadership roles at multiple organizations, including Director of LDBC (London), Advisor to Data.world (Austin), and Board Member of DuckDB Foundation (Amsterdam). Education: Not explicitly stated in provided texts, but his roles indicate advanced academic qualifications in computer science. Research focuses on graph databases, benchmarking, query optimization, and big data management. He co-founded the Linked Data Benchmark Council (LDBC) and contributed to projects like DuckDB. His work addresses challenges in scalable data systems, cloud computing, and knowledge extraction from structured data. Publications emphasize advancing graph data management standards and evaluating performance in distributed environments. He has co-authored influential papers on topics like TAKCO for table-based knowledge extraction and cloud-era benchmarking. Awards: No specific prizes listed, but his leadership roles and contributions to standards reflect industry recognition. Advising and grants: No formal advisees listed, but he contributes datasets (e.g., Public BI benchmarks on Zenodo) and collaborates globally. Active in editorial roles, such as the IEEE Data Engineering Bulletin. Labs/Teams: Core member of CWI's database group, involved in LDBC initiatives, and DuckDB Foundation development.

Huub M.M. van de WeteringView profile

Assistant Professor

• Interactive Visualization
• Visual Analytics
• Data Visualization
• +4 more

Huub M.M. van de Wetering is an Assistant Professor at the Mathematics and Computer Science school of Eindhoven University of Technology (TU/e) , with affiliations to the Visualization and Visual Analytics departments, and the EAISI Health research institute. His research focuses on Interactive Visualization , Visual Analytics , and Medical Imaging applications, particularly in Dynamic Graphs and Neuroimaging . Recent work includes Pangenomic Variant Analysis and Streamline Transparency Techniques for brain structure visualization. Dr. van de Wetering has received Scientific Awards including: Best Demo Award (2022) Best Poster Award (2022) He teaches courses in Computer Graphics , Visualization Seminars , and Visual Computing Projects .

Alberto RavagnaniView profile

Associate Professor

• Coding Theory
• Network Communication
• Combinatorics
• +4 more

Alberto Ravagnani is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), the Netherlands. He specializes in the mathematical foundations of communication theory, with a focus on coding theory, network communication, and combinatorics. His research addresses challenges in error-correcting codes, network coding, quantum error correction, and applications in data storage and cryptography. He holds a PhD from the University of Neuchâtel, Switzerland, and has held academic positions at institutions including University College Dublin, Ireland, and the University of Zurich. His work has been recognized with awards such as the KNAW Early Career Award (2021), Ambizione Research Grant (2019), and the Nexans Prize (2017). Ravagnani’s research spans theoretical and applied domains, including rank-metric codes, network decoding, and combinatorial structures. His recent publications explore eigenvalue bounds for codes, densities in metric spaces, and evasive subspaces. He is actively involved in editorial roles for journals like IEEE Transactions on Information Theory and Designs, Codes and Cryptography. His teaching includes courses on coding theory and combinatorics, and he has mentored multiple PhD students in areas such as DNA storage and quantum error correction. Key grants include a NWO Gravitation Grant (EUR 10M) and an EuroTech PhD Program Grant (EUR 235K).

• human-centered ai
• psychology of technology
• machine learning
• +5 more

Chao Zhang is an Assistant Professor in Human-Centered AI at the Human-Technology Interaction group, Eindhoven University of Technology. His research bridges psychology and technology through data-driven methods and psychological theories, focusing on behavior change, AI-human dynamics, and trustworthy systems. MSc in Human-Technology Interaction (TU/e) PhD in Intelligent Systems (TU/e, 2019) Postdoc at Utrecht University Research interests span Human-Centered AI , Explainable AI , and Behavioral Data Science . Key contributions include: Integrating psychological principles with machine learning Modeling longitudinal behavioral data Developing interpretable AI systems for lifestyle interventions Investigating social characteristics in AI-driven environments Current work includes the ENFIELD project on Trustworthy Green AI. Scientific contributions appear in CHI Conference , arXiv , and International Journal of Social Robotics . Recognized with a TU/e PhD Thesis Award nomination in 2021.

• Systems Biology
• Synthetic Biology
• Genomics
• +4 more

Jasper Koehorst is an Assistant Professor in Systems and Synthetic Biology at Wageningen University & Research. His research focuses on integrating computational and experimental approaches to study microbial genomics, metabolic engineering, and systems biology. He leads projects exploring genome-scale models for industrial biotechnology applications, such as syngas fermentation optimization and machine learning-based process control. His work also addresses fundamental questions in plant-microbe interactions, symbiotic relationships, and evolutionary genomics. Koehorst has contributed to initiatives like FAIR data standards for genomics and digital twin frameworks in life sciences. Education: He holds a PhD in Molecular Biology from Wageningen University (2019), with a thesis on necrotizing fasciitis pathogenesis. His postdoctoral research expanded into systems biology and synthetic microbial consortia. Research Interests: His group investigates microbial metabolic flexibility, computational modeling of biological systems, and applications in bioenergy and bioprocess engineering. Key areas include genome annotation pipelines, microbial community dynamics, and the development of open science resources like Wikidata integration for biological data. Publications: His recent work spans high-impact journals like Cell and Computational and Structural Biotechnology Journal , with a focus on interdisciplinary topics such as MLOps in fermentation, auxin signaling mechanisms, and genomic analyses of symbiotic microbes. Over 50 peer-reviewed articles highlight his contributions to microbial genomics and systems approaches. Grants & Awards: While no specific awards are listed, his projects have received institutional support through Wageningen University collaborations and EU-funded initiatives. He currently co-supervises three active PhD projects on microbiome dynamics, computational modeling, and bioindustry 4.0 applications. Labs/Teams: Koehorst is affiliated with the Systems Biology group and collaborates with the Bioinformatics & Systems Biology department at Wageningen. His team works closely with industry partners on bioprocess optimization and digital twin technologies.

Xiaodong ChengView profile

Assistant Professor

• Control Systems
• Optimization
• Machine Learning
• +5 more

Xiaodong Cheng is an Assistant Professor at the Mathematical and Statistical Methods (Biometris) group in the Department of Plant Science at Wageningen University & Research. His research focuses on control systems, optimization, and machine learning, with applications in agricultural and energy systems. He holds a Ph.D. (cum laude) from the University of Groningen, under Prof. Jacquelien Scherpen, and prior roles include Research Associate at the University of Cambridge and Postdoctoral Researcher at Eindhoven University of Technology. Education: B.S. and M.E. from Northwestern Polytechnical University, China (2011, 2014) Ph.D. (cum laude) in Engineering from the University of Groningen, Netherlands (2018) Research Interests: Data-driven modeling, dimensionality reduction, learning-based control, system identification, and applications in agriculture and energy. He emphasizes practical implementations through tools like SYSDYNET and Bayesian neural ODEs for greenhouse systems. Key Contributions: His work spans model reduction for network systems, fault-tolerant control, and stochastic MPC for greenhouse production. Recent trends in his publications highlight advancements in resilient microgrid control, precision agriculture via drone-based sensing, and Bayesian methods for dynamic systems. Awards: Automatica Paper Prize Award (2017–2019) IEEE Transactions on Control Systems Technology Outstanding Paper Award (2020) Labs/Teams: Leads the Biometris group's efforts in integrating control theory and machine learning for sustainable systems. His work often collaborates with agricultural and energy sector stakeholders for real-world impact.

Senja BarthelView profile

Assistant Professor

• Geometric topology
• Mathematical chemistry
• Spatial graphs
• +5 more

Dr. Senja Barthel is an Assistant Professor at the Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam. Her research bridges geometric topology and mathematical chemistry, focusing on spatial graphs and their applications in materials science. She is affiliated with the Center for Topology and Applications Amsterdam, Amsterdam Sustainability Institute, and NWO DIAMANT cluster. Current projects include Topology of molecular braids (NWO M1 grant), geometrical analysis for mass transport, and contributions to the NWA Emergence Consortium (WP4.3 and WP4.4). Research interests: entanglements in spatial graphs, embeddings in surfaces, pore geometry in materials, energy landscapes, crystal net topologies, and topological data analysis for molecular modeling. Teaching includes Linear Algebra, Differential Topology, and Calculus at BSc levels, with supervision of BSc and PhD projects. Her recent publications analyze ion diffusion in solids, thermal transport in ZIFs, and topological descriptors for crystal networks, reflecting interdisciplinary work combining mathematics, chemistry, and computational methods. Collaborators include Magnus Botnan (Dutch Applied Topology days) and Amber Mace (Uppsala Universitet).

• Computational Geometry
• Geometric Algorithmics
• GIScience
• +5 more

Prof. Marc van Kreveld is a Professor and Head of the Department of Computer Science at Utrecht University since February 2023. His research focuses on computational geometry, geometric algorithmics, and their applications in GIScience, cartography, and puzzle design. He leads the Geometric Computing group and has made significant contributions to algorithms for trajectory data, geometric similarity, and spatial data analysis. Education: PhD in Computer Science from Utrecht University (1992). Research Interests: Computational geometry, GIScience, graph drawing, LIDAR point cloud reconstruction, and geometric puzzle analysis. His work bridges theoretical foundations and real-world applications, such as route data analysis and environmental modeling. Publications: Over 290 peer-reviewed papers, including foundational work on Fréchet distance algorithms, geometric spanners, and trajectory grouping. Recent contributions include advancements in collision detection for modular robots and mobility data science. Awards & Editorial Roles: Editor-in-Chief of Computing in Geometry and Topology , and editorial board member of Journal of Computational Geometry . Active in program committees for conferences like GIScience and SoCG. Teaching: Specializes in computational geometry and its applications, with a focus on algorithm design and geometric computing. Labs & Teams: Leads the Geometric Computing group at Utrecht University, collaborating on projects like braided river network modeling and automated puzzle generation.

• Computer Vision
• Image Processing
• Machine Learning
• +12 more

T. Huysmans is a researcher in the Faculty of Industrial Design Engineering at TU Delft, specializing in Human Factors. Their work integrates computer science and engineering to advance 3D anthropometry, ergonomics, and human-centered design. Research interests focus on image processing, machine learning, and 3D/4D scanning for applications in safety equipment, product design, and human physiology. Key areas include facial and foot anthropometry, pressure discomfort mapping, and aerodynamic modeling. The recent research articles demonstrate a strong trend in applied computer vision and data-driven ergonomics , particularly in cross-national comparisons of facial dimensions for PPE design and dynamic foot shape modeling during walking. These works combine advanced deep learning with practical industrial applications. No scientific awards were mentioned in the provided text. Huysmans has supervised at least one research project, as indicated by the 'Supervised Work' section. While specific grants are not listed, the scale of data collection (e.g., scanning Chilean workers and Dutch children) suggests involvement in funded research initiatives. The research is supported by datasets such as the 'Dense 3D pressure discomfort threshold map' and the 'Generic Cyclist Model', indicating active lab-based work involving 3D scanning, data modeling, and simulation.

Tiedo TingaView profile

Professor

• Predictive Maintenance
• Bayesian Filtering
• Fault Diagnostics
• +5 more

Tiedo Tinga is a Full Professor at the Netherlands Defence Academy and a leading expert in Dynamics-Based Maintenance . With over 175 research outputs and an h-index of 30, his work focuses on predictive maintenance, fault diagnostics, and prognostics in mechanical systems. Research Highlights : Bayesian filtering, sensor performance evaluation, composite material diagnostics, and smart maintenance systems. Scientific Recognition : Recipient of the 2015 Maintenance Awareness Award. Recent Contributions : Advanced methods for impact identification in composites, integration of FMEA/FTA in fault diagnosis, and applications of transfer learning for marine propulsion systems. His research combines physics-based models with data-driven approaches to solve practical maintenance challenges in defense and industrial applications. Recent Publications (2025-2023): Application of transfer learning for shaft power predictions, Bayesian filtering frameworks, bond graph diagnostics, and sensor performance comparisons in composite structures. Scientific Awards Maintenance Awareness Award (2015) Professional Activities Invited talks at conferences (2023-2025) on predictive maintenance, smart systems, and data-driven maintenance challenges. Collaborations on motor vibration monitoring datasets and defense technology projects.

J.C. ScholtesView profile

Professor

• Natural Language Processing
• Information Retrieval
• Text Mining
• +5 more

J.C. Scholtes is an Extra-ordinary Professor of Text Mining at the Department of Knowledge Engineering, Faculty of Science and Engineering, University of Maastricht. He is also a Senior Research Fellow at the Dutch School for Information and Knowledge Systems (SIKS), a Board Member at IPRally, and a Venture Partner at ENDEIT Capital. M.Sc. in Computer Science from Delft University of Technology Ph.D. in Computational Linguistics from University of Amsterdam His research expertise spans Natural Language Processing, Machine Learning, and Artificial Intelligence, with applications in legal, medical, business, and regulatory domains. He focuses on text mining, machine translation, question-answering systems, and information extraction from unstructured data. Recent publications (2024–2025) emphasize context-aware machine translation, misinformation detection in recommendation systems, healthcare data analysis, and food science applications. Key themes include integrating deep learning architectures (e.g., Transformers), optimizing search and translation efficiency, and leveraging hybrid human-machine approaches. He has collaborated widely in industry and academia, notably deploying e-discovery software for institutions like the UN War Crimes Tribunals and FBI-ENRON. His career history includes leadership roles at ZyLAB (1987–2021) and prior service in the Royal Dutch Navy.