Willem Jonker is a Full Professor at the Digital Society Institute, specializing in Semantics, Cybersecurity & Services. His research focuses on encryption schemes, access control, and privacy-preserving technologies. He has contributed to over 120 publications, with recent work addressing CVE-to-CWE mapping, anomaly detection in network traffic, and functional encryption systems. His expertise aligns with UN Sustainable Development Goals related to secure digital systems and privacy. Jonker has supervised 10 students and actively participates in academic conferences, presenting on topics like secure data management and cryptographic protocols. Research interests include cryptographic protocols, secure data management, and cybersecurity solutions. Notable projects involve developing methods for detecting covert channels, enhancing data privacy in healthcare, and improving secure search over encrypted data. He has also contributed to standards in digital rights management and forensic image recognition.
Noela Müller is an Assistant Professor in the Mathematics and Computer Science school at Eindhoven University of Technology . Her research focuses on Probability Theory , Random Matrices , and Random Graphs , with significant contributions to understanding the rank of sparse matrices and clique factors in probabilistic settings. Research Outputs : Published 22 works including journal articles and preprints. Collaborations : Active in international networks, particularly in sparse matrix analysis and probabilistic combinatorics. Her recent work explores sparse pooled data algorithms , random 2-SAT models , and sharp thresholds in random graphs , showcasing interdisciplinary applications in computer science, mathematics, and theoretical physics.
Jop Briët is a Researcher at the Department of Algorithms and Complexity at Centrum Wiskunde & Informatica (CWI) in the Netherlands. His work focuses on theoretical computer science, quantum information theory, combinatorics, and tensor analysis. He has held grants including the Veni Innovational Research Grant from NWO and a Rubicon fellowship. He has authored over 50 publications in leading venues, exploring topics such as Grothendieck inequalities, quantum computing, and additive combinatorics. His research interests span the interplay between combinatorics and computational complexity, with particular emphasis on tensor analysis, probabilistic methods, and algorithm design. Recent work includes studies on Szemerédi’s theorem with random differences and the application of quantum query algorithms to entanglement-based problems. Awards: Outstanding paper award TQC (2020), Andreas Bonn medal (2013), Stieltjesprijs (2011). Professional Activities: Editor for ERCIM News, Board Member of Koninklijk Wiskundig Genootschap, and frequent invited speaker at workshops on quantum computing and combinatorics. Grants: Veni Grant (2014), Rubicon Fellowship (2012). Current teaching includes courses on Additive Combinatorics and Quantum Information Processing, reflecting his commitment to bridging foundational theory with advanced applications in computing and mathematics.
Ross J. Kang is a Canadian mathematician currently serving as an Associate Professor at the Korteweg–de Vries Institute for Mathematics within the Faculty of Science at the University of Amsterdam since 2022. He is an active member of the Discrete Mathematics and Quantum Information group and the NETWORKS consortium. Previously, he held positions as Assistant/Associate Professor at Radboud University Nijmegen (2014-2022), Assistant Professor at Utrecht University (2013), and Researcher at Centrum Wiskunde & Informatica (2012-2013). His academic journey includes postdoctoral positions at Durham University (2010-2012) and McGill University (2008-2010), where he was advised by Bruce Reed and Louigi Addario-Berry. DPhil in Mathematics, University of Oxford (2008) - Thesis: 'Improper colourings of graphs', advised by Colin McDiarmid BSc (Hons) in Mathematics and Computer Science, University of Victoria (2003) - Governor General's Silver Academic Medal recipient Ross J. Kang's research focuses on probabilistic and extremal combinatorics, random discrete structures, graph coloring, geometric graphs, and algorithms. His work bridges theoretical mathematics with practical applications, exploring fundamental questions in discrete mathematics. He has made significant contributions to understanding graph coloring problems, particularly in the contexts of list coloring, distance coloring, and strong coloring. His research often employs probabilistic methods to establish bounds and structural properties in graph theory. Kang's work on the hard-core model, local occupancy method, and triangle-free graphs has advanced our understanding of the interplay between local constraints and global structure in discrete systems. Analysis of his recent publications reveals a strong emphasis on graph coloring problems, particularly list coloring variants and their extensions. His work frequently explores the relationship between graph structure (such as degree constraints, girth, or forbidden subgraphs) and coloring properties. A notable trend is his development and application of the local occupancy method to establish improved bounds for chromatic numbers in various graph classes. His research also demonstrates a consistent interest in extremal problems, seeking optimal configurations under specific constraints, particularly in the context of triangle-free graphs and geometric representations. NWO Open Competition M-1 grant entitled 'Asymptotic triangle-free structure (3Free)', 2022-2026 NWO Vidi grant entitled 'On the edge: theory and techniques at the frontiers of edge-colouring', 2017-2023 NWO Veni grant entitled 'Generalised colouring for random graph models', 2012-2015 Van Gogh travel grants (2020-2021 with Marthe Bonamy; 2016-2017 with Louis Esperet) Governor General's Silver Academic Medal (2003) Ross J. Kang has successfully supervised multiple PhD students including Eoin Hurley (defending May 2025), Stijn Cambie (defended April 2022), and François Pirot (winner of 2020 prix Charles Delorme). His research is supported by significant grants from the Netherlands Organisation for Scientific Research (NWO), including the prestigious Open Competition M-1 grant. Kang is actively involved in the academic community through his editorial role at Combinatorial Theory, co-organization of conferences like the Dutch Days of Combinatorics, and leadership in initiatives such as Innovations in Graph Theory, a diamond open access journal he helped launch in August 2023. As a member of the Discrete Mathematics and Quantum Information group at the University of Amsterdam and the NETWORKS consortium, Kang collaborates with researchers across various institutions. He has established strong international connections through his Van Gogh travel grants and participation in collaborative projects like the Sparse (Graphs) Coalition sessions. His research group focuses on theoretical aspects of discrete mathematics with connections to quantum information science, and he maintains active collaborations with researchers across Europe and North America.
Didier Meuwly is a Full Professor of Forensic Biometrics at the University of Twente (since 2013) and Principal Scientist at the Netherlands Forensic Institute (NFI). His work focuses on automating and validating probabilistic evaluation of forensic evidence, particularly biometric traces. He has contributed to international standards via ISO Technical Committee 272 and served as Associate Editor for Forensic Science International . PhD in Forensic Speaker Recognition (University of Lausanne, 2000) Research spans forensic biometrics, likelihood ratios, AI validation, and gait/body analysis from surveillance footage. Recent work addresses ISO standards (21043), forensic AI explainability, and multimodal evidence evaluation. His publications emphasize empirical validation and statistical rigor. Key awards include: ENFSI Distinguished Forensic Scientist Award (2022) University of Lausanne Law Faculty Prize (2002) Active in global forensic networks, he chairs the ENFSI R&D Committee and collaborates across disciplines on digital evidence, biometric security, and forensic methodology.
Aaqib Saeed is an Assistant Professor in the Department of Industrial Design at Eindhoven University of Technology. His research focuses on Human-Centric AI, Federated Learning, Self-Supervised Learning, and Audio Understanding, with applications in Personal Health. He holds a PhD (cum laude) from TU/e and an MSc (cum laude) from the University of Twente. Education: PhD in Computer Science (cum laude), TU/e (2021) MSc in Computer Science (cum laude), University of Twente (2018) Research Interests: Development of robust federated learning frameworks for decentralized data Self-supervised learning for audio and physiological signal analysis AI-driven solutions for healthcare monitoring Key Contributions: DeltaMask: Reducing communication overhead in federated fine-tuning FedNS: Mitigating noisy decentralized data in federated learning Labeling Chaos to Learning Harmony: Handling label noise in FL Professional Experience: Visiting Industrial Fellow, University of Cambridge (2023) Research Scientist, Philips Research (2019–2023) Research Internships: Google Research, TNO/EIT Digital Awards: UT Scholarship (MSc) Cum Laude awards for both PhD and MSc Labs/Teams: EAISI Health, EAISI Foundational, Computational Design Systems.
Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)
Laura Toni is an Associate Professor in the Department of Electronic & Electrical Engineering at University College London (UCL). She serves as Director of the MSc in Telecommunications and Internet Engineering and the MRes in Telecommunications. Additionally, she is a Turing Fellow at the Alan Turing Institute and a member of ELLIS (European Lab for Learning and Intelligent Systems). Her research focuses on coding, streaming technologies, machine learning for immersive communications, decision-making under uncertainty, and large-scale signal processing. She leads the LASP (Learning And Signal Processing) group at UCL. Education: MSc (2005) and PhD (2009) from the University of Bologna, followed by postdoctoral research at UC San Diego and EPFL under Professors L. Milstein, P. Cosman, and P. Frossard. Key roles include Technical Program Chair at ACM MM 2022, Keynote Co-Chair at ACM MMSys 2022, and leadership in organizing workshops on graph-based machine learning and emerging technologies in performing arts. She is a Senior IEEE Member and holds editorial roles in IEEE Multimedia Magazine and EURASIP Journal on Signal Processing. Her work bridges communication systems and machine learning, with contributions to adaptive streaming, network optimization, and graph signal processing. She actively promotes diversity and inclusion in technical conferences, including roles as Diversity Chair at MMSys 2021 and PIMRC 2020.
Bert de Vries is a Professor at the Signal Processing Systems Group at Eindhoven University of Technology (TU/e), where he has been employed since January 2012. He maintains a dual career, also working at GN Hearing in the hearing aids industry since April 1999, where he holds both research and managerial roles. His academic journey began at TU/e, where he earned his MSc in Electrical Engineering in 1986, followed by a PhD from the University of Florida in 1991. Between 1992 and 1999, he worked at Sarnoff Research Center in Princeton, NJ, contributing to diverse signal and image processing projects. Professor de Vries's research centers on Bayesian Machine Learning, with particular focus on the Free Energy Principle and its applications to engineering problems. His work bridges theoretical neuroscience with practical signal processing systems, especially in biomedical applications. He directs the BIASlab research team at TU/e, which develops probabilistic programming tools including RxInfer.jl, ForneyLab.jl, GraphPPL.jl, ReactiveMP.jl, and Rocket.jl. His research spans active inference, variational message passing, probabilistic programming, and Bayesian neural networks, with applications ranging from hearing aids to multi-agent systems. Analysis of his recent publications reveals a strong trend toward practical implementations of Bayesian inference frameworks, particularly through Julia-based probabilistic programming tools. His work shows increasing focus on active inference applications, message passing algorithms, and the intersection of Riemannian geometry with probabilistic modeling. The research demonstrates consistent progression from theoretical foundations toward real-world engineering applications, particularly in biomedical signal processing and autonomous systems. Professor de Vries teaches a graduate-level course on Bayesian Machine Learning at TU/e and actively contributes to open-source software development through his GitHub profile (bertdv), with recent activity as recent as August 2025. His research team has developed several influential probabilistic programming libraries that have gained significant attention in the machine learning community. The BIASlab research group continues to advance the state of the art in Bayesian inference methods with applications in hearing technology, robotics, and signal processing.
Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.
Nikhil Bansal holds the prestigious Patrick C. Fischer Professorship of Theoretical Computer Science in the Department of Computer Science & Engineering at the University of Michigan's College of Engineering. His research program has established him as a leading figure in theoretical computer science, with significant contributions to algorithm design and analysis, particularly in discrete optimization problems. Bansal's research focuses on theoretical computer science with emphasis on design and analysis of algorithms for discrete optimization problems. His work spans multiple areas including discrepancy theory, approximation algorithms, randomized algorithms, combinatorial optimization, complexity theory, machine learning theory, and probability. He has made significant contributions to understanding the limits of approximation algorithms and developing novel techniques for combinatorial optimization problems. Analysis of Bansal's recent publications reveals a strong focus on discrepancy theory, online algorithms, and combinatorial optimization. His work often bridges theoretical computer science with discrete mathematics and probability theory. A recurring theme across his publications is the development of novel algorithmic techniques for solving NP-hard problems with provable guarantees. His research has evolved from foundational work in approximation algorithms to more recent contributions in quantum computing complexity and stochastic optimization. Patrick C. Fischer Professor of Theoretical Computer Science Bansal has advised numerous PhD students including Marek Elias, Shashwat Garg, and Greg Koumoutsous, as well as mentoring several postdoctoral researchers. He has served on editorial boards for top journals including Journal of the ACM, Theory of Computing, and Stochastic Models, and has been active on program committees for major conferences such as STOC, FOCS, SODA, and ICALP, including serving as chair for ICALP 2021. Bansal has organized multiple academic workshops including the STOC 2020 Workshop on Recent Advances in Discrepancy and Applications, several SDP Days at CWI Amsterdam, and the Semester on Bridging Continuous and Discrete Optimization at UC Berkeley in Fall 2017.
Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.
Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.
Daniel Dadush is a part-time Professor of Geometry of Optimization at Utrecht University and leads the Networks & Optimization group at Centrum Wiskunde & Informatica (CWI). He has held previous positions as a Simons Postdoctoral Fellow at the Courant Institute of Mathematical Sciences (New York University) and a PhD in the ACO program (Algorithms, Combinatorics, and Optimization) at Georgia Tech. Research Interests: Lattice Algorithms, Geometry of Numbers, Linear/Integer Programming, Extended Formulations, Discrepancy Theory, Convex Optimization, Asymptotic Convex Geometry. Awards: Van Dantzig Prize (2020), Best Paper Award at CCC'20 (2020), Tucker Prize (2015). Advising: Supervised PhD/MSc students including Ben Bals, Samarth Tiwari, Sander Borst, Sophie Huiberts, Huck Bennett, and Yilin Li. Recent Publications: His 15 most recent articles focus on strongly polynomial algorithms, exact integer programming, convex optimization in the oracle model, matrix discrepancy, circuit diameter bounds, and integrality gaps, spanning journals and conferences like STOC, SODA, FOCS, and Mathematical Programming. Professional Activities: Organizer of the Dutch Day on Optimization (2022), co-organizer of workshops on Discrepancy Theory, Lattices, and Discrete Optimization at institutions like HIM Bonn and the Simons Institute. Served on program committees for STOC 2025, SODA 2024, and other major conferences. Teaching: Lectured on Interior Point Methods, Straight-Line Complexity, and courses in Continuous Optimization at Utrecht University and Mastermath.
Maurice van Keulen is an Associate Professor affiliated with the University of Twente's research institutes including Datamanagement & Biometrics, Digital Society Institute, and TechMed Centre. His multidisciplinary work bridges computer science, healthcare, and social systems. Research Focus: Van Keulen specializes in artificial intelligence applications with emphasis on: Data management (quality, integration, probabilistic databases) Explainable AI and interpretable machine learning models Healthcare informatics (cancer prediction, medical imaging, outcome analysis) Natural language processing and social media analytics His recent work explores dynamic sparse training, meta-learning for data imputation, and ethical AI frameworks. Publication Trends: Recent articles (2023-2025) show strong focus on: Interpretable AI methods in healthcare diagnostics Robust machine learning under data corruption Meta-learning approaches for data preprocessing 3D medical imaging and reconstruction techniques Awards: Beste paper award (2018) for work on probabilistic data conditioning Supervision & Activities: Has supervised 14 research projects and serves on executive boards including EDBT (Extending DataBase Technology) and IFIP WG 2.6. Leads research on ethical dimensions of AI systems.
