جست‌وجوی استاد و پژوهشگر

• Optical Transmission Systems
• Space Division Multiplexing (SDM)
• Quantum Secure Communications
• +۴ مورد دیگر

Chigo M. Okonkwo is a Full Professor and Chair of Secured Ultra High Capacity Transmission at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). He leads the high-capacity optical transmission laboratory within the Electro-Optical Communications Group at the Institute for Photonics Integration, focusing on next-generation photonic networks. Education: PhD in Optical Signal Processing, University of Essex (2009) Appointments: Since 2014 (tenured), 2010 as Post-Doctoral Researcher Key Research Areas: Space Division Multiplexing, Quantum Secure Communications, Advanced Modulation Formats His work spans optical transmission systems, including probabilistic signal shaping , low-complexity digital signal processing , and multi-mode/multi-core fiber components . Recent publications highlight breakthroughs in Petabit/s transmission and quantum cryptography use cases . Scientific recognition includes: ACP 2018 Best Paper Award ECOC 2018 Student Paper Award Optica 2022 Student Paper Awards Corning Outstanding Student Paper Competition Finalist 2025 He actively contributes to standardization efforts as a Technical Program Committee member for ECOC and serves as Senior Member of IEEE. Current projects include FIQCS (Fieldlab Quantum Cryptography Solutions) and NGF-ECO1 (Netherlands Quantum Flagship), advancing quantum-secure protocols and petabit/s capacity scaling.

Assela Pathiranaمشاهده پروفایل

استاد

• Water Infrastructure Asset Management
• Climate Resilience of Small Island Developing States (SIDS)
• Hydrological Modeling
• +۵ مورد دیگر

Dr. Assela Pathirana is a Professor at the IHE Delft Institute for Water Education, specializing in water infrastructure asset management (WIAM), climate resilience of Small Island Developing States (SIDS), and sustainable urban water systems. His work bridges academia, policy, and practice, focusing on digitalization, data-driven decision-making, and nature-based solutions. Key roles include Chief Technical Advisor for the Maldives’ water systems and leadership of a MOOC on SIDS climate adaptation. He holds a BSc (First Class Honours) from the University of Peradeniya and advanced degrees from the University of Tokyo, specializing in hydrology and water resources engineering. Education: Bachelor of Science in Civil Engineering (First Class Honours), University of Peradeniya, Sri Lanka Master’s and Doctoral Degrees in Civil Engineering (Hydrology and Water Resources), University of Tokyo, Japan Research Interests: Dr. Pathirana’s work emphasizes climate resilience, SIDS adaptation, urban flood risk management, and sustainable infrastructure. He develops decision-support tools for flood forecasting, evaluates land-use changes in Jakarta, and promotes equity in water rationing systems. His interdisciplinary approach integrates hydrological modeling, open-source software development, and policy analysis. Advising & Capacity Building: He leads training-of-trainers programs and capacity-building initiatives, enhancing postgraduate education in technical disciplines. His efforts focus on didactics and pedagogy, ensuring graduates gain both theoretical knowledge and practical expertise. Key Projects: Developed the WIAM curriculum at IHE Delft MOOC on SIDS climate adaptation and water security Consultancy for the Maldives’ Ministry of Environment and UNDP Labs & Collaborations: Engages in cross-disciplinary teams addressing urban water challenges, including sponge cities in China and flexible adaptation planning in Melbourne and Pune. His work with UNESCO-ICHARM and UNU underscores global water security and disaster risk reduction.

Jan Draismaمشاهده پروفایل

استاد

• Algebraic Geometry
• Combinatorics
• Algebraic Statistics
• +۸ مورد دیگر

Jan Draisma is a full professor of Mathematics at the University of Bern and a part-time full professor of Applied Algebra and Geometry at Eindhoven University of Technology (TU/e), where he is affiliated with the Department of Mathematics and Computer Science, specifically in Discrete Algebra and Geometry and Coding Theory and Cryptology. He obtained his Master's and Ph.D. degrees from TU/e cum laude and held a postdoctoral position at the University of Basel (2002–2005). He returned to TU/e as an assistant professor, later advancing to associate professor (2011–2016), and served as a part-time full professor at VU Amsterdam (2015–2016). His research focuses on the interplay between combinatorics, statistics, and algebraic geometry. Key areas include tropical geometry, algebraic statistics, symmetric systems of polynomial equations in infinitely many variables, and representation stability. His work often explores the structure of infinite-dimensional algebraic objects and their finite approximations. The most recent publications highlight trends in polynomial functors, topological Noetherianity, amoebas of linear spaces, and the geometry of tensor representations. These works reflect a deep integration of algebraic geometry with combinatorics and category theory, emphasizing stabilization phenomena and symmetry in algebraic structures. NWO Vici Award: Stabilisation in Algebra and Geometry (2015) NWO Vidi Award: Finite thanks to symmetry (2010) Draisma has received significant research funding, including the NWO Vidi and Vici grants, and two NWO Free Competition grants (2008, 2012). He has supervised 16 students and is actively involved in the academic community as an associate editor for Experimental Mathematics, SIAM Journal on Applied Algebra and Geometry, and Linear and Multilinear Algebra. He has held leadership roles in major conferences such as MEGA 2015 and SIAM AG 19. He is affiliated with the research groups in Discrete Algebra and Geometry and Coding Theory and Cryptology at TU/e and leads research activities centered on algebraic methods in discrete mathematics and statistics.

Tarek Alskaifمشاهده پروفایل

دانشیار

• Energy Informatics
• Smart Grids
• Artificial Intelligence
• +۹ مورد دیگر

Dr. Tarek Alskaif is an Associate Professor of Energy Informatics at Wageningen University & Research, specializing in the intersection of information technology and energy systems. He leads research on smart energy systems, focusing on electricity markets, distributed energy resources, and AI-driven solutions. His work integrates modeling, optimization, and big data analytics to advance the sustainable energy transition. Education: PhD in Energy Informatics (2012–2016, Cum Laude) from Universitat Politècnica de Catalunya, Spain. Postdoc at Utrecht University’s Copernicus Institute (2016–2020). Current roles include coordinating the BSc Data Science Minor and teaching Python and Big Data courses. Research interests emphasize leveraging digitalization for energy systems, including smart grids, electric mobility, and battery storage. Notable projects include HighLO Energy Markets (EU-funded, using particle physics and AI for market transparency) and MESSM (coordinated via TKI Urban Energy). He also leads the AI ELSA Lab (NWO-funded). Editorial roles include Associate Editor for IEEE Transactions on Smart Grid and IEEE Power Engineering Letters . Member of IEEE, the Netherlands Institute for Research on ICT (4TU.NIRICT), and the Technical Program Committee for IEEE SmartGridComm and PSCC 2026. Has supervised over 50 students (MSc/BSc) and 7 PhDs. Projects address challenges like grid congestion, EV charging optimization, and decentralized energy trading. His work bridges academic research with industry collaborations, including partnerships with CERN and ACER.

Marcus Gerholdمشاهده پروفایل

استادیار

• Model-Based Testing
• Software Reliability
• Critical Infrastructures
• +۷ مورد دیگر

Marcus Gerhold is an Assistant Professor in the Formal Methods and Tools group at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on model-based testing for software reliability in critical infrastructures, particularly railway systems, alongside significant contributions to game design and programming language analysis. His educational background includes: PhD in Computer Science from University of Twente (2018): Choice and Chance: Model-based Testing of Stochastic Behaviour MSc in Mathematics from Friedrich Schiller Universität Jena (2013): Embeddings of Weighted Morrey Spaces BSc in Mathematics from Friedrich Schiller Universität Jena (2011): Entropy-, Approximation- and Kolmogorov Numbers on Quasi-Banach Spaces Gerhold's research integrates theoretical model-based testing with practical critical infrastructure applications . His work on railway conformance testing addresses EULYNX controller validation, while his game design research explores affective mirroring in NPCs and procedural dungeon generation. The code modernity analysis stream leverages static analysis to quantify legacy code evolution across languages like Python and PHP, revealing version identification challenges through deep learning. Publication trends show consistent focus on model-based testing methodologies (40%), railway safety applications (25%), and innovative game design/code analysis (35%). Recent work increasingly incorporates AI/ML techniques for UML assessment and Python version identification, while maintaining rigorous formal methods foundations. He actively mentors 63 students across all academic levels and contributes to major research initiatives: STORM_SAFE (ERDF, 2024): Daily Supervisor for WP1/WP2 on software reliability for critical infrastructures ZORRO (KIC grant, 2023): Daily Supervisor for WP4 on zero downtime in cyber-physical systems MISSION (MSCA RISE, 2021-2025): Interim coordinator (early 2024) for space systems modeling As part of the Formal Methods and Tools research group, Gerhold participates in European collaborations while serving on SAC-SVT 2024 and FormaliSE 2023 program committees.

Jop Briëtمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Quantum Information Theory
• Combinatorics
• +۳ مورد دیگر

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.

Laura Toniمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Immersive Communications
• +۵ مورد دیگر

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.

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete Optimization
• +۷ مورد دیگر

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.

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +۵ مورد دیگر

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.

• Lattice Algorithms and Geometry of Numbers
• Linear and Integer Programming
• Extended Formulations
• +۳ مورد دیگر

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.

• Optical Transmission Systems
• Space Division Multiplexing
• Quantum Cryptography
• +۵ مورد دیگر

Chigo Okonkwo is Full Professor and Chair of Secured Ultra High Capacity Transmission at the Department of Electrical Engineering , Eindhoven University of Technology. He leads the high-capacity optical transmission laboratory at the Institute for Photonics Integration and contributes to the Center for Quantum Materials and Technology Eindhoven (QT/e) . Academic Qualifications: MSc in Telecommunications and Information Systems, University of Essex (2002) PhD in Optical Signal Processing, University of Essex (2010) Research Interests: Professor Okonkwo focuses on: Maximizing capacity of single-mode fiber systems through advanced-coded modulation and Probabilistic/Geometrically shaped signals Developing Space Division Multiplexing (SDM) systems for Petabit/s transmission using multi-mode/multi-core fibers Quantum secure communications and cryptographic protocol development Optical vector network analyzer (OVNA) technology for SDM fiber characterization Free-space optical link deployment in urban environments Low-complexity digital signal processing algorithms Recent Publications Trends: His 15 most recent articles (2023-2025) demonstrate active research in: Quantum-classical network integration Extreme capacity fiber transmission (Petabit/s systems) Machine learning for optical diagnostics SDM fiber measurement technologies Hybrid QKD-PQC security frameworks Free-space optical urban communication Scientific Awards: Asia Communications and Photonics Conference (ACP) 2018 Best Paper Award European Conference on Optical Communications (ECOC) 2018 Student Paper Award Optica Student Paper Awards (2022) Corning Outstanding Student Paper Competition Finalist (2025) Advisory & Collaborations: Advisor to 8+ researchers including Menno van den Hout, Vincent van Vliet, and Thomas Bradley Technical Program Committee Member, European Conference on Optical Communications (ECOC) since 2014 Sub Committee Chair for Digital Signal Processing track at ECOC 2018 General Chair for OSA Advanced Photonics Congress on Signal Processing for Photonics Collaborates with EU projects (HOMTech, PhotonDelta) and industrial partners Co-founder and Chief Technology Officer of CUbIQ Technologies Laboratory & Infrastructure: Maintains the world-class High Capacity Optical Transmission Lab at TU/e, featuring: Advanced SDM fiber testing equipment Quantum communication research infrastructure Free-space optical link experimental setups Multi-core fiber amplification systems Coherent transmission testbeds Machine learning-enabled diagnostic tools

Bert de Vriesمشاهده پروفایل

استاد

• Bayesian Machine Learning
• Signal Processing
• Biomedical Applications
• +۳ مورد دیگر

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.

• Computational Biology
• Cell Motility
• Dynamic Modelling
• +۳ مورد دیگر

Dr. Leonie van Steijn is an Assistant Professor at the Faculty of Science, Utrecht University , specializing in Theoretical Biology and Bioinformatics. Her research focuses on computational modeling of cell motility and navigation, particularly how cells process environmental cues to influence their movement. She contributes to teaching in the Molecular and Biophysical Life Sciences Bachelor program, emphasizing computational skills, programming, and statistical modeling. Education : PhD in Mathematical Institute, Leiden University (2016–2021). Research Interests : Van Steijn investigates the interplay between cell deformability, environmental microstructures, and motility patterns. Her work extends to modeling leukocyte migration during wound healing and genome-scale metabolic networks in zebrafish. Key methodologies include Python-based simulations and dynamic modeling of biological systems. Publications : Her recent studies explore topotaxis in dense microenvironments, adhesion dynamics in motility modes, and mathematical foundations of biological networks. These span interdisciplinary fields from immunology to theoretical mathematics. Skills : Expertise in Python programming, computational modeling, and development of biological simulation frameworks.

Wouter Botzenمشاهده پروفایل

استاد

• Climate Change Economics
• Natural Disasters
• Behavioral Economics
• +۵ مورد دیگر

Prof. Wouter Botzen holds a dual role as a full Professor of Economics of Global Environmental Change at the Utrecht University School of Economics and Professor of Economics of Climate Change and Natural Disasters at VU University Amsterdam's Department of Environmental Economics, where he also serves as Head of the department. He is a senior research fellow at the Wharton School's Risk Management and Decision Processes Center at the University of Pennsylvania. His work bridges environmental economics, risk analysis, and policy design. Botzen earned his MSc in Economics from Maastricht University (2005–2006) and completed his PhD at VU University Amsterdam (2006–2009). His academic career includes prior roles as Associate and Assistant Professor at VU University (2010–2016) and visiting scholar positions at institutions in the U.S. and Spain. His research focuses on climate change economics, behavioral responses to disaster risks, valuation of natural environments, and flood risk management. He emphasizes integrating behavioral insights into policy tools, such as insurance mechanisms to incentivize risk reduction and adaptation. Key projects include Future Deltas , NATURVATION , and Pathways to Sustainability , which explore urban resilience, nature-based solutions, and socio-economic tipping points from climate impacts. Botzen has received prestigious awards such as the Vidi and Veni scholarships from NWO, the Lloyd's Science of Risk Prize, and multiple best-paper awards in Risk Analysis . His grants include EU-funded initiatives like NATURVATION and Dutch-funded projects on sustainable energy governance. He collaborates extensively with academic teams and policymakers, contributing to global studies on flood risk, carbon pricing, and climate adaptation policies. His work aims to address systemic challenges in risk mitigation and resource allocation through interdisciplinary frameworks.

Jasper Goselingمشاهده پروفایل

دانشیار

• Differential Privacy
• Network Coding
• Optimization Problem
• +۴ مورد دیگر

Jasper Goseling is an Associate Professor at the Digital Society Institute and affiliated with the Mathematics of Operations Research department. His research spans differential privacy, network coding, optimization, and wireless systems, often bridging theoretical and applied domains. Key research areas: Differential Privacy, Network Coding, Optimization, Wireless Sensor Networks, Machine Learning His recent work focuses on robust optimization techniques for local differential privacy, addressing trade-offs between data utility and privacy preservation. Earlier contributions include studies on energy-efficient data collection in sensor networks, caching strategies in wireless environments, and entropy-based analysis of hydrothermal systems. Article trends reveal a strong emphasis on privacy-preserving algorithms (2022-2024) and historical expertise in network coding, queueing theory, and thermodynamic entropy. His research integrates mathematical rigor with practical applications in wireless communication and data management. Activities include organizing the 45th Symposium on Information Theory and Signal Processing (2025) and leadership roles in the IEEE Benelux Chapter on Information Theory (Chair, 2017; Member, 2012-2017). He also contributed to the 2015 European School of Information Theory.