Find Academic Supervisors

Erik QuaeghebeurView profile

Assistant Professor

• Probability Theory
• Statistics
• Uncertainty in Artificial Intelligence
• +4 more

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)

Raymond H. CuijpersView profile

Associate Professor

• Cognitive Robotics
• Human-Robot Interaction
• Socially Assistive Robotics
• +5 more

Raymond H. Cuijpers is an Associate Professor at Eindhoven University of Technology in the Human Technology Interaction group. His research focuses on Cognitive Robotics , Human-Robot Interaction , and Artificial Intelligence for cognitive agents, with applications in healthcare robotics and aging population support. PhD in Physics of Man from Utrecht University (2000) Postdoctoral research at Erasmus MC Rotterdam and Radboud University Nijmegen Key research areas include: Developing socially intelligent robots with proper social cue interpretation Hybrid AI approaches for real-world complexity handling Visual-haptic perception integration in human motor control Service robots for COPD patient assistance (KSERA project) Rescue robotics and tele-operation applications Recent research output (2025) includes studies on: Personalization in human-robot communication Optimal lighting for elderly visual perception Human-robot bonding mechanisms Interactive sensorized platforms for homecare (GUARDIAN) Audiovisual temporal integration in virtual environments He coordinates large-scale European projects like GUARDIAN and previously KSERA, contributes to sustainable development goals through healthcare robotics, and serves on editorial boards of leading journals including International Journal of Social Robotics . His work spans both technical robotics development and human-centric interaction studies.

Alfred SteinView profile

Professor

• Spatial Statistics
• Image Analysis
• Remote Sensing
• +7 more

Prof. Alfred Stein is a Full Professor in Spatial Statistics and Image Analysis at the Department of Earth Observation Science, Faculty ITC, University of Twente. He earned his MSc in Mathematics and Information Science from Eindhoven University of Technology and a PhD in Spatial Statistics from Wageningen University. His career spans roles at Wageningen University (1988–2002), ITC (2002–present), including leadership positions as department head, vice-rector research, and portfolio holder for education. Education: MSc (Eindhoven University of Technology), PhD (Wageningen University) Leadership: Department Head (Earth Observation Science), Vice-Rector Research (2008–2012), Portfolio Holder Education (2012–) His research focuses on Spatial and Spatio-Temporal Statistics , emphasizing Bayesian inference , data quality , image analysis , and fuzzy techniques . Key application domains include agriculture, health, urban land use, coastal systems, hazards, and wildlife. He has mentored over 30 PhD students since 1998, with 11 currently under supervision. Recent research trends highlight AI-driven remote sensing for glacier mapping, urban livability, and disease modeling. Publications span Deep Learning for SAR tomography, Bayesian hierarchical models for health data, and multitemporal SAR analysis for environmental monitoring. Awards include the Best Paper Award (2019) and ISARA Founder's Award (2020) . Scientific Awards Best Paper Award (2019) ISARA Founder's Award (2020) As Editor-in-Chief of Spatial Statistics and associate editor for multiple journals, he leads academic discourse. Collaborations include the University of Cape Town and University of Pretoria as Honorary Professor. His work contributes to UN Sustainable Development Goals, particularly in climate action and sustainable cities.

• Artificial Intelligence and Law
• Computational Argumentation
• Legal Reasoning
• +5 more

Professor Henry Prakken is a leading researcher in Artificial Intelligence and Law at Utrecht University's Department of Computer Science. He holds a prominent position in the Responsible AI research group and has been instrumental in advancing computational models of legal argumentation. With an extensive publication record spanning over three decades, Prakken has established himself as a central figure in the intersection of law and artificial intelligence. Prakken's research interests focus on computational models of argumentation, legal reasoning systems, and the application of AI to legal domains. His work bridges theoretical foundations with practical applications, particularly in case-based reasoning, Bayesian networks for legal evidence, and formal argumentation frameworks. He has made significant contributions to understanding how computational models can represent and analyze legal reasoning processes, with particular attention to precedent-based reasoning and argument strength. His publication record demonstrates consistent research productivity with numerous high-impact papers in venues like Artificial Intelligence, Artificial Intelligence and Law, and proceedings of major conferences including ICAIL and COMMA. Recent work has expanded into evaluating generative AI's legal reasoning capabilities and developing more sophisticated models of argument strength and acceptability. Past president of the International Association for AI & Law (IAAIL) Past president of the JURIX Foundation for Legal Knowledge-Based Systems Past president of the steering committee of the COMMA conferences on Computational Models of Argument Editorial board member of Artificial Intelligence and Law Associate editor of Artificial Intelligence (2017-2022) Prakken has played a significant role in mentoring and shaping the field through his leadership positions and editorial work. His research has influenced both theoretical developments in computational argumentation and practical applications in legal technology, contributing to the growing field of responsible AI in legal contexts.

Laura ToniView profile

Associate Professor

• Machine Learning
• Signal Processing
• Immersive Communications
• +5 more

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.

Jan van den BrakelView profile

Professor

• Survey Methodology
• Statistical Analysis
• Time Series Modeling
• +7 more

Jan van den Brakel is an Extraordinary Professor of Survey Methodology at Maastricht University and a Senior Statistician at Statistics Netherlands. He holds a position in the Department of Quantitative Economics within the School of Business and Economics. His primary affiliation is with the methodology department at Statistics Netherlands, where he focuses on advancing statistical methods for official statistics. **Research Interests:** Professor van den Brakel specializes in survey methodology, with a focus on mixed-mode surveys, time series modeling, small area estimation, and discontinuity analysis. He explores topics such as sampling techniques, variance estimation, and the integration of non-probability data sources (e.g., social media) into official statistics. His work addresses challenges in labor force surveys, health surveys, and the impact of technological changes like tablet-based data collection. **Key Projects:** Current projects include mixed-frequency time series modeling for short-term statistics, accuracy assessments of non-probability web panels, and modeling discontinuities in official surveys. He has contributed to high-impact studies, such as analyzing mobility patterns during the pandemic and improving unemployment rate estimation using Bayesian models. **Awards:** His 2013 paper on factorial designs in probability samples was honored as the best journal paper by Survey Methodology in 2014. He is a member of the Advisory Committee on Statistical Methods at Statistics Canada, reflecting his international leadership in statistical methodology. **Professional Roles:** In addition to his academic role, he collaborates extensively with national statistical offices, advising on methodological challenges. His work bridges theory and practice, ensuring statistical methods are robust and applicable to real-world data collection and analysis.

• Dark Matter
• Black Holes
• Gravitational Waves
• +3 more

Gianfranco Bertone is a Professor at the Faculty of Science, University of Amsterdam, specializing in astrophysics and theoretical physics with a focus on dark matter, black holes, and gravitational waves. His work bridges cosmology and particle physics through multi-messenger approaches. Research Interests: Dark matter detection via gravitational wave signatures Black hole binary dynamics in dark matter environments Relativistic simulations of extreme mass ratio inspirals Multi-messenger astronomy and fundamental physics Cosmological simulations for dark matter distribution Publication Trends: Recent works emphasize gravitational wave astronomy's role in dark matter studies, including waveform distortions from dark matter spikes, boson cloud effects in black hole binaries, and simulation-based inference for astrophysical observations. His research spans theoretical modeling, computational astrophysics, and observational constraints.

Peter DesainView profile

Professor

• Brain-Computer Interfaces
• Neuroscience
• Electrophysiology
• +3 more

Peter Desain is a Professor and Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour, Radboud University. His work focuses on developing advanced brain-computer interfaces (BCI) leveraging evoked potentials, particularly through code-modulated visual and auditory stimuli. He pioneers methods like noise-tagging and Bayesian dynamic stopping to enhance BCI efficiency and accessibility. His research spans neurotechnology, electrophysiological modeling, and clinical applications such as objective EEG audiometry and ALS communication aids. Recent studies emphasize gaze-independent systems, semantic decoding, and minimizing BCI calibration requirements. Key contributions include optimizing c-VEP code-books, real-time fMRI neurofeedback for memory contexts, and literature reviews on BCI design trends. Experimental pilot studies explore auditory attention and high-frequency SSVEP dynamics. No scientific awards are explicitly mentioned. His work integrates multidisciplinary approaches, bridging neuroscience, machine learning, and engineering to advance human-computer interaction and clinical tools.

Rineke VerbruggeView profile

Professor

• Artificial Intelligence
• Logic and Cognition
• Multi-agent systems
• +4 more

Prof. Rineke Verbrugge is a Professor in Artificial Intelligence at the University of Groningen's Faculty of Science and Engineering, affiliated with the Bernoulli Institute. Her research focuses on computational theory of mind, multi-agent systems, hybrid intelligence, and logical frameworks applied to social networks and legal reasoning. She holds additional roles on the Institute Advisory Board of CWI (Dutch National Research Institute for Mathematics and Computer Science) and several ERC/NWO selection committees. Her work bridges cognitive science and AI, emphasizing human-agent collaboration, belief formation in groups, and ethical AI design. Recent projects include developing computational models for theory of mind in negotiations and scenario-based Bayesian networks for legal evidence analysis. She has authored over 220 publications and supervised multiple PhD candidates in AI and logic. Key research themes include higher-order theory of mind applications, zero-one laws in provability logic, and agent-based policy evaluation for sustainable technologies. Her contributions span conferences like AAMAS, ICAIL, and HHAI, addressing topics from lie detection mechanisms to privacy conflicts in multi-user systems.

• Statistics
• Applied Data Science
• Veterinary Epidemiology
• +2 more

Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre

Rianne de HeideView profile

Assistant Professor

• Machine Learning
• Statistics
• Hypothesis Testing
• +7 more

Rianne de Heide is an Assistant Professor in the Statistics group (STAT) within the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. She maintains collaborative arrangements with LUXs Data Science in Leiden, CWI, and VU Mathematics in Amsterdam as a guest researcher while working partly remotely during her family's relocation. Her academic journey includes a previous position as Assistant Professor at Vrije Universiteit Amsterdam. PhD Dissertation: 'Bayesian Learning: Challenges, Limitations and Pragmatics' (2020) MSc Thesis: 'The Safe-Bayesian Lasso' (2016) De Heide's research spans multiple interconnected domains within statistics and machine learning, with particular emphasis on developing mathematically rigorous frameworks that remain accessible to diverse audiences. Her work bridges theoretical foundations with practical applications, focusing on hypothesis testing with e-values, Bayesian learning methodologies, and best-arm identification problems in multi-armed bandit settings. She demonstrates exceptional interdisciplinary range, connecting statistical theory with philosophical inquiry and even theological discussions as evidenced by her publications on biblical authorship verification and mathematical beauty. Analysis of her publication trajectory reveals a clear evolution toward developing anytime-valid statistical methods, particularly through e-values and e-processes for multiple testing scenarios. Her recent work shows increasing focus on foundational questions in statistical inference while maintaining strong connections to practical machine learning applications. The 2024 'Safe Testing' paper in the Journal of the Royal Statistical Society represents a significant contribution that generated a formal discussion meeting. VENI project 'E-values for Multiple Testing' NWO M2 grant of €742,708 with Jelle Goeman (funding 2 PhD students and a scientific programmer) 2025 Bernoulli Society New Researcher Award De Heide actively supervises research through her VENI project and the NWO M2 grant, while also contributing to broader academic service through the 'Kindness and Excellence in Academia' initiative she co-founded. This initiative addresses critical cultural issues in academic environments through opinion pieces, resources, and community building around compassionate academic practices. She has organized specialized events like the E-Day meet-up for e-value researchers at CWI in Amsterdam, demonstrating leadership in her niche research community. Her research activities are centered around the Statistics group at the University of Twente, with significant external collaborations through the E-mailing list for e-value researchers and partnerships with institutions including CWI, VU Amsterdam, and Leiden's LUXs Data Science. The interdisciplinary nature of her work creates connections across mathematics, computer science, philosophy, and even religious studies.

Wouter M. Koolen-WijkstraView profile

Professor

• Machine Learning Theory
• Game Theory
• Information Theory
• +5 more

Wouter M. Koolen-Wijkstra is a Professor of Mathematical Machine Learning at the University of Twente (Statistics group) and a Scientific Staff Member at Centrum Wiskunde & Informatica (CWI), Amsterdam, in the Machine Learning department. His research bridges theoretical machine learning, game theory, and statistics, with active projects on multi-armed bandits, online learning, and safe inference methodologies. He co-leads INRIA-CWI associate teams (6PAC and 4TUNE) and is an ELLIS Scholar. His work emphasizes provable guarantees in learning algorithms, including: Regret minimization under risk-averse scenarios Multi-scale adaptation in online decision-making Game-theoretic equilibria computation Anytime-valid statistical inference via e-processes Recent publications demonstrate a focus on robust learning frameworks , particularly in bandit problems, hypothesis testing, and Nash equilibrium characterization, often leveraging information-theoretic and optimization principles. Awards include: Veni Grant (2015) for 'Learning at the Intrinsic Task Pace' QUT Vice-Chancellor's Fellowship (2013) for multitask learning Rubicon Grant (2010) for game-theoretic online learning ELLIS Scholar recognition He teaches graduate courses on Machine Learning Theory and Graphical Models at CWI. Current grants include collaborations with INRIA (4TUNE and 6PAC teams) and industry partnerships (e.g., PPS Booking.COM).

Evert van NieuwenburgView profile

Assistant Professor

• Quantum Physics
• Machine Learning
• Condensed Matter Physics
• +6 more

Dr. Evert van Nieuwenburg is an Assistant Professor at Leiden University, affiliated with both the Leiden Institute of Advanced Computer Science (LIACS) and the Leiden Institute of Physics (LION). His research bridges the fields of Quantum Physics , Machine Learning , and Condensed Matter Physics , with a focus on quantum algorithms, reinforcement learning, and quantum game development (e.g., Quantum TiqTaqToe ). He actively contributes to the Applied Quantum Algorithms (aQa) initiative and leads the QuantumPlayed subgroup for quantum games and education. Research Interests: AI-driven quantum experiment control, quantum machine learning, variational quantum circuits, and quantum games for education and intuition-building. Publications: 15+ peer-reviewed works spanning quantum error correction, phase transitions, reinforcement learning in quantum systems, and quantum dot array simulations. Community Engagement: Developer of educational quantum games, open science advocate, and active participant in interdisciplinary initiatives. Selected Trends: His work demonstrates AI's transformative role in quantum physics, from decoding error-correcting codes with graph neural networks to merging reinforcement learning with quantum control systems. Labs & Initiatives: Affiliated with the Applied Quantum Algorithms (aQa) initiative and co-founder of QuantumPlayed , where quantum mechanics meets game theory to engage diverse audiences.

• Stochastic Processes
• Operations Research
• Data-Driven Decision Making
• +4 more

Stella Kapodistria is an Associate Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds roles as EAISI High Tech Systems Associate Professor and editorial board member of journals like MCAP and PEIS. Her research focuses on data-driven decision-making, stochastic systems optimization, and maintenance policies, with applications in renewable energy, critical infrastructure, and cryptocurrency networks. She has secured grants including NWA-ORC, NWO Big Data, and TKI WoZ, and collaborates with industry partners in the Brainport region. Education: BSc (2003), MSc (2006, Hons.), and PhD (2009, summa cum laude) in Mathematics from the University of Athens. Postdoc at TU/e, followed by roles at Groningen University and TU/e's Stochastic Operations Research group. Teaching includes courses on Optimal Decision Making, Stochastic Performance Modeling, and Financial Mathematics. Research interests emphasize real-time learning, system resilience, and scalable algorithms for complex networks. Recent work addresses maintenance logistics, blockchain confirmation times, and wind energy prediction. She has published over 40 peer-reviewed articles and contributed to the 4TU Resilience Engineering Center. Awards include editorial leadership roles and grant funding. Advised 32 academic works and oversees industrial projects bridging theory and practice. Her labs and collaborations focus on adaptive systems, predictive analytics, and sustainable engineering solutions.

Peter Groot KoerkampView profile

Professor

• Agricultural Engineering
• Environmental Engineering
• Animal Production Systems
• +7 more

Prof. Peter Groot Koerkamp is a Professor and Managing Chairholder at the Agricultural Biosystems Engineering group at Wageningen University. His expertise spans agricultural engineering, environmental systems, and sustainable livestock production. He holds an MSc (1990) and PhD (1998) from Wageningen University, with a focus on ammonia emissions from poultry systems. He has held roles as a researcher, project manager, and senior scientist across multiple institutes before joining Wageningen University in 2005. His research emphasizes sustainable agricultural systems, including manure/nutrient management, gaseous emissions, and animal welfare. He leads projects on methane monitoring in dairy cows, climate control in livestock housing, and bioenergy from segregated excreta. He supervises over 50 PhD students and collaborates on initiatives like the National Growth Fund’s regenerative agriculture projects. Notable projects include Synergia (optimizing dairy systems) and collaborations with the European Society of Agricultural Engineers (EurAgEng). His work integrates environmental engineering with technological innovation, addressing challenges like particulate matter reduction in poultry houses and precision livestock farming.