جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence and Law
• Computational Argumentation
• Legal Reasoning
• +۵ مورد دیگر

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.

Jan van den Brakelمشاهده پروفایل

استاد

• Survey Methodology
• Statistical Analysis
• Time Series Modeling
• +۷ مورد دیگر

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
• +۳ مورد دیگر

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.

• Statistics
• Applied Data Science
• Veterinary Epidemiology
• +۲ مورد دیگر

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 Heideمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Hypothesis Testing
• +۷ مورد دیگر

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-Wijkstraمشاهده پروفایل

استاد

• Machine Learning Theory
• Game Theory
• Information Theory
• +۵ مورد دیگر

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 Nieuwenburgمشاهده پروفایل

استادیار

• Quantum Physics
• Machine Learning
• Condensed Matter Physics
• +۶ مورد دیگر

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
• +۴ مورد دیگر

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.

Clemens V. Verhooselمشاهده پروفایل

دانشیار

• Computational Mechanics
• Isogeometric Analysis
• Fluid Dynamics
• +۵ مورد دیگر

Clemens V. Verhoosel is an Associate Professor in Computational Methods for Model- and Data-Driven Engineering at Eindhoven University of Technology (TU/e). He holds positions in the Department of Mechanical Engineering under the Energy Technology and Fluid Dynamics section, and is affiliated with the EAISI Foundational initiative. His research focuses on scan-based immersed isogeometric analysis, uncertainty quantification, and Bayesian inference for complex engineering problems. He leads the Group Verhoosel and manages the Engineering Mechanics Graduate School since 2018. Education: MSc (Aerospace Engineering, TU Delft, 2005, cum laude PhD, TU Delft, 2009). Postdoctoral research at University of Texas at Austin (2009-2010). Awarded NWO VENI Grant (2011). Research interests include numerical methods for solid mechanics, fluid dynamics, coupled problems, and applications in biomedical engineering (e.g., cardiac mechanics). He develops open-source tools like the Nutils toolkit and collaborates with industry partners such as Evalf Computing. Key contributions include isogeometric analysis for fracture mechanics, phase-field models, and mesh-free simulation workflows. Honors: NWO Veni Award (2011). Teaching includes Advanced Discretization Techniques and Scientific Computing courses. Active in professional activities, including invited talks on cardiac mechanics and computational methods.

• Computational Mechanics
• Granular Materials
• Machine Learning in Physics-Based Simulations
• +۷ مورد دیگر

H. Cheng is a researcher at the University of Twente, affiliated with the Faculty of Engineering Technology and the Department of Mechanics of Solids, Surfaces and Systems. He plays a central role in several interdisciplinary research projects focused on computational modeling of granular materials, geohazards, and machine learning integration in physics-based simulations. His research centers on advancing numerical methods such as the Discrete Element Method (DEM) and developing machine learning surrogates for efficient uncertainty quantification in complex systems. Key project areas include offshore infrastructure resilience under climate change (POSEIDON), dynamic fault slip in induced seismicity (FastSlip), upscaling particulate systems for industrial applications (TUSAIL), and automated segmentation of soil-root systems using micro-CT imaging (UNSAT). H. Cheng leads and supervises multiple early-career researchers across EU-funded initiatives, including MSCA Doctoral Networks and COST Actions. He is the main applicant and supervisor in the GrainLearning project, which integrates Bayesian inference with physics-based models to improve simulation accuracy and efficiency. His scientific contributions span collaborative research across academia and industry, with a strong emphasis on open science, reproducibility, and cross-sectoral training. He contributes to community-building through initiatives like ON-DEM, promoting best practices in particle-based simulations. Supervisor of multiple PhD students and postdoctoral researchers Daily supervisor in POSEIDON, FastSlip, TUSAIL, UNSAT Vice-lead of Working Group 1 in ON-DEM COST Action Main applicant and project lead for GrainLearning H. Cheng is actively involved in training the next generation of computational scientists and engineers, with a focus on interdisciplinary methodologies that bridge mechanics, data science, and industrial applications.

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.

Feixiong Liaoمشاهده پروفایل

استادیار

• Urban Planning
• Transportation Systems
• Mobility Behavior
• +۳ مورد دیگر

Feixiong Liao is a tenured Assistant Professor in Urban Planning and Transportation at Eindhoven University of Technology. He serves as Education Coordinator for the USRE Unit and holds affiliations with EAISI Mobility. His research focuses on developing large-scale urban transportation planning models. Education includes a doctorate degree (cum laude) from TU/e, where he developed multi-state supernetwork approaches for metropolitan accessibility. Post-doctoral research involved international collaborations on travel behavior analysis. Research interests center on network modeling for mobility demand, accessibility measurement in urban contexts, behavior analysis with bounded rationality, service innovation for mobility issues, and sustainability in mobility-energy-social nexuses. His work integrates land-use transport modeling, AI applications, and climate-neutral mobility solutions. Publications show strong focus on transportation modeling innovations, particularly in multimodal systems, activity-travel behavior, and sustainable mobility solutions. Recent works increasingly incorporate machine learning and big data approaches to urban mobility challenges. Best Paper Award (in CTS) 2019 Best Paper Award (POMS in China) 2019 Education Innovation Fund 2020 HKSTS Best Student Paper Award 2011 Supervises 5-7 PhD students and post-docs annually. Leads the international joint research program JPI Urban Europe (2019-2024) on accessibility valuation. Secured multiple grants including a five-year education innovation fund for challenge-based learning in smart mobility. Leads research groups including EAISI Mobility and Urban Planning and Transportation Group. Coordinates the UBeX Urban Behavior eXtended reality lab project until 2026.

• Machine Learning
• Neural Networks
• Uncertainty Quantification
• +۴ مورد دیگر

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen within the Faculty of Science and Engineering and the Artificial Intelligence department of the Bernoulli Institute. He holds a PhD from Heriot-Watt University (2019) and a Master's in Autonomous Systems from Bonn-Rhein-Sieg University of Applied Sciences (2014). His research focuses on trustworthy machine learning models , particularly in uncertainty quantification , medical AI , and robotics , with applications in computer vision and explainable AI. He teaches courses like Introduction to Machine Learning and Deep Learning at the Bachelor and Master levels. His work emphasizes robustness in AI systems, including uncertainty estimation for medical applications, super-resolution techniques, and neuromorphic robotics. He has published widely on topics like Bayesian neural networks, prompt tuning, and sanity checks for explanations. Notable awards include Best Reviewer at ICML (2024) and Highlighted Reviewer at ICLR (2022). He collaborates with institutions like the German Research Center for Artificial Intelligence and actively contributes to open-source datasets (e.g., the Japanese Uncertain Scenes Dataset ). Key grants and activities include organizing the ENLIGHT BIP Course on Deep Learning for Forestry and teaching at the European Summer School on AI . His research also addresses regulatory challenges like the EU AI Act's implications for uncertainty quantification in general-purpose AI.

Leah Hendersonمشاهده پروفایل

استاد

• Philosophy of Science
• Epistemology
• Scientific Realism
• +۴ مورد دیگر

Leah Henderson is a Professor in the Department of Theoretical Philosophy at the University of Groningen. She holds the endowed chair on 'Societal Trust' from the Royal Holland Society of Sciences and Humanities (KHMW) and the Agricola School. Previously, she was a Rosalind Franklin Fellow since 2015. Her expertise spans philosophy of science, epistemology, and the philosophy of quantum theory. She earned a PhD in Philosophy from MIT and a DPhil in theoretical physics from the University of Oxford, with postdoctoral work in quantum information theory. Her research focuses on scientific realism, anti-realism, inference to the best explanation, and the problem of induction. She also explores societal trust dynamics and belief polarization in scientific contexts. Henderson has contributed to debates on higher-order evidence, meta-induction, and conjunctive explanations. Her work bridges philosophy with cognitive science and social epistemology. Awards: Rosalind Franklin Fellow (2015–present). Grants & Labs: Endowed chair in Societal Trust (KHMW/Agricola School), leading interdisciplinary research on societal cohesion and public trust in science. Active in media engagement, including discussions on climate skepticism and science education.

Daniël Lakensمشاهده پروفایل

دانشیار

• Meta-Science
• Research Methods
• Applied Statistics
• +۱۰ مورد دیگر

Daniël Lakens is an Associate Professor in the Human Technology Interaction group at Eindhoven University of Technology (TU/e), with expertise in meta-science, research methods, and applied statistics. His empirical research focuses on conceptual thought, similarity, meaning, study design, and reward structures in science. He has contributed to replication research and open science initiatives. Academic Background: PhD in Experimental Psychology (2010) from Utrecht University Grants: NWO VIDI grant (2017), pilot project for replication research grants Teaching: MOOC on 'Improving Your Statistical Inferences', TU/e Teacher of the Year (2014), over 40 workshops on open science His research spans methodologies to optimize study structures, meta-statistics, and interdisciplinary collaborations. Recent publications include datasets and interventions in esports, behavioral modeling, and methodological frameworks. Scientific Awards: Ammodo Science Award for Fundamental Research (2023) Leamer-Rosenthal Prize for Open Social Science (2017) NWO Vidi Award (2017) Datasets and projects highlight reproducibility in psychology, computational models for goal-directed behavior, and open data practices. He serves on editorial boards and has supervised 23 students, with current courses in advanced research methods and ethics.