جستجوی استاد راهنمای دانشگاهی

Ivana Nikoloskaمشاهده پروفایل

استادیار

• Machine Learning
• Quantum Computing
• Communication Engineering
• +۷ مورد دیگر

Ivana Nikoloska is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She is affiliated with the Center for Quantum Materials and Technology Eindhoven and BIASlab (Bayesian Intelligence and Stochastic Agents Lab). Her academic career includes prior roles as a Research Associate at King’s College London and a Visiting Researcher at Aalborg University. PhD: Monash University, Australia (2023) MSc & Dipl.-Ing.: University of Ss. Cyril and Methodius, North Macedonia Research Interests span foundational and applied machine learning, quantum computing, and information/communication engineering. Her work focuses on integrating Bayesian inference, variational methods, and quantum technologies for tasks like signal processing, channel estimation, and power control optimization. Quantum Machine Learning Bayesian Simulation-Based Inference Meta-learning for Wireless Systems Hybrid Quantum-Classical Architectures Stochastic Signal Processing Quantum Sensing & Metrology Notable Trends in Publications include quantum recurrent neural networks with adaptive gating, Bayesian frameworks for quantum sensing, and meta-learning applications in communication systems. She explores variational inference for planning and robust algorithms for channel estimation under non-ideal conditions.

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.

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.

Bart van Erpمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Bart van Erp serves as a University Researcher within the Department of Electrical Engineering at Eindhoven University of Technology, actively contributing to the Signal Processing Systems (SPS) group and Bayesian Autonomous Intelligent Systems lab (BIASlab). He completed his PhD in June 2025 under Prof. Bert de Vries with dissertation work on automated Bayesian hearing aid design. His academic foundation includes: Bachelor of Science in Electrical Engineering, cum laude (2015-2018) Master of Science in Electrical Engineering, cum laude with honors (2018-2020), supported by ALSP scholarship Van Erp's research centers on Bayesian inference frameworks for real-world signal processing challenges, particularly in hearing aid personalization and autonomous systems. He pioneers message-passing algorithms on factor graphs to implement variational free energy minimization, enabling adaptive user preference learning and energy-efficient control systems. His work bridges theoretical probabilistic modeling with clinical applications in hearing impairment solutions. Analysis of his 2024-2025 publications reveals consistent focus on Bayesian neural networks and active inference methodologies, with increasing emphasis on practical implementations for assistive devices. Key trends include Dirichlet process applications for context-aware systems and depth estimation improvements in uncertainty-aware computer vision. Recognition includes: ALSP scholarship for Master's excellence As project member in the CoHear initiative (2017-2025), he contributed to collaborative hearing device design while securing open-source development through ReactiveMP.jl. Though newly minted as a PhD, he has not yet supervised graduate students but actively supports university education (0.3 FTE during studies). His dual role as Product Lead/Co-Founder at Lazy Dynamics BV since May 2023 demonstrates translational research impact. Within BIASlab, van Erp drives the integration of probabilistic programming with medical device innovation, maintaining strong industry partnerships in hearing technology through GN Hearing collaborations and open-source tooling.

Clemens Verhooselمشاهده پروفایل

دانشیار

• Computational Mechanics
• Numerical Methods
• Solid Mechanics
• +۵ مورد دیگر

Clemens Verhoosel is an Associate Professor in Computational Methods for Model- and Data-Driven Engineering at Eindhoven University of Technology (TU/e), specializing in the Department of Mechanical Engineering. His research focuses on numerical methods for engineering applications, including isogeometric analysis, immersed methods for complex geometries, and uncertainty quantification. He holds a prestigious NWO Veni Grant (2011) and has contributed to open-source tools like the Nutils toolkit. Education: MSc (with honors) in Aerospace Engineering from TU Delft (2005) PhD in Computational Mechanics from TU Delft (2009, cum laude) Postdoctoral research at the University of Texas at Austin (2009-2010) Research Interests: Scan-based immersed isogeometric analysis Uncertainty quantification and Bayesian inference Fluid-structure interaction Multiphase flow and fracture mechanics Teaching: Courses include Advanced Discretization Techniques (Isogeometric Analysis) and Scientific Computing for Mechanical Engineering. Awards: NWO Veni Award (2011): "Efficient modelling of bone fractures" Labs/Teams: Leads the Group Verhoosel and contributes to EAISI Foundational and Energy Technology research groups.

Wouter Kouwمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Variational Bayesian Inference
• Active Inference
• +۶ مورد دیگر

Wouter Kouw is an Assistant Professor at the Electrical Engineering department of Eindhoven University of Technology (TU/e) , leading the Bayesian Intelligent Autonomous Systems lab. With a dual PhD in Computer Science (2018, TU Delft) and MSc in Neuroscience (2013, Maastricht University) , he bridges neurobiology and artificial intelligence through variational Bayesian inference and active inference frameworks. Research Focus : Probabilistic machine learning systems using message passing algorithms on factor graphs , applied to mobile robotics and adaptive control Key Projects : CONTACT-AI (contact-rich robot navigation), FEP-walker (active inference-based locomotion), and BayesBrain (hybrid neuro-in-silico computing) His work spans nonlinear system identification , uncertainty quantification , and sensor modeling , with recent publications in IEEE Transactions , Entropy , and Communications in Computer and Information Science . Awards include the Niels Stensen Fellowship (2017) and TU/e Team Science Award nomination (2023) . Collaborations extend to institutions in Germany, USA, and Denmark, with teaching responsibilities in Bayesian Machine Learning and Neuro Computation .

Thijs W. van de Laarمشاهده پروفایل

استادیار

• Probabilistic Programming
• Active Inference
• Bayesian Signal Processing
• +۴ مورد دیگر

Thijs W. van de Laar is an Assistant Professor at the Eindhoven University of Technology in the Department of Electrical Engineering , affiliated with the Bayesian Intelligent Autonomous Systems lab (BIASlab) and the EAISI Foundational and High Tech Systems groups. His research focuses on intelligent systems for decision-making under dynamic conditions, particularly through scalable probabilistic programming implementations of Active Inference. Academic Background : PhD (2019, TU/e) in automated Bayesian signal processing algorithm design; MSc in natural sciences (biophysics, science communication, Radboud University). Van de Laar's research integrates probabilistic programming with active inference to develop autonomous agents capable of real-time adaptation. Key methodologies include variational message passing , factor graph representations , and free energy minimization . His recent publications examine active inference formalisms, factor graph applications in AI, and uncertainty reduction strategies. Collaborative work spans hearing aid technology, autonomous navigation, and synthetic intelligence development.

James Townsendمشاهده پروفایل

پژوهشگر

• deep generative models
• lossless compression
• unsupervised learning
• +۴ مورد دیگر

James Townsend, also known as Jamie, is a machine learning researcher at the Amsterdam Machine Learning Lab (AMLab) within the Informatics Institute at the University of Amsterdam. He completed his PhD in 2020 at the UCL AI Centre in London under the supervision of Professor David Barber. His educational background includes a PhD in lossless compression with latent variable models from University College London, with prior research contributions to the Autograd library and early development of JAX during a Google Brain internship in 2018. Townsend's research centers on deep generative models and lossless compression, extending to unsupervised learning, approximate inference, Monte Carlo methods, optimization, and machine learning software systems. His work bridges theoretical information theory with practical implementation, particularly in neural compression techniques. He has significantly contributed to open-source tools including Autograd and JAX, demonstrating expertise in automatic differentiation systems. His publication record spans high-impact venues like NeurIPS, ICLR, and ICML, with recent work focusing on innovative compression paradigms for complex data structures including graphs and multisets. Key contributions include shuffle coding, reversible programming for compression verification, and multiset compression techniques that challenge conventional approaches. Scientific recognition includes: Best Paper Award at Deep Generative Models and Downstream Applications Workshop (2021) Townsend actively participates in the research community through invited talks at Stanford's Information Theory Forum and the Languages for Inference workshop. His collaborations span academic institutions and industry partners like Google Brain, with current work centered on advancing lossless compression through deep learning at the AMLab. He maintains an active open-source presence via GitHub (@j-towns) and technical discourse on Twitter (@_j_towns), while publishing through Google Scholar under his formal name James Townsend.

Hamdi Joudehمشاهده پروفایل

دانشیار

• Information Theory
• Communications
• Signal Processing
• +۳ مورد دیگر

Hamdi Joudeh is an Associate Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He is affiliated with the Information and Communication Theory (ICT) Lab and the Signal Processing Systems (SPS) Group. His research focuses on information theory, communications, and signal processing, with applications in wireless networks and radar systems. He holds a Ph.D. in Electrical Engineering from Imperial College London and has held research positions at Technische Universität Berlin and Imperial College London. His research interests include quantum sensing, error exponents, MIMO systems, and channel coding. He leads projects such as the IT-JCAS (Information Theoretic Foundations of Joint Communication and Sensing) and ANTERRA (Beam Prediction for Fast-Moving LEO), addressing challenges in 5G/6G communication and radar technologies. He has received an ERC Starting Grant (2023) for his work on environment-scanning mobile networks. Education: Ph.D. in Electrical Engineering (Imperial College London), M.Sc. in Communications and Signal Processing (Imperial College London) Editorial Roles: Editorial board member of IEEE Transactions on Signal Processing , IEEE Communications Letters , and EURASIP Journal on Wireless Communications and Networking Labs/Teams: ICT Lab, SPS Group, and leads projects at TU/e’s Center for Wireless Technology Grants: ERC Starting Grant, TKI-HTSM/22.0547/TKI2212P11 RAIDAR, and others His recent work explores the intersection of communication and sensing, including quantum radar processing and robust beamforming techniques. He has published extensively on topics like error exponents, MIMO channel analysis, and interference management, with over 40 peer-reviewed articles.

• Medical Statistics
• Computer Science
• Artificial Intelligence
• +۶ مورد دیگر

Gerton Lunter is a Professor of Medical Statistics at the University of Groningen's Faculty of Medical Sciences, affiliated with the Department of Epidemiology. He holds a visiting professorship in Computational Biology and Artificial Intelligence at the Weatherall Institute of Molecular Medicine, University of Oxford. His expertise spans Bayesian statistics, statistical genetics, functional genomics, and machine learning applied to evolutionary biology and healthcare challenges. Education: PhD in Mathematics (Dynamical Systems), University of Groningen (1999) MSc Mathematics and Theoretical Physics (cum laude), University of Groningen (1994) Research Focus: Integrates computational methods with medical and genetic data to address complex biological problems. Key areas include: Statistical modeling of genetic variation Machine learning for healthcare diagnostics Evolutionary dynamics of genomic mutations Multi-morbidity and lifestyle epidemiology Recent Work Trends: Publications emphasize interdisciplinary approaches, combining genomic data analysis with clinical applications (e.g., cancer immunotherapy prediction, maternal hypertension patterns, and rare disease diagnostics). His work bridges computational innovation with real-world medical challenges. Affiliations & Roles: Current roles include group leadership at Genomics plc and former positions as a postdoctoral researcher at University of Oxford's Department of Statistics and Philips Research Laboratories. He has contributed to initiatives like the AIHACKCOVID challenge. Team & Infrastructure: Leads research groups focusing on computational biology and statistical learning, collaborating across disciplines to leverage large-scale genomic datasets.

Julia Engelmannمشاهده پروفایل

پژوهشگر

• Causal network modeling
• Metagenomics
• Marine microbial ecology
• +۵ مورد دیگر

Dr. Julia Engelmann is a Tenure-track Scientist in the Marine Microbiology and Biogeochemistry (MMB) department at the Royal Netherlands Institute for Sea Research (NIOZ) , specializing in causal network modeling, metagenomics, and bioinformatics. Her research focuses on understanding marine microbial interactions through high-throughput data analysis and computational models, particularly in the North Sea, Wadden Sea, and Antarctic waters, with implications for climate change studies. Education: PhD in Bioinformatics (2008) and Diploma in Biology (2004) from German universities. Academic Positions: Senior Scientist at NIOZ (2017-present), Assistant Professor (2013-2017), and Postdoctoral roles in computational diagnostics and oncology. Engelmann applies statistical methods to infer causal relationships between marine microorganisms from observational data, using Bayesian networks and conditional independence analysis. Her work bridges computational biology with experimental validation, aiming to design synthetic microbial consortia for applications like plastic degradation. She contributes to meta-barcoding and metagenomic analyses , developing pipelines like Cascabel for reproducible sequence data processing. Her recent publications examine methane dynamics, microbial lipid biosynthesis, and plastisphere diversity in coastal and polar systems. Scientific Awards NWO Women in Science Excel (WISE) tenure track award Engelmann's research integrates environmental factors (temperature, CO2) into microbial interaction models to predict climate change impacts, collaborating with international teams on Antarctic expeditions.

Thijs van de Laarمشاهده پروفایل

استادیار

• Bayesian Machine Learning
• Active Inference
• Probabilistic Programming
• +۳ مورد دیگر

Thijs van de Laar is an Assistant Professor at the BIASlab (Bayesian Intelligence and Autonomous Systems Laboratory) within the Department of Electrical Engineering at Eindhoven University of Technology. His research bridges advanced computational principles with real-world applications in uncertainty modeling. Education: PhD in Machine Learning (2019), Eindhoven University of Technology MSc in Natural Sciences (2010), Radboud University Nijmegen Research Focus: Bayesian machine learning, active inference, and probabilistic programming, with applications in autonomous agent control, hearing loss compensation, and efficient real-time decision-making systems. His work integrates insights from physics and neuroscience to develop novel computational frameworks. Software Contributions: Key contributor to ForneyLab.jl, a Julia toolbox for automated Bayesian inference through message passing on factor graphs. Also developed Herring.jl, a Poisson node extension for probabilistic modeling. Publications: His 15 most recent articles focus on variational message passing, active inference agents, epistemic value in graphical models, and factor graph-based algorithm design. These works span disciplines including artificial intelligence, control theory, and biomedical engineering.

Quentin Changeatمشاهده پروفایل

استادیار

• Exoplanets
• Atmospheric Modeling
• Telescope Observations
• +۳ مورد دیگر

Quentin Changeat is an Assistant Professor at the Kapteyn Astronomical Institute, University of Groningen, and an Honorary Research Fellow in the Department of Physics and Astronomy at University College London. His research bridges exoplanet atmospheric studies and computational astrophysics, with affiliations spanning multiple international institutions. Research Interests: Dr. Changeat focuses on the physics and chemistry of exoplanet atmospheres, leveraging data from the James Webb Space Telescope (JWST), Hubble Space Telescope (HST), and Spitzer. His work emphasizes atmospheric retrieval techniques, machine learning applications, and the development of open-source tools like TauREx for spectral analysis. He is actively involved in the ESA Ariel mission, which aims to survey 1000 exoplanets by 2029. Publication Trends: His 15 most recent articles (2019–2024) reveal a strong emphasis on JWST/HST data analysis, atmospheric retrievals of hot Jupiters, and population-level studies. Key themes include thermal inversions, phase-curve degeneracies, and the development of statistical frameworks for large-scale exoplanet characterization. Machine learning and interdisciplinary collaborations feature prominently in his methodology. Awards: Honorary Research Fellow, University College London Academic Contributions: He leads educational initiatives like the ARES Summer School and ORBYTS program, mentors PhD students in exoplanet research, and develops datasets for community use (e.g., Ariel Data Challenge). His lab focuses on atmospheric retrieval algorithms and collaborates with teams at UCL, Flatiron Institute, and ESA.

• Algorithms
• Planning
• Constraints
• +۷ مورد دیگر

Mathijs de Weerdt is a Full Professor at Delft University of Technology, leading the Algorithmics Group within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on developing advanced algorithms for planning and scheduling under uncertainty, with applications in energy systems, railway logistics, satellite operations, and agricultural supply chains. He bridges fundamental AI research with practical implementations through collaborations with Dutch National Railways (NS), Shell Recharge, and industry partners. Education: PhD in Multi-Agent Plan Merging (2003), MSc in Computer Science (Utrecht University, cum laude) Research Interests: Robustness in AI, Scalability of Optimization Algorithms, and Multi-Party Coordination His work integrates Stochastic Programming , Reinforcement Learning , and Constraint Programming to address challenges in energy transition and transportation. Recent 15 most recent publications emphasize surrogate modeling for EV charging optimization, multi-agent pathfinding in railways, and dynamic programming for decision trees. Scientific Awards: Recipient of the Erasmus Energy Forum Science Award (2016), Best Teacher Award in Delft Computer Science (2015), and honorable mentions for dissertation and paper awards. As a promotor , he guides over 20 PhD candidates in projects spanning smart grid algorithms, train unit shunting, and strawberry supply chain optimization. He leads large-scale initiatives like the NWO ESI-FAR project and co-chairs the Dutch AI Coalition's Energy & Sustainability working group.

• Information Theory
• Machine Learning
• Deep Neural Networks
• +۷ مورد دیگر

Shujian Yu is an Assistant Professor at the Department of Artificial Intelligence , part of the Faculty of Science at Vrije Universiteit Amsterdam. He is also affiliated with the Network Institute . His research focuses on Information Theory , Machine Learning , and Deep Neural Networks , with applications in causal discovery, brain network analysis, and generalization bounds. Key research interests include: Information-Theoretic Methods for ML interpretability and robustness Transfer entropy and causal inference in complex systems Feature selection and dimensionality reduction techniques Brain network-based psychiatric diagnosis (e.g., schizophrenia analysis) He teaches courses on Data Mining Techniques , Deep Learning , and Introduction to Reinforcement Learning . Recent work explores hierarchical state space models, Cauchy-Schwarz divergence applications, and Granger causality in chemical processes. His collaborations span interdisciplinary fields including neuroscience and industrial engineering. Publications emphasize theoretical foundations while addressing practical challenges in ML generalization, adversarial robustness, and sequential decision-making.