جستجوی استاد راهنمای دانشگاهی

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.

• Gravitational Wave Astronomy
• Neutron Star Physics
• Multimessenger Astrophysics
• +۴ مورد دیگر

Peter T.H. Pang is a Researcher in the Gravitational and Subatomic Physics (GRASP) group within the Physics Department at the Faculty of Science, Utrecht University. His work focuses on gravitational wave astronomy, neutron star physics, and multimessenger astrophysics, contributing significantly to the LIGO-Virgo-KAGRA scientific collaborations. Dr. Pang's research interests span gravitational wave data analysis, neutron star equation of state, multimessenger astronomy, nuclear physics constraints from gravitational wave observations, and gravitational wave detector characterization. His work often involves developing and applying Bayesian frameworks to extract physical information from gravitational wave signals, particularly related to binary neutron star mergers and their electromagnetic counterparts. Analysis of his recent publications reveals a strong emphasis on connecting gravitational wave observations with nuclear physics through the study of neutron star properties. His research frequently addresses the equation of state of dense matter, parameter estimation techniques for gravitational wave signals, and the development of frameworks that combine nuclear physics with multimessenger astrophysical observations. His work contributes to understanding compact object mergers, gravitational wave detector performance, and cosmological measurements using gravitational waves as standard sirens. Dr. Pang is an active contributor to major gravitational wave collaborations, particularly the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration. His research demonstrates strong interdisciplinary connections between nuclear physics, astrophysics, and gravitational wave astronomy, with numerous publications in high-impact journals including Physical Review D, Physical Review X, Nature Communications, and Astrophysical Journal.

• Toxicology
• In Vitro Toxicology
• Neurotoxicology
• +۷ مورد دیگر

Dr. Emma Kasteel is an Assistant Professor at the Institute for Risk Assessment Sciences (IRAS) within the Faculty of Veterinary Medicine at Utrecht University. Her work focuses on toxicology, particularly neurotoxicology of micro- and nanoplastics and alternative methods to animal testing. Dr. Kasteel completed her PhD in 2021 on Next Generation Risk Assessment of Chemicals with a focus on alternative (in vitro/in silico) methods for toxicity testing. Her educational background includes a Master of Science in Drug Discovery and Safety (cum laude) from VU Amsterdam (2014-2016) and a Bachelor of Science in Pharmaceutical Sciences from VU Amsterdam (2011-2014). Her research primarily centers on neurotoxicology, microplastics, and risk assessment of chemicals. She is currently investigating whether micro- and nanoplastics can cross the blood-brain barrier and cause potential neurotoxic effects as part of the MOMENTUM project. Dr. Kasteel is also involved in the INTACT project, which focuses on integrated in vitro toxicokinetics and toxicodynamics to advance multi-organ toxicity testing. Her publication record demonstrates strong expertise in applying in vitro data in physiologically-based kinetic models, refining neurotoxicity approaches by accounting for interspecies and interindividual differences, and developing next-generation risk assessment methods. Her work frequently employs Bayesian meta-analysis to address human variability in toxicokinetic and toxicodynamic processes. Dr. Kasteel has received recognition for her work, including the 2022 Joep van den Bercken award for best PhD thesis in toxicology in 2021. She is actively involved in teaching, particularly in courses related to toxicology, food, pesticides, and drugs. She also shares her expertise through media appearances discussing microplastics risks and advances in animal-free toxicity testing.

Francisco Blasquesمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Data Science
• +۵ مورد دیگر

Francisco Blasques is a Full Professor of Econometrics and Data Science at the Vrije Universiteit Amsterdam (VU) and a Research Fellow at the Tinbergen Institute. He holds leadership roles, including Co-founder of Churned (QuantIQ) and datastuff BV, and served as TI Doctoral Director for the School of Business and Economics. His research focuses on econometrics, statistics, and data science, with expertise in time-series analysis, financial econometrics, and nonlinear dynamics. Teaching includes advanced econometrics courses at VU and Tinbergen Institute, covering topics like time-series modeling and financial econometrics. His PhD supervision spans over 15 students since 2012, focusing on econometric methods and applications. Research contributions emphasize score-driven models, volatility analysis, and spatial econometrics. Key publications address topics such as natural disaster impacts on economies, robust volatility estimation, and fusion of experimental/observational data. Awards include the NWO-VIDI Grant (2019) for methodological innovations in econometrics. Professional activities include ancillary roles as Partner at QuantiQ BV and Director at Metyis, alongside academic duties. His work contributes to UN Sustainable Development Goals through economic resilience and policy analysis.

• Control Systems
• Mechatronics
• Machine Learning
• +۷ مورد دیگر

T.A.E. Oomen is a Professor in the Department of Mechanical Engineering, actively engaged in research related to control systems and mechatronics. His academic contributions span interdisciplinary domains, integrating theoretical and applied approaches. Research Interests: His work focuses on Control Systems Mechatronics Machine Learning Dynamic Systems Noise Optimization Motion Control . Key themes include predictive repetitive control, inverse system identification, and the integration of neural networks in mechatronic applications. Recent Publications analyze challenges in precision engineering (e.g., laser frequency control in astrophysics, interventional X-ray imaging) and methodological advancements in sampled-data control and Bayesian modeling techniques. These works emphasize performance optimization through data-driven strategies. Collaborative Affiliation: He is associated with the team led by Jan-Willem van Wingerden , contributing to cutting-edge research in control systems and multidisciplinary engineering problems.

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 .

Jelmer Borstمشاهده پروفایل

دانشیار

• Computational Cognitive Neuroscience
• Neuroimaging Analysis
• Cognitive Modeling
• +۵ مورد دیگر

Prof. Jelmer Borst is an Associate Professor in Computational Cognitive Neuroscience at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence department within the Bernoulli Institute. His research focuses on integrating computational models with neuroimaging data to understand cognitive processes like multitasking, working memory, and decision-making. He advises three PhD candidates and collaborates internationally on projects involving EEG/fMRI analysis and cognitive modeling frameworks such as ACT-R and Nengo. Research Interests: Neuroimaging analysis methods, cognitive bottlenecks in multitasking, memory retrieval localization, and adaptive learning systems. Key Projects: Developed the PREDICTOR tool for semi-automated driving response timing, and advanced models linking symbolic process stages to brain activity via MEG/EEG. Recent work includes large-scale evaluations of adaptive learning systems and interventions to mitigate mind-wandering in driving scenarios. He has received the Allen Newell Best Student-led Paper Award (2021) for contributions to cold-start adaptive learning research. His research group maintains active collaborations on datasets involving working memory, decision-making, and cognitive architecture validation through neuroimaging experiments. He also contributes to open-source tools for cognitive modeling and neuroimaging analysis.

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.

M.B. Eichlerمشاهده پروفایل

دانشیار

• Time Series Analysis
• Econometrics
• Causal Inference
• +۳ مورد دیگر

Dr. M.B. Eichler is an Associate Professor of Econometrics at the QE Econometrics Department within the School of Business and Economics at Maastricht University. His research focuses on advancing methodologies for causal inference in time series analysis, particularly addressing limitations in Granger causality and developing algorithms for multivariate time series with latent variables. He collaborates with institutions like Raytheon BBN Technologies on projects involving heterogeneous data sources for predicting rare events. His expertise spans econometrics, functional time series analysis, and dynamic factor models. Notable projects include electricity spot price modeling, semi-parametric approaches for non-stationary processes, and applications in neuroscience and energy economics. He advises PhD students such as Carlos A. Moreno, Dennis Tuerk, and Anne van Delft. Research Themes: Causal inference, Granger causality, time series econometrics, dynamic factor models Labs/Teams: QE Econometrics Research Group Grants: Open Source Indicator (OSI) Program funded by IARPA Publications highlight contributions to spectral analysis, graphical modeling of time series, and applications in energy markets. His work bridges theoretical advancements with practical challenges in economics, neuroscience, and engineering.

• Advanced Feedforward Control
• Learning Control
• Repetitive Control
• +۴ مورد دیگر

Lennart L.G. Blanken is a part-time Assistant Professor in the Control Systems Technology group at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e). He also works at Sioux Technologies in the Mechatronics department since 2019, focusing on projects in the semiconductor and printing industries. His research emphasizes advanced control strategies including feedforward control, learning control, repetitive control, and system identification, with applications in mechatronic systems. Academically, he earned his MSc (cum laude) in Mechanical Engineering from TU/e in 2015 and his PhD in 2019, both under the supervision of Oomen, with a thesis on learning and repetitive control for complex systems applied to large format printers. His work has been published in journals like Meathctronics and Mechanical Systems and Signal Processing , focusing on frequency-domain methods, iterative learning control, and multirate systems. Key collaborations include projects with Philips Innovation Services, Océ Technologies (Canon Production Printing), and international researchers. His contributions span theoretical control development and industrial applications, with a focus on enhancing system performance through innovative control methodologies.

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.

Duarte J. Guerreiro Tomé Antunesمشاهده پروفایل

دانشیار

• Control Theory
• Optimal Control
• Stochastic Control
• +۷ مورد دیگر

Duarte J. Guerreiro Tomé Antunes is an Associate Professor in the Mechanical Engineering Department at Eindhoven University of Technology (TU/e), affiliated with the Control Systems Technology Group and the EAISI Foundational initiative. He holds a PhD in Automatic Control from Instituto Superior Técnico (IST), Lisbon, and has postdoctoral experience at TU/e's Hybrid and Networked Systems group. Education: Licenciatura in Electrical and Computer Engineering (IST, 2005) PhD in Automatic Control (IST, 2011) Research Focus: Specializes in optimal control, stochastic control, and networked control systems. His work addresses challenges in large-scale system optimization (e.g., drone swarms, smart grids) and event-triggered control strategies for cloud-connected systems. Recent interests include robotics, particularly quadcopter dynamics. Key Contributions: Pioneered methods for reducing the 'curse of dimensionality' in high-dimensional control systems. Developed event-triggered algorithms for energy-efficient cloud-based control and stability analysis frameworks for networked systems with data losses. Awards: IEEE Control Systems Letters Outstanding Paper Award (2019) Teaching & Supervision: Teaches optimal control, dynamic programming, and robotics courses. Supervised 47 academic works to date. Active in TU/e's Cyber-Physical Systems and Systems Engineering research area.

• Statistical Genetics
• Graphical Models
• Genomics
• +۵ مورد دیگر

Pariya Behrouzi is an Assistant Professor in the Department of Mathematical and Statistical Methods - Biometris at Wageningen University & Research. She specializes in developing statistical methodologies with applications in genetics, genomics, and nutrition science. Her research focuses on graphical models, copula theory, and Bayesian approaches to analyze complex biological systems. Behrouzi leads or collaborates on projects such as SignAphy (plant signaling dynamics) and Sensing Age (epigenetic aging studies). She co-developed the netgwas R package for network-based genome-wide association studies. Her work bridges statistical theory with practical applications in agriculture, nutrition, and computational biology. Education: PhD in Statistics (as indicated by title) Key Research Tools: Copula graphical models, spatial statistics, and Bayesian networks Current Projects: Multi-omics network reconstruction, resilience in lettuce crops, and microbiota systems analysis Her recent publications explore topics like meat analogue texture optimization, telomere length dietary correlations, and plant disease resistance mechanisms. Behrouzi advises four PhD candidates and collaborates with international teams on interdisciplinary research.

Paul M.J. Van den Hofمشاهده پروفایل

استاد

• Data-driven modeling
• Control systems
• Dynamic networks
• +۶ مورد دیگر

Paul M.J. Van den Hof is a Full Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e), specializing in data-driven modeling and control of dynamic systems. He leads the Control Systems Group and is affiliated with multiple research centers, including EAISI High Tech Systems, Mobility, and Foundational, as well as the EIRES Research initiative. Research Interests: His work focuses on developing fundamental techniques for dynamic network modeling, closed-loop identification, data analytics, experimental design, and model-based control. Applications span industrial process control, oil reservoir engineering, high-tech mechatronic systems, and cyber-physical systems. Recent projects include SYSDYNET (dynamic network identification), NAPAS (nanometer-accurate actuation), and CADUSY (data-driven modeling using symbolic methods). Scientific Contributions: Van den Hof has authored over 250 research outputs, including foundational work on identifiability in dynamic networks, frequency domain identification, and distributed control algorithms. His 2024-2025 publications address challenges in fault detection, Bayesian network topology identification, and multi-step least squares methods. He emphasizes practical implementation through tools like the SYSDYNET MATLAB toolbox. ERC Advanced Research Grant (2015) Honorary Member, Hungarian Academy of Sciences (2016) IEEE Fellow (2007) and IEEE Life Fellow (2023) IFAC Advisor (2020)

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.