جست‌وجوی استاد و پژوهشگر

C. Garcia Sanchezمشاهده پروفایل

پژوهشگر

• Urban Data Science
• Computational Fluid Dynamics
• Building Energy Modeling
• +۴ مورد دیگر

C. Garcia Sanchez is a researcher at Delft University of Technology within the Architecture and the Built Environment school, specializing in Urban Data Science. Their work bridges computational fluid dynamics, urban microclimate modeling, and building energy systems through advanced data-driven approaches. Education includes a PhD in Physics from von Karman Institute for Fluid Dynamics and Antwerp University (2017), a Research Master in Fluid Dynamics from von Karman Institute (2012), and an Aerospace Engineering degree from Universitat Politécnica de Valencia (2011). Postdoctoral research was conducted at Carnegie Institution for Science. Research focuses on urban data science with emphasis on High-fidelity computational fluid dynamics for urban environments Synthetic data generation for turbulence modeling Integration of building energy models with urban canopy systems Automated 3D building reconstruction for microscale simulations Recent publications demonstrate expertise in creating efficient simulation workflows that address urban ventilation, climate resilience, and energy efficiency challenges. Awarded the Outstanding Paper Award for 3D Processing & Visualization (2021) for contributions to urban simulation techniques. Research outputs include 19 publications, 3 datasets, and media coverage of digital twin applications for heatwave response. Active in supervising research projects and developing computational tools for urban science, with recent work featured in Computers and Fluids, SoftwareX, and Building and Environment. Current projects involve creating high-resolution urban models that integrate meteorological data with human behavior simulations during extreme weather events.

• Enhancer Evolution
• Computational Genomics
• Functional Genomics
• +۵ مورد دیگر

Dr. Severin Uebbing is an Assistant Professor at Utrecht University 's Faculty of Science , affiliated with the Genome Biology and Epigenetics division. His research focuses on enhancer evolution and gene regulation in mammals, integrating computational genomics and functional assays using induced pluripotent stem cells (iPSCs) . Education: PhD in Evolutionary Genetics (2011-2015), Uppsala University MSc in Evolutionary Biology (2009-2011), Uppsala University BSc in Biology (2005-2009), Heidelberg University Research Trends: Analysis of 15 recent publications reveals expertise in enhancer evolution , neurodevelopmental gene regulation , 3D genomic architecture , dosage compensation , and systems biology approaches to disease mechanisms. Key methods include massively parallel assays , humanized mouse models , and comparative genomics across mammals and birds. Laboratory: Leads research on enhancer evolution at Utrecht University, employing iPSC-derived models and genome-scale functional assays to investigate mammalian regulatory innovation.

• Artificial Intelligence
• Natural Language Processing
• Multi-Agent Systems
• +۵ مورد دیگر

Dr. Gerard Vreeswijk is an Assistant Professor in the Department of Information and Computing Sciences within the Faculty of Science at Utrecht University. He specializes in Natural Language Processing and Artificial Intelligence, with over 100 publications spanning multiple decades of research. His academic journey began with a PhD in theoretical computer science from the Vrije Universiteit Amsterdam in 1993. Dr. Vreeswijk's research interests center around multi-agent learning , the theory of computability , and unconventional computation . His scholarly work demonstrates a strong focus on argumentation systems, multi-agent deliberation, and computational dialectics. He serves as a core lecturer for both bachelor's and master's programs in Artificial Intelligence at Utrecht University. His publication record shows consistent contributions to the fields of artificial intelligence and computational argumentation, with recent work examining novelty strategies in game theory, motion types in particle systems, and extensions to explanatory coherence frameworks. His research has appeared in prestigious venues including Theoretical Computer Science, International Journal of Parallel, Emergent and Distributed Systems, and Law, Probability and Risk. Dr. Vreeswijk regularly contributes to the academic community through peer review activities for leading scientific journals and as an arbitrator for the Dutch Research Council (NWO) and European Research Council (ERC). His work intersects with multiple disciplines, particularly bridging theoretical computer science with practical applications in intelligence analysis and decision support systems. He maintains an active role in teaching and research supervision, contributing to the development of the next generation of AI researchers and practitioners at Utrecht University.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +۳ مورد دیگر

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.

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.

• Electromagnetic Modeling
• Multi-Physics Modeling
• Computational Electromagnetics
• +۳ مورد دیگر

Stefan Eijsvogel is a Postdoc researcher at the Electromagnetic and MultiPhysics Modeling and Computation (EMPMC) Lab within the Department of Electrical Engineering at Eindhoven University of Technology. He holds a Bachelor's and Master's degree in Electrical Engineering from the same institution, with his Master's thesis focusing on wireless power transfer for medical applications and nanosatellite clock synchronization. From 2019 to 2023, he completed his Ph.D., extending the spatial spectral Maxwell solver for soft x-ray wafer metrology applications. His research interests include Gabor frame expansions, parallel computing in multi-layered media, and noise-robust inverse-scattering algorithms. He is a core member of the Sub-wavelength resolution Lensless Imaging Algorithms and Tests project (2018–2024), contributing to advanced imaging techniques. His work bridges computational electromagnetics and optics, with applications in high-resolution metrology and scattering analysis. Notable contributions include developing efficient algorithms for Gabor coefficients and parallel 3D electromagnetic scattering methods. Stefan has published extensively in peer-reviewed journals and conferences, with recent works focusing on phaseless inverse scattering and parametrized volume integral equations. His research emphasizes both theoretical advancements and practical implementations in soft x-ray regimes.

Dragan Bosnackiمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Computational Biology
• +۶ مورد دیگر

Dragan Bosnacki is an Assistant Professor at Eindhoven University of Technology (TU/e), holding joint appointments in the departments of Biomedical Engineering and Mathematics and Computer Science. He specializes in interdisciplinary research at the intersection of computer science and biomedicine, with a focus on machine learning, bioinformatics, and computational modeling of medical therapies. His work combines formal verification techniques, GPU-accelerated algorithms, and biomedical applications such as High Intensity Focused Ultrasound (HIFU) therapy modeling. Dr. Bosnacki earned his BSc in Electrical Engineering and MSc in Computer Science from Sts. Cyril and Methodius University (Macedonia), followed by a PhD in Mathematics and Computer Science at TU/e under Professors Jos Baeten and Dennis Dams. He has pioneered advancements in parallel model checking and contributed significantly to the SPIN model checking tool. His research also spans separation logic, biomedical data analytics, and the application of mathematical methods to biological systems. Education: BSc in Electrical Engineering, Sts. Cyril and Methodius University (Macedonia) MSc in Computer Science, Sts. Cyril and Methodius University (Macedonia) PhD in Mathematics and Computer Science, TU/e (2001) Research Interests: Mechanisms of machine learning for biomedical data Formal verification of hardware/software systems GPU-optimized algorithms for computational biology Mathematical modeling of HIFU cancer therapies Reconstruction of biological networks and drug response modeling Teaching: Advanced programming and biomedical data analysis Simulation of biochemical systems Programming for data analytics Capita selecta in bioinformatics Thermodynamics & chemical kinetics Key Contributions: Co-developed GPU-based model checking tools (e.g., GPUexplore) Improved partial-order and symmetry reduction techniques Contributions to the SPIN model checker and VeriFast tool Advising: Supervised 37 academic works, focusing on interdisciplinary projects in computational biology and formal methods.

• Numerical Analysis
• Differential Equations
• Geometric Integration
• +۵ مورد دیگر

Jason E. Frank is Professor of Numerical Analysis at Utrecht University, where he was appointed in 2013. He holds positions in the Faculty of Science and Mathematical Institute, specializing in Mathematical Modeling. Previously, he served as Head of the Department of Mathematics and Director of the Mathematical Institute at Utrecht University from 2018 to 2022. His academic journey includes a full professorship at the University of Amsterdam (2010-2013) and extensive research work at CWI, Amsterdam (2000-2013), where he led the Computational and Stochastic Dynamics research group. Frank earned his B.Sc. and M.Sc. degrees in aerospace engineering from the University of Kansas in 1992 and 1994, respectively, followed by a PhD in numerical analysis from Delft University of Technology and CWI in April 2000. His doctoral research focused on parallel computing methods for ordinary and partial differential equations. Professor Frank's research fundamentally addresses modeling, analysis, and computation of ordinary, partial, and stochastic differential equations. His work spans applications in atmosphere/ocean/climate dynamics, nonlinear waves, and complex systems. A significant portion of his research focuses on geometric integration and structure-preserving numerical methods, which maintain the intrinsic geometric properties of differential equations during numerical integration. His expertise extends to applied data science and foundations of complex systems, contributing to the Utrecht Platform for Applied Data Science. Analysis of Frank's publication record reveals a strong emphasis on numerical methods for differential equations, with particular focus on data assimilation techniques, geometric integration, and climate modeling. His work bridges theoretical numerical analysis with practical applications in climate science and fluid dynamics, showing consistent development from foundational numerical methods toward increasingly complex multiscale and stochastic systems. Frank has held significant leadership roles, including Head of the Department of Mathematics and Director of the Mathematical Institute at Utrecht University from 2018 to 2022. His research has been supported by various grants that enabled work on numerical methods for climate modeling, data assimilation, and complex dynamical systems. His work connects with the Computational and Stochastic Dynamics research group (MAC1) that he previously led at CWI, Amsterdam, and continues to influence research in mathematical modeling of complex systems at Utrecht University.

Eric Verbeekمشاهده پروفایل

پژوهشگر

• Process Mining
• Process Models
• Process Discovery
• +۳ مورد دیگر

H.M.W. (Eric) Verbeek is an ICT Developer in the Department of Mathematics and Computer Science at Eindhoven University of Technology, specializing in Process Analytics. His work focuses on advancing process mining techniques, standardization of event data (e.g., IEEE XES), and the development of tools like ProM and CPN IDE. He has contributed to over 160 publications since 1986, with recent research emphasizing process discovery algorithms, workflow net synthesis, and context-rich process modeling. Research Interests include Process Mining, Process Models, Process Discovery, Petri Net Analysis, and Workflow Management. He has actively participated in projects like the GDPR compliance toolkit and has been involved in defining standards for event data interoperability. Notable contributions include the IEEE XES standard for process mining, tools for event log generation and analysis, and methods for decomposed conformance checking. His work bridges theoretical advancements in process modeling with practical tool development, supporting both academia and industry applications. Collaborations span international academia, focusing on process mining workflows and compliance monitoring. His research trends highlight a balance between algorithmic innovation and real-world applicability in areas like healthcare, logistics, and service management.

• Computational Dynamics
• Circuit Engineering
• Electric Machine Optimization
• +۱ مورد دیگر

Idoia Cortes Garcia is an Assistant Professor in the Department of Mechanical Engineering at Eindhoven University of Technology. She holds a PhD from the Technical University of Darmstadt and previously served as a postdoctoral researcher there. Her academic background includes a bachelor's in mathematics and a master's in modeling for science and engineering from the Autonomous University of Barcelona. Her research focuses on computational dynamics, circuit engineering, and optimization techniques for electric systems. Key interests include parallel-in-time simulation, electromagnetic transients, and structural optimization of electromechanical devices. Recent publications emphasize isogeometric analysis, superconducting coil modeling, and circuit-aware learning systems, reflecting trends in high-performance computing and energy-efficient design. Her work integrates numerical methods with practical engineering challenges in electromagnetics and mechanics. She has advised multiple graduate students and leads projects in computational dynamics. Her lab collaborates with industry partners on applications in aerospace and energy systems.

• Operating Systems
• Distributed Systems
• Computer Networks
• +۳ مورد دیگر

Andrew Tanenbaum is a Professor Emeritus at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Computer Systems department. He holds a PhD in Physics from the University of California and a Bachelor's in Physics from MIT. His expertise spans operating systems, distributed systems, and computer networks, with a focus on fault tolerance and system security. Tanenbaum has authored seminal textbooks like 'Operating Systems: Design and Implementation' and 'Computer Networks.' He has been awarded the Eurosys Lifetime Achievement Award (2015), ACM Fellowship (1996), IEEE Fellowship (1998), and honorary doctorates (2008, 2011). His research emphasizes reliable system design, including projects like MINIX, and explores fault injection, storage systems, and live updating mechanisms. Tanenbaum has supervised 20 PhD theses and contributed to over 300 publications. His work bridges theoretical foundations with practical implementations, influencing modern operating system and network architecture.

• System Security
• Fault Injection
• Fuzzing
• +۵ مورد دیگر

Erik van der Kouwe is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, holding dual appointments in the Computer Systems department and the Network Institute. His research focuses on system security, fault injection, fuzzing, and embedded systems security. He contributes to both theoretical advancements and practical implementations of security mechanisms. Key research areas include mitigating vulnerabilities through hardware-software co-design, exposing compiler optimization flaws that evade sanitizers, and developing efficient fuzzing frameworks for malware analysis. His work often bridges academic research with real-world applications in embedded systems and operating systems. Recent publications highlight innovations like InvisiGuard for microcontroller security, hwdbg for hardware debugging frameworks, and Enviral for environment-aware malware analysis. His research has been published in top-tier venues such as IEEE Transactions, ACM conferences, and workshops like EuroSEC. Teaching responsibilities include Advanced Operating Systems, Secure Programming, and Software Security courses. Collaborations span international teams working on topics like compiler hardening, embedded system resilience, and vulnerability benchmarking.

• Stochastic Modeling
• Model Checking
• Hybrid Systems
• +۴ مورد دیگر

Dr. Anne K.I. Remke is a Full Professor in the Department of Mathematics of Operations Research at the University of Twente's College of Engineering. Her research focuses on stochastic modeling, hybrid systems, and formal verification techniques, with applications in energy systems, cybersecurity, and healthcare. She contributes to UN Sustainable Development Goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Her recent work includes developing stochastic hybrid system specifications, intrusion detection systems for smart grids, and parameter estimation methods for battery models. She has collaborated internationally on projects involving energy distribution networks and cybersecurity infrastructure. Remke has supervised 3 PhD students and maintains an active publication record with over 76 research outputs since 2004. Her technical expertise spans model checking algorithms, probabilistic verification, and algorithm design for infinite-state systems. She frequently publishes in peer-reviewed journals like Energy Informatics and conferences such as E-Energy and IEEE CSR, focusing on real-world applications like smart grid communication and energy management optimization.

Min Wanمشاهده پروفایل

استادیار

• Terahertz Imaging Systems
• Optical Imaging Techniques
• Digital Holography
• +۵ مورد دیگر

Dr. Min Wan is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). She holds a Master's degree in Optical Engineering from Beijing University of Technology (2017) and a PhD in Optical Imaging Techniques from University College Dublin (UCD, 2021). Prior to TU/e, she served as an Assistant Professor at UCD and held a visiting position there until 2029. Her research focuses on terahertz imaging systems, digital holography, subpixel displacement estimation, and biomedical applications of THz technology, contributing to UN Sustainable Development Goals related to education and innovation. Education: PhD in Optical Engineering, University College Dublin (2017-2021) Master's in Optical Engineering, Beijing University of Technology (2014-2017) Research Interests: Terahertz full-field imaging and tomography Optical imaging techniques for biomedical applications Subpixel motion estimation algorithms Digital holography and signal processing Optical system design and optimization Her work integrates advanced imaging modalities with computational methods, emphasizing practical implementations in terahertz systems. Recent Research Trends: Dr. Wan’s recent articles emphasize advancements in terahertz full-field imaging, beam illumination optimization, and subpixel tracking algorithms. Her 2025 studies on galvanometric illumination and Bai distribution sampling highlight innovations in imaging precision and signal analysis, while 2024 work on diffractive neural networks explores optical computing applications. Awards & Grants: No specific awards listed, but her research has received significant citations and funding for projects like the RECENTRE program (2024). Labs & Teams: Active in TU/e’s Electrical Engineering labs focusing on terahertz systems and optical imaging. Collaborates internationally on projects involving THz tomography and biomedical applications.

Anton J. Wijsمشاهده پروفایل

دانشیار

• Parallel Software Development
• Formal Verification
• Model Checking
• +۴ مورد دیگر

Anton J. Wijs is an Associate Professor at the Eindhoven University of Technology (TU/e) , affiliated with the Model-Driven Software Engineering group within the Department of Software Engineering and Technology . His research focuses on parallel software development and leveraging parallelism to enhance formal verification techniques. He holds leadership roles as a Board Member of the European Association for Programming Languages and Systems (EAPLS) and a member of the Steering Committee of ETAPS. Academic Background: Wijs earned his PhD in Computer Science from VU Amsterdam (2007), followed by postdoc positions at INRIA (France), TU/e, and RWTH Aachen University (Germany). He transitioned to an Assistant Professor role at TU/e in 2015 and became an Associate Professor in 2023. Research Interests: His work emphasizes scalable verification methodologies using GPUs, formal analysis of concurrent systems, and tools like mCRL2 . He collaborates with industry partners such as ASML and Codeplay Software to bridge academic research with practical applications. Awards & Grants: Notable achievements include the Best Student Paper Award (FACS 2017) , the Amazon Research Award (2022) , and an NWO grant for GPU-accelerated state space analysis. He co-led the GEARS project (2017–2022) , focusing on efficient parallel model checking. Teaching: Wijs teaches courses on Programming , Program Verification Techniques , and Digital Twins of Medical Devices . He actively supervises students and contributes to international conferences as a program chair and reviewer.