جستجوی استاد راهنمای دانشگاهی

Derek Karssenbergمشاهده پروفایل

استاد

• Earth Surface Systems
• Spatio-Temporal Modelling
• Hydrology
• +۷ مورد دیگر

Prof. Derek Karssenberg is a Professor of Computational Geography at Utrecht University's Department of Physical Geography within the Faculty of Geosciences. His research focuses on spatio-temporal modeling of Earth surface systems, including hydrology, geomorphology, ecology, and environmental health. He develops software frameworks like PCRaster, LUE, and Campo to enhance model scalability and usability. Current roles include coordinating the Computational Geography group and leading the AI & Sustainability Lab. His work integrates geocomputation with applied data science, addressing sustainability challenges and health-environment linkages. Research interests emphasize model development for complex systems, such as air pollution exposure assessment, land-use change impacts, and global hydrological modeling. Collaborations span environmental epidemiology, remote sensing, and machine learning. He co-initiated the Applied Data Science Master's program and innovates teaching methods using blended learning. Publications (2024–2020) highlight advancements in air pollution modeling, streamflow prediction via machine learning, and scalable hydrological frameworks. Activities include editorial roles for Environmental Modelling & Software and steering committees for Utrecht's Applied Data Science initiatives.

Henk Kiersمشاهده پروفایل

استاد

• Psychometrics
• Statistical Analysis
• Multivariate Statistics
• +۳ مورد دیگر

Henk Kiers is a Professor in the Department of Psychometrics and Statistics at the University of Groningen's Faculty of Behavioural and Social Sciences. His work contributes to UN Sustainable Development Goals through advanced statistical methodologies. He holds a prominent academic role with extensive contributions to multivariate statistics and computational methods. Research focuses on three-way component analysis, principal component analysis, and matrix mathematics applications. Notable contributions include developing R packages for statistical analysis and advancing techniques like Simplimax rotation. His work bridges theoretical statistics with practical applications in fields like metabolomics and chemometrics. Awards: Psychometrika Reviewer Award (2017). Supervision: Advised 10 PhD students, including work on replication success metrics and Bayesian statistics. Collaborations: Visiting researcher at institutions such as Sapienza University of Rome (2016) and Rovira i Virgili University (2018). Labs/Teams: Involved in interdisciplinary projects integrating statistical methods with behavioral and health sciences through datasets shared on platforms like Figshare.

Sajid Mohamedمشاهده پروفایل

پژوهشگر

• Image-Based Control Systems
• Multiprocessor Systems
• Motion Control
• +۷ مورد دیگر

Sajid Mohamed is a guest researcher in the Electronic Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. His work focuses on model-based design of image-based control systems tailored for resource-constrained domains like semiconductor manufacturing and automotive industries. PhD, M.Tech, and B.Tech degrees from TU/e, IIT Kharagpur, and NIT Calicut respectively His research combines expertise in Multiprocessor Systems , Deep Neural Networks , and Motion Control to address challenges in real-time performance and optimization. Key contributions include the IMOCO4.E reference framework and methodologies for vision-based DNN deployment on legacy hardware . Recent publications highlight advancements in Motion Control through multi-rate sensor fusion , DNN-driven visual perception , and predictable multi-core implementations . His work aligns with the UN Sustainable Development Goals, particularly in technological innovation for industrial efficiency. Best Paper Award at ECYPS (2019) Professionally, he serves as a Principal Software Engineer at ITEC B.V., integrating academic research with industry applications. Collaborations include projects like FitOptiVis and oCPS , emphasizing cyber-physical systems and edge computing.

Fredrik Janssonمشاهده پروفایل

پژوهشگر

• Atmospheric Science
• Climate Modeling
• Computational Physics
• +۵ مورد دیگر

Fredrik Jansson is a Researcher at Delft University of Technology, affiliated with the Faculty of Civil Engineering and Geosciences and the Geoscience and Remote Sensing department. He works on weather and climate simulations within the Ruisdael Observatory project, focusing on improving the Dutch Atmospheric Large-Eddy Simulation (DALES) model's performance and usability for high-resolution atmospheric studies. PhD in Physics from Åbo Akademi University (Finland) Develops DALES model for cloud and convection resolution Collaborates with the Netherlands eScience Center for computational optimization Research interests: pattern formation, cloud dynamics, cold pools, superparameterization His recent work examines how cold pools and trade cumulus self-aggregation influence Earth's radiation budget and climate systems. He explores symmetry in mesoscale circulations and the impact of environmental drivers on atmospheric boundary layer dynamics . Key projects include: Regional superparameterization of OpenIFS using DALES Cloud organization in radiative-convective equilibrium High-performance computing optimizations for climate models Fredrik also contributes to computational tools like Kilombo (Kilobot simulator) and VECMAtk for uncertainty quantification. His interdisciplinary work bridges atmospheric science, robotics, and DIY electronics.

• Software Model Checking
• Formal Methods
• Software Verification
• +۶ مورد دیگر

Dirk Beyer is a Full Professor and Head of Research Chair at the Department of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), where he leads the Software and Computational Systems Lab. His research focuses on developing models, algorithms, and tools for constructing and analyzing reliable software systems, with emphasis on software verification, model checking, and static analysis. Professor Beyer's research spans multiple critical areas in software engineering and formal methods. He has made significant contributions to software model checking through tools like CPAchecker and BLAST, structure analysis of large systems using CrocoPat and CCVisu, and formal verification of real-time systems with Rabbit. His work on interfaces for component-based design (Chic) has advanced modular software development approaches. Beyer has pioneered methodologies in benchmarking and reliable experimental evaluation through BenchExec, which has become a standard in tool competitions. Analysis of his recent publications reveals a strong focus on advancing software verification techniques, particularly in transferring knowledge between hardware and software verification domains, decomposing verification tasks for parallel processing, and improving the effectiveness of verification witnesses. His work consistently bridges theoretical foundations with practical tool implementations, with a growing emphasis on comparative evaluation and competition frameworks that drive the field forward. ACM SIGSOFT Distinguished Paper Award (FSE 2024) ACM SIGSOFT Best Artifact Award (FSE 2024) As a principal investigator of the DFG Research Training Group ConVeY, Beyer has secured significant funding for advancing verification techniques. He has played leadership roles in numerous software verification competitions including SV-COMP and Test-Comp, serving as PC Chair for major conferences like TACAS 2018 and VMCAI 2020. His service contributions extend to chairing ETAPS 2022 and organizing multiple workshops on CPAchecker. Professor Beyer leads the Software and Computational Systems Lab at LMU Munich, which has developed numerous influential verification tools including CPAchecker (configurable software verification), BenchExec (reliable benchmarking), and CCVisu (software structure visualization). The lab maintains active collaborations with research groups worldwide and contributes significantly to the international verification community through competitions, benchmarks, and open-source tool development.

• Information Systems
• Grid Computing
• Data Storage
• +۵ مورد دیگر

Fokke Dijkstra is a Researcher at the University of Groningen , specializing in Information Systems , Grid Computing , and Petabyte-scale Data Storage . His work focuses on enabling large-scale scientific research through infrastructure development. Key Collaborations: Astro-WISE, LOFAR (Low-Frequency Array) projects Research Themes: Long-term archiving, distributed computing, and grid technology for astrophysics Recent Activity: Contributed to a memorial article for Joop van Lenthe in 2022 Academic Output includes 15+ publications on scaling data systems for astronomy, with notable work on LOFAR and Astro-WISE infrastructure. His research bridges computational methods with astrophysical data challenges. Activities include participation in the BioSB 2020 conference, demonstrating his interdisciplinary engagement.

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.

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.

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.

Gabriel Beckersمشاهده پروفایل

استادیار

• Neurobiology
• Cognitive Science
• Animal Cognition
• +۳ مورد دیگر

Dr. Gabriel Beckers is an Assistant Professor at Utrecht University's School of Social and Behavioral Sciences. His research focuses on neurobiology, cognitive science, and animal cognition, with a particular emphasis on avian sleep patterns and syntax in animal communication. He has contributed to over 30 publications, including studies on sleep deprivation effects in jackdaws and the lack of syntactic structures in songbird vocalizations. Beckers also developed the Darr Python library, designed for persistent, interoperable storage of numpy arrays. His work bridges neuroscience, cognitive science, and computational methods, emphasizing tool-independent data formats and evolutionary perspectives on sleep and cognition. Research Areas: Neurobiology, Cognitive Science, Avian Sleep, Syntax Analysis in Animal Communication, Data Science Tools. Recent Contributions: Key studies include EEG-based analyses of sleep patterns in birds, meta-analyses of artificial grammar learning, and development of the Darr library for scientific data storage. His interdisciplinary approach combines experimental neuroscience with software engineering to advance both theoretical and applied research.

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.

• 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.

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.

• Astrophysics
• Data Systems
• Galaxy Evolution
• +۴ مورد دیگر

Willem-Jan Vriend is a Researcher at the University of Groningen , affiliated with the Faculty of Science and Engineering and the Astronomy Department . He specializes in astrophysics , data systems , and space mission infrastructure , with a focus on the Euclid mission and Astro-WISE technologies . His research interests include galaxy evolution , dark matter , near-infrared astronomy , and distributed computing for large-scale data processing. He contributes to the development of data transmission systems and infrastructure for space missions like Euclid, collaborating with institutions such as the European Space Agency and International Astronomical Union . His work emphasizes high-resolution imaging , data federation , and software engineering for astronomical data analysis. Publications highlight his role in the Euclid Wide Survey and the design of data distribution systems for space telescopes.