جست‌وجوی استاد و پژوهشگر

Mark van den Brandمشاهده پروفایل

استاد

• Software Engineering
• Digital Twin Engineering
• Model-Driven Engineering
• +۴ مورد دیگر

Mark van den Brand is a Full Professor of Software Engineering and Technology at Eindhoven University of Technology (TU/e), where he holds the chair in Software Engineering and serves as Scientific Director of the Digital Twin Lab (part of EAISI). He concurrently holds a Visiting Professorship at Royal Holloway University of London. His academic journey includes a PhD in Computer Science from Radboud University (1992), prior roles as part-time Associate Professor at Vrije Universiteit Amsterdam, and senior research positions at CWI. He also serves as Editor-in-Chief of the Journal on Software Engineering of Autonomous Systems (JSEAS) and editorial board member of Computer Languages, Systems and Structures . His research focuses on software engineering fundamentals, digital twins, model-driven engineering, safety-critical systems, and automotive software. Key contributions include work on domain-specific languages, model consistency, and safety assessment frameworks. He leads the Software Engineering and Technology cluster at TU/e and directs educational initiatives in the Department of Mathematics and Computer Science. Recent publications emphasize digital twin engineering, model management, and automotive systems safety. He collaborates internationally on projects like the C-ITS reference architecture and functional safety for autonomous driving. His work bridges academic research with industrial challenges through tools like SAMOS and COGENT, addressing scalability and consistency in large-scale systems. Current educational responsibilities include courses on programming, digital systems, and data science. He actively engages with industry through the TU/e innovation ecosystem, focusing on impactful research solutions for modern software engineering problems.

Jan Friso Grooteمشاهده پروفایل

استاد

• Formal Methods
• Verification Technology
• Software Engineering
• +۱۲ مورد دیگر

Jan Friso Groote is a Full Professor and Chair of the Formal System Analysis group in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He also holds professorial roles in the EAISI Foundational and EAISI High Tech Systems institutes. Since 2016, he has been working part-time at ASML, contributing his expertise in formal verification to industrial applications. Education: Born in 1965, studied Computer Science at Twente University of Technology (now University of Twente), 1983–1988. PhD in 1991 from the University of Amsterdam with thesis 'Process algebra and structured operational semantics', based on research at CWI (Centrum Wiskunde en Informatica). Jan Friso Groote is a leading researcher in formal methods and software verification. His work focuses on enabling the development of flawless software through rigorous formal analysis. Key research areas include structural operational semantics, model checking, branching bisimulation, protocol verification, and the development of the mCRL2 toolset. His current goal is to integrate formal techniques into complete software system design, improving both development speed and quality. His research has demonstrated that formal methods can reduce development time by a factor of three and increase quality tenfold, with potential for zero-defect software. His recent publications demonstrate sustained contributions in formal verification, including work on mutual exclusion algorithms, industrial control system modeling, probabilistic systems, and efficient bisimulation algorithms. The articles span topics such as tunnel control systems, simulation lower bounds, and formal methods for critical systems, reflecting both theoretical depth and practical application. Scientific Awards: Best Paper Award FACS 2018 FMICS-AVoCS Best Paper Award (2017) Jan Friso Groote has held significant leadership roles in education, including Director of Education for Computer Science (2000–2010) and for multiple bachelor’s and master’s programs. He has advised numerous researchers and supervised a large body of research output (over 320 publications). He leads the Formal System Analysis group and has been involved in projects such as 'Composable Embedded Systems for Healthcare'. His work bridges academia and industry, particularly through collaborations with ASML and Rijkswaterstaat, and he has been a visiting researcher at institutions across Europe and China. He is a key contributor to the mCRL2 toolset, which supports modeling and verification of software behavior with data, time, and probabilities. His research fingerprints highlight strong expertise in model checking, transition systems, software design, and process algebra. He teaches courses such as System Validation, Embedded Software, and Capita Selecta in Formal System Analysis.

Eric Nalisnickمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +۵ مورد دیگر

Eric Nalisnick is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He holds affiliations with the Institute for Assured Autonomy, Mathematical Institute for Data Science, and Data Science and AI Institute. His research focuses on developing safe and robust intelligent systems through probabilistic modeling and computational statistics, with applications in healthcare, online content moderation, and sign language processing. His work emphasizes human-centered design, exploring how to incorporate prior knowledge, detect system failures, and integrate human-machine decision-making. Research Trends: Recent publications highlight advancements in uncertainty quantification (e.g., Lightning UQ Box tool), early-exiting neural networks for risk control, and generative models for symmetry transformations. He also investigates calibration in multi-distribution learning and ethical considerations in hate speech detection systems. Advising & Contributions: Eric mentors a diverse cohort of PhD students and has developed influential open-source tools like Lightning UQ Box and Learning to Defer frameworks. His teaching includes courses on Deep Learning, Human-in-the-Loop Machine Learning, and Bayesian Methods.

Remco Renkenمشاهده پروفایل

پژوهشگر

• Neuroimaging
• Functional MRI
• Parkinson's Disease
• +۳ مورد دیگر

Dr. Remco Renken is a neuroimaging researcher at the University of Groningen's Faculty of Medical Sciences , specializing in advanced MRI and PET scan analysis. His work bridges clinical neuroscience and computational methods , with a focus on movement disorders, visual processing, and neurodegenerative diseases. Department of Radiology and Nuclear Medicine (UMCG) Clinical Cognitive Neuropsychiatry Research Program (CCNP) Perceptual and Cognitive Neuroscience (PCN) group Research Interests : Using fMRI , PET , and machine learning to study brain connectivity , visual cortex plasticity , and neurodegenerative patterns in Parkinson’s, multiple sclerosis, and glaucoma. His innovations include algorithms for 3D motion perception tracking and neuroimaging harmonization . Notable Contributions : Co-developed IRMA (Machine learning harmonization for multicenter PET scans) Co-inventor of a 3D motion perception evaluation system Co-supervisor for PhD research on auditory cortex responses and schizophrenia hallucinations Collaborations span neurology, psychiatry, and biomedical engineering, with datasets shared via Mendeley and DataverseNL.

• Artificial Intelligence
• Logic
• Human-centered Artificial Intelligence
• +۲ مورد دیگر

Dr. Natasha Alechina is an Associate Professor at Utrecht University's Faculty of Science , specializing in the Intelligent Systems subdomain. Her research focuses on multi-agent systems, formal verification, and logic-based AI, with particular emphasis on resource constraints and human-centered applications. Research Themes : Specification and verification of multi-agent systems Resource-bounded reasoning in coalitions Temporal and probabilistic logic for AI Causal modeling in autonomous agents Article Trends show consistent contributions to multi-agent systems , formal methods , and responsible AI . Her 2023-2025 work explores causal dependencies, intention progression under uncertainty, and ethical frameworks for group responsibility. Contact : n.a.alechina@uu.nl at Buys Ballot Building, Utrecht.

Danny Lathouwersمشاهده پروفایل

دانشیار

• Nuclear Engineering
• Computational Physics
• Proton Therapy
• +۴ مورد دیگر

Danny Lathouwers is an Associate Professor at the Delft University of Technology within the Faculty of Applied Sciences and the Department of Radiation Science and Technology . His research focuses on computational techniques for nuclear reactor physics and proton therapy, particularly using adaptive numerical methods to simulate reactor systems and charged particle transport for high-quality dose distribution in patient geometries. Academic Affiliation : Delft University of Technology, Faculty of Applied Sciences, Department of Radiation Science and Technology Contact : Mekelweg 15, 2629JB Delft, The Netherlands | +31 15 278 3148 | d.lathouwers@tudelft.nl His research areas include: Nuclear Reactor Physics : Numerical methods, modal analysis of neutron transport, multi-physics simulations for Generation-IV reactors Proton Therapy : Deterministic dose calculation, uncertainty quantification, robust treatment planning Adaptive Radiation Transport : Spatial/angular refinement techniques, discontinuous Galerkin methods Recent publications highlight his expertise in: Proton therapy range verification systems Deterministic low-rank transport methods Deep learning applications for anatomical variability Multiphysics reactor modeling He actively supervises MSc and BSc student projects in computational reactor physics and proton therapy.

Dennis R. Schaartمشاهده پروفایل

استاد

• Medical Physics
• Radiation Oncology
• Medical Imaging
• +۱۳ مورد دیگر

Dennis R. Schaart is a Professor and head of the Medical Physics & Technology section at the Department of Radiation Science & Technology, Faculty of Applied Sciences, Delft University of Technology (TU Delft). He is also a member of the R&D Program Board of the Holland Proton Therapy Centre (HollandPTC), highlighting his significant role in advancing clinical radiation technologies. His work bridges fundamental physics with medical applications, particularly in imaging and therapy. His primary research interests include Medical Physics, Radiation Oncology, Medical Imaging, Radiation Detection, Dosimetry, and Biomedical Engineering . He specializes in positron emission tomography (PET), time-of-flight methods, proton therapy, and scintillation detector development. His expertise in Monte Carlo simulation and experimental physics enables rigorous evaluation and innovation in detector systems and imaging protocols. The recent publications (2025–2021) reveal a strong trend toward photon-counting X-ray and PET detectors , proton therapy optimization , and novel scintillator materials . There is a clear emphasis on improving spatial, temporal, and energy resolution in imaging, with applications in both diagnostics and treatment planning. The integration of machine learning and Monte Carlo simulations further enhances the predictive and analytical power of his research. Scientific Awards: SNMMI 2015 International Best Abstracts Award Awarded for the highest number of citations for an article published over 2004–2008 Most cited paper in preceding five years (GATE V6 paper) Recognition at Trace 'n Treat conference for radionuclide state determination Dennis Schaart has (co-)authored over 100 journal papers and is a frequently invited speaker, indicating strong leadership and influence in the medical physics community. While no direct mention of students is found, his leadership role and extensive publication record suggest active supervision and mentorship. He is involved in national advisory roles, including serving on committees for the Ministry of Economic Affairs, reflecting broader impact beyond academia. His work is supported by collaborations with institutions like Philips, CERN, and various medical centers. Laboratories and Research Groups: He leads the Medical Physics & Technology research group within the Radiation Science & Technology department at TU Delft. The group focuses on developing and evaluating novel detector systems for medical imaging and therapy, using both experimental and computational approaches. The lab is equipped for scintillator characterization, detector prototyping, and advanced simulations, particularly using the GATE platform.

Lorenzo Galeottiمشاهده پروفایل

پژوهشگر

• Mathematical Logic
• Computability Theory
• Set Theory
• +۳ مورد دیگر

Lorenzo Galeotti is a researcher affiliated with the University of Amsterdam, specifically working within the Mathematical & Computational Logic (MCL) group at Amsterdam University College. His research focuses on foundational aspects of mathematical logic, computability theory, and generalized analysis. Research Interests: Galeotti investigates topics at the intersection of logic, computability, and non-standard mathematical structures. His work includes studying transfinite computability, infinitary intuitionistic set theory, and the properties of generalized real numbers and non-Archimedean fields. He also explores the application of logical frameworks in educational contexts, such as game-based learning tools for propositional logic. Publications: His recent articles address advanced topics in logic, including models of Peano arithmetic fragments, symmetry in infinite-time computation, and surreal number systems. These works reflect a consistent focus on extending classical concepts into transfinite and non-standard domains. Contact: Email correspondence can be directed to l.galeotti@uva.nl .

• Machine Learning
• Natural Language Processing
• Language Modelling
• +۷ مورد دیگر

Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.

Mireille Boutinمشاهده پروفایل

استاد

• Applied Mathematics
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Mireille Boutin is a Professor at the Eindhoven University of Technology , affiliated with the Department of Mathematics and Computer Science within the Discrete Algebra and Geometry group. Her research spans applied mathematics, signal processing, and machine learning, with a focus on discrete inverse problems and the integration of geometric tools in probabilistic frameworks. Academic Background : She earned a Ph.D. in Mathematics from the University of Minnesota under Peter Olver, followed by postdoctoral positions at Brown University and the Max Planck Institute for Mathematics in the Sciences . Before joining TU/e, she served on the faculty at Purdue University . Research Trends : Recent publications demonstrate her interdisciplinary approach, including solutions to global positioning uniqueness problems via algebraic geometry, computer-aided qualitative data analysis in education, symmetry-breaking in acoustic path tracking, and novel clustering models for high-dimensional data. Her work often bridges theoretical mathematics with practical applications in engineering and education. Educational Contributions : She actively teaches Computational Algebra at TU/e and has collaborated on studies applying machine learning to qualitative education research, such as analyzing student explanations in programming and faculty teaching conceptions.

• Stochastic Modeling
• Statistical Physics
• Non-Equilibrium Statistical Physics
• +۴ مورد دیگر

Cristian Spitoni is an Assistant Professor in the Mathematical Modeling group at the Mathematical Institute within the Faculty of Science at Utrecht University. His office is located in the Hans Freudenthal Building at Budapestlaan 6, Room 510, 3584 CD Utrecht. He maintains an active research profile spanning mathematical physics, statistics, and interdisciplinary applications in medical informatics and neuromorphic computing. His primary research interests include Stochastic Modeling, Statistical Physics, Non-Equilibrium Statistical Physics, and Survival Analysis. Dr. Spitoni's work demonstrates a remarkable interdisciplinary range, bridging theoretical mathematics with practical applications in healthcare and computing. His research trajectory shows a fascinating evolution from fundamental statistical physics problems to medical applications and more recently to neuromorphic computing. Analysis of his recent publications (2023-2025) reveals three major research thrusts: 1) Theoretical work on probabilistic cellular automata and metastability in statistical physics; 2) Medical statistics applications focusing on ICU infections, sepsis, and survival analysis; and 3) Cutting-edge research in neuromorphic computing, particularly on memristors, fluidic circuits, and brain-inspired computing architectures. His work increasingly shows interdisciplinary convergence, with mathematical techniques from statistical physics being applied to both medical informatics and neuromorphic engineering problems. Dr. Spitoni has established productive collaborations with researchers across multiple disciplines, including medical researchers at Utrecht University Medical Center and physicists working on novel computing architectures. His research has been published in high-impact journals spanning physics, mathematics, medical informatics, and computer science, demonstrating the breadth and significance of his contributions. His teaching responsibilities include courses such as Interacting Particle Systems in the Lattice and Continuum, Introduction to Complex Systems, and Mathematical Statistics, reflecting his expertise in both theoretical and applied mathematical modeling.

Max Wellingمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۳ مورد دیگر

Max Welling is a research chair in Machine Learning at the University of Amsterdam's Informatics Institute and a Distinguished Scientist at Microsoft Research (MSR). He also serves as a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS), where he is a founding board member. His career spans academic and industrial leadership, including roles as Professor at UC Irvine and Vice President at Qualcomm Technologies. He co-directs the Amsterdam Machine Learning Lab (AMLab), the Qualcomm-UvA deep learning lab (QUvA), and the Bosch-UvA Deep Learning lab (Delta). His research bridges theoretical physics and machine learning, focusing on probabilistic modeling, neural networks, and statistical learning. CIFAR Fellow ELLIS Fellow ECCV Koenderink Prize (2010) ICML Test of Time Award (2021) Welling earned his PhD in theoretical high energy physics at Utrecht University under Nobel laureate Prof. Gerard ‘t Hooft. He completed postdoctoral work at the University of Toronto, UCL, and Caltech, mentored by Prof. Geoffrey Hinton and Prof. Pietro Perona. He has held editorial leadership roles at IEEE TPAMI and advisory positions at NeurIPS, and chaired major conferences like NeurIPS, AISTATS, ECCV, and MIDL.

Fred W. Wubsمشاهده پروفایل

دانشیار

• Numerical Mathematics
• Finite Element Analysis
• Bifurcation Analysis
• +۵ مورد دیگر

Dr. ir. Fred W. Wubs is an Associate Professor of Numerical Mathematics at the University of Groningen's Faculty of Science and Engineering, specifically within the Bernoulli Institute for Mathematics and Computer Science. His research spans multiple disciplines connecting applied mathematics with real-world applications in biomechanics and climate science. Based at the Bernoulliborg (room 451) in Groningen, Netherlands, he maintains active research collaborations across engineering, medical, and environmental fields. Wubs' research interests focus on numerical methods development and application, with particular expertise in finite element analysis, bifurcation theory, and computational modeling of complex systems. His work bridges theoretical mathematics with practical applications, spanning from biomechanical analysis of mandibular fractures to ocean circulation modeling and climate system dynamics. His fingerprint analysis reveals strong contributions in matrix mathematics (100%), bifurcation analysis (81%), time-stepping methods (58%), factorization techniques (58%), and Jacobian matrix applications (52%). His publication record shows a clear dual trajectory: one stream applying numerical methods to biomedical problems (particularly mandible fracture mechanics and orthopedic implant design), and another focused on climate and ocean modeling (including ocean circulation, ice sheet dynamics, and Earth system modeling). This interdisciplinary approach demonstrates how fundamental numerical mathematics can address diverse challenges across seemingly unrelated fields. Wubs has supervised 5 research projects as indicated by his academic profile, though specific student names aren't provided in the available information. His work has been cited extensively, with some publications receiving over 25 citations in Scopus. His research appears in high-impact journals including Scientific Reports, Journal of Advances in Modeling Earth Systems, and Journal of Computational Physics. He maintains active laboratory work in numerical simulation, with recent publications indicating ongoing projects in biomechanical testing validation using 3D printed models and advanced computational techniques for ocean and climate modeling. His work on physics-controlled echo state networks represents an innovative integration of machine learning with traditional numerical methods.

Ömür Arslanمشاهده پروفایل

استادیار

• Robotics
• Motion Planning
• Control Theory
• +۵ مورد دیگر

Ömür Arslan is an assistant professor in the Department of Mechanical Engineering at the Eindhoven University of Technology (TU/e) , affiliated with the Robotics Group and the EAISI Mobility and EAISI High Tech Systems institutes. His research focuses on the algorithmic foundations of robotics, particularly safe adaptive motion planning algorithms for robots operating around humans. Education: PhD in Electrical and Systems Engineering (2016), University of Pennsylvania MSc in Electrical Engineering (2012), University of Pennsylvania MSc in Electrical & Electronics Engineering (2009), Bilkent University BSc in Electrical & Electronics Engineering (2007), Middle East Technical University His research interests span robotics, motion planning, control theory, sensor networks, dynamical systems, optimization, machine learning , and machine perception . Recent work includes safe unicycle control, probabilistically safe corridors, and statistical coverage control of mobile sensor networks. He has received nominations for best paper awards at ICRA 2018 and WAFR 2016 . Ömür has supervised student projects such as Hancheng Min's MSc thesis on statistical coverage control and Vincent Pacelli's work on local geometry in motion planning .

Zerrin Yumakمشاهده پروفایل

استادیار

• Virtual Humans
• Social Robotics
• Affective Computing
• +۳ مورد دیگر

Zerrin Yumak is an Assistant Professor at the Department of Information and Computing Sciences, Faculty of Science, Utrecht University, specializing in virtual humans and social robotics. She leads Task 3.2: Embodiment and Physical Interaction in the RAGE project. Her research focuses on Social and emotional behavior analysis for virtual characters Machine learning applications in motion capture data Constrained motion graph search algorithms Lip-sync and gesture animation generation Real-time multimodal interaction systems Notable projects include the Virtual Human Controller asset for Unity 3D, integrated with BiP Media and Randstad applications. Recent work spans 2025: SemGes for semantics-aware gesture generation 2024: ProbTalk3D emotion-controllable facial animation 2023: Diffusion model-based facial animation (Facediffuser) 2020: Music-driven expressive gesture generation She contributes to workshops like GENEA (2021-2022) and MASSXR (2025), advancing non-verbal behavior modeling for embodied agents.