Ravindra Laxman Shinde is a Researcher in Computational Chemical Physics at the MESA+ Institute, University of Twente. His work focuses on quantum Monte Carlo methods, exascale quantum simulations, and high-performance software development for accurate electronic structure calculations. Research Interests: Quantum Monte Carlo (QMC) Machine-Learned Force Fields Exascale Computing Electronic Structure Theory Scientific Software (CHAMP, AiiDA) Reproducibility in Computational Physics His recent publications highlight a strong trend in developing robust, scalable software solutions for quantum mechanical simulations, particularly in navigating hardware-software challenges at the exascale. His work bridges theoretical physics, computational chemistry, and computer science, with a focus on practical implementation and data integrity. Scientific Awards: FAIR data fund 4TU 2024 Advising and Grants: While specific students are not listed, Shinde is deeply involved in large-scale collaborative research projects such as TREX and NWO CHAINS, indicating leadership in grant-funded, team-based scientific computing initiatives. His role in creating software and datasets suggests mentorship in computational methods and data practices. Labs and Teams: He is a key contributor to the TREX project and the development of the CHAMP software suite, working within interdisciplinary teams focused on advancing quantum simulation capabilities at the exascale.
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.
Prof. Dr. Saša Kenjereš is a Full Professor in the Department of Chemical Engineering at the Delft University of Technology , Faculty of Applied Sciences, Netherlands. He leads a multidisciplinary research group focusing on transport phenomena in biomedical, environmental, and energy systems. His work integrates computational modeling, high-performance computing, and experimental validation, with strong collaborations with medical centers (e.g., EMC Rotterdam, LUMC Leiden) and international universities (ETH Zurich, Harvard, AGH Krakow). Education and Academic Background: Ph.D. in Applied Physics, Delft University of Technology (1999) Research Fellow, Royal Netherlands Academy of Arts and Sciences (KNAW, 2001–2005) Burgers Visiting Associate Professor, University of Maryland, USA (2005–2006) Marie-Curie Visiting Professor, AGH University, Poland (2007–2013) Visiting Professor, Shibaura Institute of Technology & AGH University (2022–present) Research Interests: Prof. Kenjereš specializes in multiscale transport phenomena , combining fluid dynamics , heat and mass transfer , turbulence , and magnetohydrodynamics (MHD) with applications in biomedical engineering (e.g., blood flow, drug delivery), environmental flows , and high-performance computing (HPC) . His lab uses advanced techniques like PIV and MRI to simulate patient-specific cardiovascular and respiratory systems. Publication Trends: His recent publications reflect a strong focus on biomedical fluid dynamics , particularly patient-specific CFD simulations of blood and air flows, magnetic drug targeting , and atherosclerosis modeling . The integration of experimental validation with computational models is a recurring theme, emphasizing translational research from engineering to clinical applications. Scientific Awards and Honors: Research Fellow, KNAW (2001–2005) Burgers Visiting Associate Professor, University of Maryland ERCOFTAC & Leonhard Euler Centre Fellowships (ETH Zurich) Marie-Curie Visiting Professor Current Visiting Professor in Japan-Poland MSc program Advising and Grants: Prof. Kenjereš has supervised over 20 PhD students , 15 postdocs , and more than 200 MSc/BSc students across Applied Physics, Chemical Engineering, and Mechanical Engineering. His research is supported by strategic collaborations with medical institutions and international universities, indicating strong grant funding and interdisciplinary project leadership. He teaches core courses such as Continuum Physics and Advanced Physical Transport Phenomena . Laboratories and Research Groups: The Kenjereš Lab is a hub for computational and experimental fluid dynamics , focusing on biomedical transport phenomena . The lab emphasizes patient-specific modeling , magnetic drug targeting , and high-fidelity simulations using HPC. It maintains close ties with medical imaging centers and employs advanced PIV and MRI techniques for experimental validation.
Prof. Wiro Niessen serves as Dean of the Faculty of Medical Sciences at the University of Groningen and a board member of the UMCG Faculty of Medical Sciences. His research focuses on AI applications in medical imaging and health, particularly in tumor diagnostics, genomics, and clinical informatics. He holds leadership roles in multiple academic and industry organizations, including the MICCAI Society and the Royal Netherlands Academy of Arts & Sciences (KNAW). Key research contributions include AI-driven tumor segmentation, genetic variant analysis, and automated disease classification systems. His work integrates multi-omics data and deep learning to improve diagnostic accuracy and personalized medicine. Niessen has authored over 450 publications, with recent focus on radiological imaging standards and multi-center validation studies. Awards include Fellowships from MICCAI and IS3R, and the KNAW Young Scientist Award. He actively participates in national and international initiatives for digital healthcare innovation, open science, and high-performance computing strategies.
Tanja Vos is a Full Professor at the Faculty of Science , Department of Computer Science , specializing in Software Testing , Automated Testing , and Behavior-Driven Development (BDD) . Her work extends to Gamification in Education and Artificial Intelligence (AI) in Learning Systems . Research Program: Towards High-quality and Intelligent Systems (THIS) Active in Graphical User Interface (GUI) Analysis and Test Automation Research Interests : Vos focuses on advancing software testing methodologies through AI-driven solutions , particularly in 3D game testing , chatbot applications , and gamified educational platforms . Her work bridges theoretical research with industrial implementation . Recent Article Trends : Her publications emphasize automated testing frameworks (e.g., iv4XR ), GUI regression analysis , and AI chatbots for personalized learning . Subfields include evolutionary algorithms , dynamic interface testing , and gamification mechanics . Supervision & Collaboration : Vos leads projects like TESTOMAT and AI Study Coach , collaborating with institutions such as Universidad Politecnica de Valencia .
Edwin van der Weide is an Associate Professor in Engineering Fluid Dynamics at the University of Twente, actively contributing to computational fluid dynamics research and graduate education as of 2024. His work bridges theoretical numerical methods with practical aerospace and renewable energy applications, evidenced by 73 research outputs including journal articles, conference papers, and a graduate textbook on scientific computing. His research centers on Computational Fluid Dynamics (CFD) with specialization in Discontinuous Galerkin methods, supersonic/hypersonic flows, and wind turbine aerodynamics. Key investigations include boundary condition development for Navier-Stokes solvers, ice crystal prediction in aircraft engines, and erosion effects on renewable energy systems. His approach integrates high-fidelity numerical simulations with experimental validation to solve complex fluid-structure interaction problems. Recent publications (2022-2024) reveal a strong trend toward applied aerospace challenges: boundary condition optimization for fluid solvers, supersonic cross-flow injection systems, aircraft icing certification, and wind turbine blade erosion. These works demonstrate interdisciplinary convergence of computational mathematics, aerodynamics, and engineering design for real-world propulsion and energy systems. Scientific recognition includes: HiSST Best Student Paper Award 2024 (for dual injection in supersonic cross-flow research) Van der Weide has supervised 9 research projects, collaborating with colleagues across fluid dynamics and aerospace engineering. His peer-review activities for journals like Mathematics and Computers in Simulation and extensive conference presentations indicate active engagement in the global research community. Current work includes open-source scientific computing frameworks and high-order grid development for complex flow simulations.
Antonio Pellegrino is a Professor of Subatomic Physics, specifically Flavor Physics, at the University of Groningen's Faculty of Science and Engineering. He is affiliated with the High-Energy Frontier research group at the Van Swinderen Institute for Particle Physics and serves as a Senior Researcher/Project Leader at Nikhef, the National Institute for Subatomic Physics. Professor Pellegrino's research focuses on subatomic physics with specialization in flavor physics and high-energy particle physics. His work primarily involves the LHCb experiment at CERN, where he contributes to charged-particle tracking and trigger systems, particularly the Vertex Locator Tracking System and Track Reconstruction Computing. His research spans B-meson decays, CP violation, heavy-flavor physics, and heavy-ion collision studies. He has made significant contributions to understanding fundamental particle properties through precision measurements of decay processes and interactions. Analysis of Professor Pellegrino's recent publications reveals consistent focus on precision measurements in particle physics, particularly in B-meson decays, angular analyses, and detector performance optimization. His work shows strong emphasis on understanding CP violation mechanisms and searching for physics beyond the Standard Model through careful analysis of rare decay processes. Professor Pellegrino has supervised six research projects and is actively involved in the development of future detector upgrades for the LHCb experiment. His research group at the University of Groningen collaborates extensively with international partners on the analysis of data from the Large Hadron Collider, contributing to the global effort in advancing our understanding of fundamental particle physics.
Tom Wilderjans is an Associate Professor in the Department of Psychology Methodology and Statistics at Leiden University's Faculty of Social and Behavioural Sciences. His work bridges quantitative methodology and behavioral research through advanced data analytical techniques. His research focuses on developing novel methods for complex data analysis in social sciences, particularly: Uncovering qualitative/quantitative differences in human behavior mechanisms Data fusion of heterogeneous datasets (EEG/fMRI, observational/questionnaire) Unsupervised learning for inter-individual differences High-performance computing for big data analysis His publication trends reveal strong interdisciplinary applications, primarily using machine learning and statistical modeling to address clinical psychology challenges like HIV-related depression interventions. The 2019 JMIR study exemplifies his focus on web-based behavioral interventions with rigorous methodological validation. Teaching responsibilities include Psychology programs at BSc and MSc levels, emphasizing methodological rigor in behavioral research.
Dr. Shoko Jin is a Researcher at the Kapteyn Astronomical Institute within the Faculty of Science and Engineering at the University of Groningen. Her work centers on astronomical instrumentation and galaxy evolution, with no indication of part-time status or additional institutional affiliations. Her research spans: Galaxy formation and evolution dynamics Stellar population analysis and star formation processes Spectroscopic survey design (notably the WEAVE project) Applications of machine learning in astrophysical data analysis High-redshift cosmic structures and gravitational lensing Dr. Jin's publications (2018-2025) show consistent focus on large-scale astronomical instrumentation, particularly the development and optimization of the WEAVE spectrograph. Her collaborative work frequently addresses galaxy surveys, data retrieval methods, and telescope system validation. She is affiliated with the Kapteyn Astronomical Institute but leads no explicitly named research group. Prospective collaborators may contact her via s.jin@rug.nl.
Denis Voskov is an Associate Professor at Delft University of Technology (Faculty of Civil Engineering and Geosciences, Department of Geoscience & Engineering). He also holds an Adjunct Professor position at Stanford University (Department of Energy Resources Engineering, USA). His academic career spans roles as Senior Researcher (2007-2015) and Research Associate (2005-2007) at Stanford, CTO of Rock Flow Dynamic (2005-2008), and engineering positions in oil companies and research institutions in Russia (2000-2005). Current: Head of Reservoir Engineering Section (2024-present) Current: Associate Professor at TU Delft (2015-present) Current: Adjunct Professor at Stanford University (2016-present) Voskov specializes in modeling complex subsurface systems , focusing on reactive flow and transport in altering porous media, scale translation for physical processes, high-performance computing for forward/inverse problems, thermal/geothermal process simulation, CO2 sequestration, and nonlinear solver analysis. His work bridges computational methods (finite volume frameworks, augmented flash calculations, operator-based linearization) with energy transition applications like geological carbon storage and geothermal resource management. Key trends in his recent publications (2024-2025) include: advanced CO2 storage mechanisms (capillary pinning, halite precipitation), multiphysics simulation frameworks (thermal-hydro-mechanical-compositional models), data assimilation for geothermal reservoirs, physics-informed neural networks for subsurface problems, and open-source tools like DARTS-well. Topics frequently intersect with energy transition, reservoir heterogeneity, and numerical robustness. Voskov's lab and team activities center on subsurface energy systems at TU Delft, including the campus geothermal project for direct-use heating and digital twin development for geothermal reservoirs. He collaborates internationally, particularly with Stanford University, and leads initiatives in GPU-based Monte Carlo simulations for uncertainty quantification.
Robert Belleman is a Lecturer and researcher at the Informatics Institute within the Faculty of Science at the University of Amsterdam. He holds multiple leadership roles, including Director of the College of Informatics and Manager of the Visualization Lab at Science Park. His work focuses on computational science and data visualization, supporting interdisciplinary research and education initiatives. Current affiliations: Computational Science Lab, Informatics Institute, Faculty of Science, University of Amsterdam Key roles: Director of College of Informatics, Manager of Visualization Lab, Head of Education Group Research interests: Computational Science, Data Visualization, High-Performance Computing, Scientific Computing Contact: R.G.Belleman@uva.nl
Alex Dömling is Professor and External Collaborator at the University of Groningen's Faculty of Science and Engineering, affiliated with the Groningen Research Institute of Pharmacy. His research spans medicinal chemistry, organic synthesis, and drug discovery, with specialized expertise in multicomponent reactions and protein-protein interaction modulation. Research interests focus on: Design and synthesis of bioactive compounds Development of novel synthetic methodologies Cancer immunotherapy targets (PD-1/PD-L1) Computational drug design approaches Protein-protein interaction inhibitors Publication trends show consistent work in medicinal chemistry with recent expansion into metabolic health and immunotherapy. His highly cited work includes foundational research on multicomponent reactions and protein interaction antagonists. Significant scientific recognition includes: Ranking among top 2% most influential researchers worldwide (2021) PITT Innovator Award (2011) ERUDITE Scholar in Residence (2011) Honorary Diploma (2012)
Dr. Adam Belloum is affiliated with the Informatics Institute within the Faculty of Science at the University of Amsterdam . His research focuses on distributed systems, high-performance computing, big data, and eScience, with a strong emphasis on scientific workflow management and data science education. He has delivered numerous invited talks across Europe, the Middle East, and Asia, covering topics such as the evolution of distributed systems, AI development, and challenges in STEM education. His work bridges academic research with practical applications in HPC, cloud computing, and training data scientists for industry. He has co-organized international workshops like eScience2019, HPCS15, and SWES06-09, and contributed to special issues on workflow management systems in journals like Future Generation Computer Systems and Journal of Scientific Programming .
Kubilay Atasu is an Associate Professor at Delft University of Technology, affiliated with the School of Electrical Engineering, Mathematics and Computer Science and the Data-Intensive Systems department. His research focuses on core areas of computer science and electrical engineering, particularly in data systems and high-performance computing.
Marcella A.M.G. Hoogeboom serves as an Assistant Professor in Professional Learning & Technology at the University of Twente. Her research program centers on understanding and measuring learning processes in professional contexts, with particular emphasis on how technology can support learning-on-the-go in workplace settings. Dr. Hoogeboom's research interests span multiple interdisciplinary domains: Learning Sciences and Educational Technology Leadership Psychology and Team Dynamics Physiological Measures in Learning Contexts Informal Learning Processes Workplace Learning in Smart Industry Behavioral Coding and Analysis Her recent publications demonstrate a strong methodological approach that combines quantitative data analytics with qualitative behavioral observations. She has conducted significant research on informal learning processes using mobile applications, coordination patterns in high-stakes environments like the Apollo 13 mission, and physiological correlates of effective leadership behaviors. Her work on physiological arousal in leadership contexts published in the Leadership Quarterly has garnered 16 citations in Scopus, indicating its impact in the field. Dr. Hoogeboom actively contributes to the academic community through presentations at major conferences including the EARLI SIG14 Conference on Learning On-the-Go and the Academy of Management Annual Meeting. Her current research explores how digital tools can support production workers' learning in the Smart Industry sector, addressing critical challenges in measuring and supporting dynamic learning processes in technology-rich work environments.
