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Simon HengeveldView profile

Researcher

• Distance Geometry
• Computational Geometry
• Bioinformatics
• +3 more

Simon Hengeveld is a Researcher and Software Developer for Health Research at Wageningen University. He holds a PhD from Université de Rennes (2024), and bachelor's/master's degrees in Computer Science from Utrecht University. His research focuses on computational geometry, protein structure determination via Distance Geometry, and human motion analysis. He has developed applications like the UNESCO World Heritage Collect app, combining software engineering with historical and travel interests. Education: Bachelor's/Master's in Computer Science, Utrecht University PhD in Structural Biology/Computational Geometry, Université de Rennes 1 Research Interests: His work bridges computational methods with biological applications, including protein structure determination using NMR data and optical computing. He also explores human motion retargeting and geometric algorithms for adaptive maps. Simon emphasizes practical implementations, such as GPU-based solutions for 1D Distance Geometry problems. Publications: His articles span computational geometry, bioinformatics, and algorithm design, with contributions to journals like Journal of Optics and conferences such as SOCG, GSI, and BIBM. Key themes include optical processors for matrix operations and geometric algorithms for skeletal posture analysis. Awards: No scientific awards explicitly mentioned. Advising/Grants: No formal advising roles or grants detailed in the text. Labs/Teams: Developed the UNESCO World Heritage Collect app with collaborators, though no formal university lab affiliation is noted.

Go SugimotoView profile

Research Fellow

• Linked Data
• Entity Linking
• Data Integration
• +4 more

Go Sugimoto holds multiple Visiting Fellow positions at Vrije Universiteit Amsterdam across key research units: Faculty of Science Business Web and Media Network Institute Intelligent Information Systems His research bridges Computer Science and Digital Humanities with emphases on: Quality assessment of Linked Data infrastructure Entity Linking for cultural heritage applications Data Integration challenges in historical research NLP-driven RDF generation from biographical texts FAIR principles implementation barriers Publication analysis reveals consistent focus on empirical evaluation of semantic web tools for humanities contexts, particularly examining data connectivity gaps in cultural heritage systems. His work demonstrates methodical investigation of real-world tool limitations while advancing practical integration frameworks for heterogeneous historical datasets. No major scientific awards are documented in available records. Information regarding student advising or research grant management is not disclosed in current profiles. Sugimoto operates within VU Amsterdam's Network Institute and Intelligent Information Systems ecosystem, leveraging interdisciplinary collaboration between computer science and humanities scholars to address data infrastructure challenges in cultural heritage domains.

• Genetics
• Neuroimaging
• Brain Development
• +7 more

Sander Lamballais Tessensohn is a Researcher in the Department of Clinical Genetics at Erasmus MC, a leading academic medical center in the Netherlands. His work bridges genetics, neuroscience, and population health, with a focus on understanding the genetic and environmental factors influencing brain development and psychiatric conditions. Institution: Erasmus MC Department: Clinical Genetics Role: Researcher His research interests include genetics, neuroimaging, brain development, cognitive reserve, and psychiatric disorders. He employs advanced genomic and imaging techniques to explore individual differences in brain structure and function, particularly in pediatric and general populations. The recent publications reveal a strong trend in integrating large-scale genetic data (e.g., polygenic scores, GWAS) with neuroimaging to uncover mechanisms underlying brain development, mental health, and disease risk. His work spans from psychiatric conditions like depression to broader public health issues such as artificial sweetener consumption and environmental exposures. He is actively involved in major international research consortia such as the CHARGE and ENIGMA consortia, indicating collaborative, high-impact research in genomic epidemiology and neuroimaging genetics. While no specific awards or mentoring activities are listed, his publications have been widely recognized, with mentions in news outlets and academic platforms like Mendeley and X (formerly Twitter), reflecting significant scholarly engagement. Dr. Lamballais Tessensohn contributes to interdisciplinary science by linking genetic variation with brain phenotypes and behavioral outcomes, advancing precision medicine and population neuroscience.

Mark van den BrandView profile

Professor

• Software Engineering
• Digital Twin Engineering
• Model-Driven Engineering
• +4 more

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.

• AI Ethics
• Network Analysis
• Machine Learning
• +3 more

Dr. Doina Bucur is a prominent academic in the Electrical Engineering, Mathematics and Computer Science (EEMCS) faculty at the University of Twente. Her work bridges interdisciplinary research in AI ethics, network science, and computational sustainability. She leads the DECIDE project, which focuses on transparent AI decision-making systems impacting public services, healthcare, and insurance sectors. Her research spans machine learning applications in ecological networks, fault detection in sensor systems, and multi-objective optimization in social networks. She has pioneered methods for analyzing microbial community structures (e.g., gFlora), automated fault tree learning, and bias reduction in AI systems. Grants & Collaborations: Recipient of NWA ORC funding (2024) for the DECIDE initiative. Collaborates with over 50 researchers across disciplines including computer science, philosophy, law, and psychology. Active in developing evolutionary algorithms for network vulnerability analysis and multi-objective optimization. Labs & Teams: Core member of the Twente AI Ethics Lab and the Network Science Research Group. Leads interdisciplinary teams addressing real-world challenges through computational methods.

Remco W. van der HofstadView profile

Professor

• Probability theory
• Statistics
• Complex networks
• +7 more

Remco W. van der Hofstad is a Full Professor and Chair of Probability at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). He also serves as Scientific Director of EURANDOM (Dutch center for statistics, probability theory, and stochastic operations research) and co-directs the 'Random Spatial Structures' program at EURANDOM. His research focuses on probability theory, statistics, and their applications to complex networks including social media systems, with specific interests in percolation, random graphs, self-interacting random processes, and interdisciplinary applications in electrical engineering, computer science, and theoretical physics. Key Affiliations: Eindhoven University of Technology (TU/e) EURANDOM Gravitation Program NETWORKS Platform Wiskunde Nederland (spokesman) Research Highlights: He investigates probabilistic models for complex networks, combining theoretical mathematics with real-world applications. His work connects statistical physics concepts (e.g., percolation theory) with network science challenges in areas like glioma patient neuroimaging and pandemic transmission modeling. Scientific Recognition: Recipient of prestigious awards including the Prix Henri Poincaré (2003), Rollo Davidson Prize (2007), and NWO VIDI/VICI grants. His research contributes to UN Sustainable Development Goals related to scientific innovation. Collaborative Leadership: Active in interdisciplinary projects like the Gravitation Program NETWORKS and the Dutch Mathematics Platform. He also contributes to public dissemination through roles as Editor-in-Chief of the Network Pages community website.

Ondřej RokošView profile

Assistant Professor

• Computational Mechanics
• Materials Engineering
• Multiscale Modeling
• +4 more

Ondřej Rokoš is an Assistant Professor in the Mechanics of Materials section at Eindhoven University of Technology's Department of Mechanical Engineering. He obtained his PhD in Civil Engineering from Czech Technical University in Prague (2014) and joined TU/e as faculty in 2020 after postdoctoral positions at CTU, TU/e, and University of Luxembourg. His research focuses on multiscale phenomena in materials engineering, particularly computational homogenization techniques for metamaterials. Research Interests: Rokoš specializes in computational methods for bridging scale-dependent material behaviors, including: Mathematical modeling of microstructure-property relationships Homogenization techniques for metamaterials and inelastic materials Quasicontinuum methods and molecular statics simulations Experimental-computational approaches using digital image correlation Stochastic modeling of structural dynamics His work aims to enable optimal material design through fundamental understanding of cross-scale physical phenomena. Publication Trends: Rokoš's 52+ publications show evolving focus from fundamental homogenization methods (2016-2019) toward machine learning applications in materials design (2020-2025). Recent work integrates graph neural networks, reduced-order modeling, and experimental mechanics for programmable metamaterials. Key domains include mechanical metamaterials (67% of works), computational homogenization (47%), and multiscale engineering (45%). Teaching & Service: Courses taught include: Solid Mechanics (since 2013) Machine Learning for Multi-Physics Modelling (since 2020) Computer Aided Engineering (since 2013) He leads Group Rokos and collaborates internationally on metamaterials research. Media frequently cites his work on micromechanical parameter identification techniques.

• Machine Learning
• Data Mining
• Matrix and Tensor Factorisation
• +6 more

Dr. Wentian Fu is a Guest Researcher at the Leiden Institute of Chemistry (LIC) within Leiden University. His affiliation is under the department of Catalysis and Surface Chemistry, part of the Fundamental Research in Energy & Sustainability group. He holds a strong focus on interdisciplinary research at the intersection of computer science and applied chemistry. Research Interests: Machine Learning and Data Mining techniques, including tensor factorisation for intrinsic pattern discovery. Multi-clustering methodologies and cluster validation through filtering/ranking approaches. Unsupervised feature selection in data streams and graph/network analysis (e.g., blockmodelling for core-periphery structures). Social computing applications like itinerary recommendation systems and role discovery in online forums. Cloud security innovations, particularly preventing co-residency attacks via VM allocation strategies. Teaching: Involved in teaching "Leren onderzoeken 1" for the BSc MST program. Technological Contributions: Proposes frameworks balancing cloud security, power efficiency, and job efficiency. His work on graph clustering has applications in marketing, systems abstraction, and bioinformatics.

• Machine Learning
• Natural Language Processing
• Language Modelling
• +7 more

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.

Myroslav KavatsyukView profile

Assistant Professor

• Particle Physics
• Nuclear Physics
• High Energy Physics
• +3 more

Myroslav Kavatsyuk is an Assistant Professor in the Faculty of Science and Engineering at the University of Groningen, affiliated with the Nuclear Energy — Energy and Sustainability Research Institute Groningen. His research focuses on high-energy particle physics, particularly using the BESIII detector at the BEPCII collider. He specializes in studying rare decays of particles like charmed baryons, branching fractions, and resonance structures in electron-positron collisions. His work includes analysis of data from the BESIII collaboration, contributing to precision measurements in quantum chromodynamics and exploring new physics through non-open charm hadron interactions. He has co-authored over 600 publications, with recent studies on topics such as proton inelastic scattering, coupled-channel resonance analysis, and decay asymmetry measurements. His research interests span particle physics, nuclear physics, and collider experiments, with a focus on advancing understanding of fundamental interactions and particle dynamics. He collaborates internationally on projects like the WASA-FRS HypHI experiment and contributes to detector development for high-energy physics applications.

Federico PianzolaView profile

Assistant Professor

• Digital Social Reading
• Computational Literary Studies
• Cultural Analytics
• +4 more

Federico Pianzola is an Assistant Professor in Computational Humanities at the Faculty of Arts, University of Groningen. He leads the ERC Starting Grant project GOLEM, focusing on narrative evolution through graphs and ontologies. His research bridges literary studies with computational methods, examining digital social reading platforms like Wattpad and fanfiction communities. He serves as Vice President of IGEL and on the scientific board of OPERAS. Research interests include narrative absorption models, virtual reality's impact on reading, and cultural analytics. He explores how readers interact with digital texts, using mixed-methods approaches. Notable projects include analyzing reader engagement on social platforms and developing ontologies for narrative representation. Recent publications (2024) highlight cross-cultural reader response analysis, gender dynamics in fanfiction, and knowledge graph applications for literary studies. His work integrates computational tools with humanities scholarship to advance understanding of narrative evolution and digital reading practices. Grants: ERC Starting Grant (GOLEM) Affiliations: OPERAS Scientific Board, IGEL Vice President Key Projects: GOLEM-Knowledge Graph, Linked-Potter Ontology

Andreas van CranenburghView profile

Assistant Professor

• Computational Linguistics
• Statistical Parsing
• Computational Literary Studies
• +3 more

Dr. Andreas van Cranenburgh is an Assistant Professor of Digital Humanities and Information Sciences at the University of Groningen, Faculty of Arts. His work focuses on computational linguistics, statistical parsing, and computational literary studies, with expertise in information science, language & linguistics, and artificial intelligence. He leads projects on historical text normalization, authorship attribution, and narrative analysis frameworks like the GOLEM Triple Store. His research integrates NLP techniques with literary analysis, addressing topics such as gender bias in literary prizes, coreference resolution in Dutch literature, and psycholinguistic applications in speech disorder detection. He collaborates on corpora like OpenBoek and Dutch Novels 1800-2000, advancing digital humanities infrastructure. Notable contributions include developing Dutchcoref systems for literary text processing, exploring machine learning approaches to literary quality, and advancing graph-based narrative representations. His work bridges computational methods with humanistic inquiry, impacting both academic research and cultural heritage preservation.

Monique LaurentView profile

Professor

• Discrete Mathematics
• Optimization
• Polynomial Optimization
• +5 more

Monique Laurent is a Tilburg University professor and senior researcher at CWI (Centrum Wiskunde & Informatica), focusing on discrete mathematics and optimization . Her work bridges algebra, geometry, and computer science to solve complex combinatorial and polynomial optimization problems. Part-time full professor at Tilburg University (since 2009) Group leader of Networks and Optimization at CWI (2005-2016) Current member of CWI Management Team Research Focus: Semidefinite programming hierarchies, noncommutative polynomial optimization, quantum information theory, and matrix factorization. Her recent work explores applications in quantum entanglement , graph parameters , and combinatorial data analysis . Key Publications: 15+ articles from 2017-2024 address topics like copositive matrices , sum-of-squares convergence , and hypergraph optimization . Collaborations span institutions in the Netherlands, France, Germany, and the U.S. Awards: 2023 Khachiyan Prize SIAM Fellow (2017) KNAW member (2018) EUROPT Fellow (2021) Grants & Projects: Leads EU-funded initiatives TENORS (2024) and POEMA (2019), with prior NWO and Marie Curie grants. Organizes international workshops on polynomial optimization and quantum information.

Çiçek GüvenView profile

Assistant Professor

• Network Analysis
• Graph-Structured Data
• Machine Learning
• +5 more

Çiçek Güven is an Assistant Professor at the Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University. With a mathematical background in discrete algebra and geometry, she holds a PhD from Eindhoven University of Technology (2012) and has academic affiliations spanning academia and industry. Education: PhD in Mathematics, Eindhoven University of Technology Master's in Mathematics, Koç University Bachelor's in Mathematics, Koç University Her research focuses on network analysis , learning on graph-structured data , and explainable AI . She investigates how graph topology and spectral properties influence machine learning outcomes, with applications to brain networks, electrical grids, and social systems. She emphasizes socially impactful AI , contributing to projects like Child Growth Monitor (malnutrition detection) and Ilustre (Caribbean energy transition). Recent publications highlight her work in graph neural networks , higher-order network analysis , and ethical data practices . She serves as Lab Manager for the ICAI Ilustre Lab and sits on the Scientific Advisory Board for Informatics at the Lorentz Center. Scientific Awards and Grants are not explicitly mentioned in the provided texts. However, her contributions to AI ethics, interdisciplinary research, and open datasets like ARAN demonstrate significant scholarly impact.

Erik-Jan van KesterenView profile

Assistant Professor

• Data Science
• Computational Social Science
• Bayesian Statistics
• +7 more

Dr. Erik-Jan van Kesteren is an Assistant Professor in the Methodology and Statistics department at Utrecht University , where he leads the ODISSEI Social Data Science (SoDa) team. His work bridges statistical methodology with computational social science, with a focus on synthetic data, Bayesian inference, and open research infrastructure. Current research initiatives include: Developing privacy-preserving synthetic data frameworks Improving computational approaches to structural equation modeling Addressing geospatial sampling bias in volunteer-collected datasets Exploring text-based personality computing and language association tools He maintains the department's compute server infrastructure and contributes to open-source projects like ArtScraper and osmenrich. Contact via e.vankesteren1@uu.nl or erikjanvankesteren@pm.me