جست‌وجوی استاد و پژوهشگر

J.C. Scholtesمشاهده پروفایل

استاد

• Natural Language Processing
• Information Retrieval
• Text Mining
• +۵ مورد دیگر

J.C. Scholtes is an Extra-ordinary Professor of Text Mining at the Department of Knowledge Engineering, Faculty of Science and Engineering, University of Maastricht. He is also a Senior Research Fellow at the Dutch School for Information and Knowledge Systems (SIKS), a Board Member at IPRally, and a Venture Partner at ENDEIT Capital. M.Sc. in Computer Science from Delft University of Technology Ph.D. in Computational Linguistics from University of Amsterdam His research expertise spans Natural Language Processing, Machine Learning, and Artificial Intelligence, with applications in legal, medical, business, and regulatory domains. He focuses on text mining, machine translation, question-answering systems, and information extraction from unstructured data. Recent publications (2024–2025) emphasize context-aware machine translation, misinformation detection in recommendation systems, healthcare data analysis, and food science applications. Key themes include integrating deep learning architectures (e.g., Transformers), optimizing search and translation efficiency, and leveraging hybrid human-machine approaches. He has collaborated widely in industry and academia, notably deploying e-discovery software for institutions like the UN War Crimes Tribunals and FBI-ENRON. His career history includes leadership roles at ZyLAB (1987–2021) and prior service in the Royal Dutch Navy.

Leonie Ryvkinمشاهده پروفایل

مدرس

• Computational Geometry
• Algorithms
• Bioinformatics
• +۲ مورد دیگر

Leonie Ryvkin is a University Lecturer in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). Her teaching responsibilities include: Discrete Mathematics (since September 2022) Formal Algorithm Analysis for Premaster (since September 2019) Logic and set theory for P&T (September 2018 to August 2024) Her research spans computational geometry and algorithms with significant applications in bioinformatics. She develops geometric similarity measures for biological location data analysis, investigates free space structures for curves, and examines complexity in combinatorial systems. Her interdisciplinary work bridges computer science and biological sciences, particularly in migratory bird movement patterns and cryptochrome protein analysis. Recent publications reveal a consistent focus on geometric algorithms applied to real-world biological problems and theoretical computer science. Key themes include Fréchet distance applications, free space realizability, and computational complexity of puzzle mechanics, demonstrating strong connections between algorithmic theory and biological data analysis. She has supervised one student as indicated by "Supervised Work (1)" in her profile. Her research activities suggest active involvement in interdisciplinary projects at the computer science-biology interface, though specific grant details are not provided in available sources.

• Functional Programming
• Compiler Design
• Game Development
• +۳ مورد دیگر

Jeroen Fokker is a Lecturer at the Faculty of Science , Utrecht University , affiliated with the Department of Information and Computing Sciences and the Software Technology group. He combines academic work with freelance software development for entomological field data processing using Klasse . His research interests focus on functional programming , compiler design , and game development , particularly in educational contexts. His recent articles (2019) emphasize game programming fundamentals, UI/UX design, and C# applications in game development, reflecting a strong pedagogical approach to teaching programming through games. He occasionally provides educational advice and web design services (2×2 days/year). His publications span functional programming tools, compiler architecture, and theoretical explorations of lambda calculus and Turing's work.

Patrick Forréمشاهده پروفایل

استادیار

• Causal Inference
• Machine Learning Theory
• Information Theory
• +۴ مورد دیگر

Patrick Forré is an Assistant Professor and Lab Manager of the AI4Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. His work bridges theoretical machine learning and scientific applications, fostering interdisciplinary collaboration across informatics, mathematics, ecology, chemistry, physics, biology, and astrophysics. His research centers on mathematical foundations of machine learning including causal inference, graphical models, information theory, conditional independence structures, and geometric deep learning. He specializes in applying these techniques to scientific data problems, particularly in electro-catalysis and nitrogen fixation, where machine learning enhances molecular simulations and quantum chemical modeling. His theoretical work addresses non-linear structural causal models with cycles and latent confounders. The AI4Science Lab under his management focuses on detecting hidden patterns in scientific data through projects like electrode-electrolyte interface modeling, nitrogen-fixing coordination complexes analysis, and classical DFT neural approximations. Located in LAB42 Building at Amsterdam Science Park, the lab connects diverse scientific disciplines through machine learning innovation while organizing colloquia, workshops, and PhD defenses.

Frank van Harmelenمشاهده پروفایل

استاد

• Artificial Intelligence
• Semantic Web
• Knowledge Representation
• +۳ مورد دیگر

Prof. Frank van Harmelen is a Full Professor at Vrije Universiteit Amsterdam, holding positions in the Faculty of Science (Artificial Intelligence), Network Institute, and Knowledge Representation and Reasoning department. He also serves as an Adjunct Professor at Wuhan University of Science and Technology and Wuhan University Faculty of Information Sciences since 2014 and 2015, respectively. He earned his PhD in Artificial Intelligence from the University of Edinburgh (1985–1989), focusing on meta-level inference efficiency. His research spans Semantic Web, Knowledge Representation, Ontology Engineering, and neurosymbolic AI, with contributions to UN Sustainable Development Goals related to technological innovation. His recent work emphasizes knowledge graphs (e.g., KG4NH for nutrition/health, OfficeGraph for IoT), neurosymbolic integration, and applications in healthcare, sustainability, and education. He has led projects like the HumanE AI Network and contributed to datasets such as MetaLink and equivalence class analyses in LOD. Awards: Fellow of European AI Society (2007), Member of Royal Dutch Academy of Sciences (2017), Ten Year Impact Award (2012) Activities: Steering roles in international conferences, member of prestigious academies, and involvement in EU/US Agent Markup initiatives. Teaching: Courses on AI in Health and Introduction to AI. Grants/Projects: Principal Investigator for HumanE AI Network (2020–2024), contributed to IoT and smart grid solutions. His 292+ publications include foundational texts like the Semantic Web Primer and recent advancements in knowledge graph completion and guideline-informed reinforcement learning.

• Bioinformatics
• Genomics
• Metabolomics
• +۷ مورد دیگر

Marnix Medema is a Personal Professor in Bioinformatics at Wageningen University & Research. His research focuses on computational approaches to understand microbial natural product biosynthesis, microbiome dynamics, and plant-microbe interactions. He leads projects on gene cluster discovery, AI-driven natural product science, and microbial synthetic communities. His work bridges bioinformatics, genomics, and metabolomics. Key contributions include the antiSMASH tool for gene cluster analysis and the BGC Atlas web resource. He collaborates globally on topics like lichen biochemistry and crop microbiome resilience. He supervises multiple PhD candidates exploring topics such as bacteriophage diversity modeling and natural product discovery via AI. His research has been featured in Cell, Nucleic Acids Research, and Nature Product Reports.

• Computer Science
• Mathematical Morphology
• Data Visualization
• +۴ مورد دیگر

Jos Roerdink is a Professor at the Bernoulli Institute within the Faculty of Science and Engineering at the University of Groningen , Netherlands. His research spans multiple disciplines including computer science, mathematical morphology, data visualization, and neuroscience, with a focus on algorithm development and applications in medical imaging and astronomical data analysis.

• Multi-agent systems
• Decision-making
• Game theory
• +۷ مورد دیگر

Davide Grossi is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Bernoulli Institute for Mathematics, Computer Science, and Artificial Intelligence. He also holds an associate professorship at the University of Amsterdam's Institute for Logic, Language, and Computation. His research focuses on multi-agent systems, decision-making, game theory, social choice theory, and argumentation, with applications to digital democracy and hybrid intelligence. Grossi leads the Multi-Agent Decisions Lab (MAD-lab) and co-leads the Democratic Innovations Lab (DIL) at the University of Groningen. He has published extensively in top venues such as Artificial Intelligence , Journal of Artificial Intelligence Research , and conferences like AAMAS and IJCAI. His work bridges theoretical computer science, social choice, and legal informatics, addressing challenges in collective decision-making and democratic innovation. Research Interests: Grossi's expertise spans formal models of multi-agent systems, social choice theory, argumentation frameworks, and their applications to real-world democratic processes. He explores topics like liquid democracy, deliberative coalition formation, and the ethical implications of AI in governance. His recent work emphasizes hybrid intelligence systems that augment human decision-making with AI, ensuring transparency and fairness in automated systems. Publications: His recent articles analyze cooperation in public goods games, case-based reasoning for legal datasets, and Condorcet markets for epistemic social choice. These contributions highlight trends in AI-driven decision systems, algorithmic fairness, and computational social choice. Grossi's work on digital democracy proposes frameworks for participatory governance, while his technical papers advance methods in multi-agent learning and argumentation evaluation. Grants & Advising: Grossi leads research projects funded by the Netherlands Institute for Advanced Study (NIAS) and collaborates with institutions like the Hybrid Intelligence Center. While no specific student advisees are listed, his labs mentor graduate students in AI and social choice theory. He actively participates in interdisciplinary collaborations, integrating computer science with legal and political science perspectives. Labs & Teams: Beyond MAD-lab and DIL, he contributes to the Groningen Cognitive Systems and Materials Center (CogniGron) and the Deliberation and Argumentation Special Interest Group of Hybrid Intelligence. These groups focus on cognitive robotics, AI ethics, and deliberative mechanisms for societal challenges.

Balder ten Cateمشاهده پروفایل

دانشیار

• Logic in computer science
• Data management
• Knowledge representation
• +۵ مورد دیگر

Balder ten Cate is an Associate Professor at the University of Amsterdam, where he leads the Theoretical Computer Science research unit within the Institute for Logic, Language and Computation (ILLC). His academic journey includes previous positions at INRIA, UC Santa Cruz, Stanford, LogicBlox, and Google. He maintains active collaborations across institutions including the University of Bergen (co-advising PhD students) and Tsinghua University. Primary Affiliation: Theoretical Computer Science (TCS) Secondary Affiliation: Mathematical & Computational Logic (MCL) Office: Room L6.38, LAB42, Science Park 900, Amsterdam Dr. ten Cate's research spans diverse applications of logic in computer science and AI, with particular emphasis on data management, knowledge representation, and machine learning. His work bridges theoretical foundations with practical applications, focusing on how logical frameworks can enhance data systems and AI capabilities. He has developed significant contributions in finite model theory, database theory, and computational learning theory, with recent work exploring connections between universal algebra and learning theory. His publication record shows a strong trajectory in theoretical computer science, with recent papers focusing on query algorithms, interpolation in logical fragments, and the learnability of database queries. The research demonstrates consistent contributions at top venues including PODS, ICDT, IJCAI, and ACM Transactions journals. His work on 'Extremal Fitting Problems for Conjunctive Queries' received the ACM PODS 2023 Best Paper Award, while 'SAT-Based PAC Learning of Description Logic Concepts' earned an IJCAI 2023 Distinguished Paper Award. 2024 ETAPS Best Paper Nomination 2023 ACM SIGMOD Research Highlights Award 2023 Alberto Mendelzon Test-of-Time Award ICDT 2012 Best Paper Award 2006 EACSL Ackermann Prize for best dissertation Dr. ten Cate actively supervises PhD and MSc students, with recent theses covering topics like modal formula characterization, conjunctive queries, and temporal logic. He serves on numerous program committees including PODS 2025 and ICALP 2024, and has organized workshops on learning and logic. His current MSCA European Re-Integration Fellowship 'LLAMA: Logic and Learning: an Algebra and Finite Model Theory Approach' (2021-2025) demonstrates ongoing research leadership. He also contributes to academic service through committee memberships including the ASL Committee on Education and the editorial board of the Journal of Logic, Language and Information.

• Genetics & Heredity
• Neurodegenerative Disorders
• Mitophagy
• +۳ مورد دیگر

Dr. Kai Yu Ma is an Academic Teacher in the Department of Genetics at the University of Groningen's Faculty of Medical Sciences. His research focuses on the genetic basis of neurodegenerative and movement disorders, particularly Parkinson's disease and spinocerebellar ataxia. He employs techniques such as gene-editing (e.g., CRISPR/Cas9) and molecular genetics to investigate disease mechanisms, including mitophagy dysfunction and genetic variants' roles in neurodegeneration. Education : PhD in Molecular Genetics, University of Groningen (2022) Research Interests : His work explores gene-editing applications in understanding monogenic disorders, dopamine receptor variants linked to movement disorders, and cerebellar developmental deficits in spinocerebellar ataxia. He also examines mitochondrial pathways (e.g., PINK1/Parkin-mediated mitophagy) in Parkinson's disease pathogenesis. Publications : Recent studies highlight interdisciplinary approaches, including C. elegans models for Parkinson's origins, structural biology of coatomer proteins, and systematic analyses of genetic variants in neurodegenerative diseases. His work bridges molecular genetics, neuroscience, and structural biology. Grants & Advising : No specific grants or advising details are provided in the text.

Bettina Speckmannمشاهده پروفایل

استاد

• Computational Geometry
• Geometric Algorithms
• Spatial Data Analysis
• +۱۲ مورد دیگر

Bettina Speckmann is a full Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU Eindhoven), where she leads the Applied Geometric Algorithms group. She holds additional appointments as EAISI Health Professor and EAISI Foundational Professor, and is affiliated with the Data Science Center Eindhoven. Her research bridges theoretical algorithm design with practical applications in spatial computing. Her research interests lie primarily in computational geometry and geometric algorithms, with strong applications in GIScience, Smart Mobility (including moving object analysis and automated cartography), geo-visualization, visual analytics, and e-Humanities. She focuses on developing efficient algorithms and data structures for spatial data, combining rigorous theoretical methods with practical engineering for real-world impact. Her recent publications (2025) show a strong trend in geometric data processing, particularly in polycube segmentations, dual loop algorithms, density estimation for moving groups, and topological analysis using merge trees and Fréchet distances. These works reflect her interdisciplinary focus on computational geometry, visualization, and data structures. Scientific awards received include: Netherlands Prize for ICT Research (2011) NWO Vici Award (2012) PEriTiA Prize (2020) Bettina Speckmann has advised numerous students and researchers through her group and has secured major grants, including the NWO Vici. She has served in leadership roles such as PC co-chair for Graph Drawing (GD 2011), PC chair for ICALP Track A (2015), and PC co-chair for SoCG (2018). She teaches courses such as Data Structures and Heuristic Algorithms. She leads the Applied Geometric Algorithms group, which actively collaborates with industry partners like HERE Global B.V., Fugro NL Land B.V., and OCLC B.V., and contributes to UN Sustainable Development Goals in areas related to data and mobility.

• Computational Social Science
• Data Science
• Social Network Analysis
• +۴ مورد دیگر

Termeh Shafie is a Professor of Computational Social Science and Data Science at the University of Konstanz, Department of Politics and Public Administration. She specializes in statistical network analysis, multigraph modeling, and interdisciplinary applications across social sciences and digital humanities. Her research includes network entropy, data privacy, and archaeological network reconstruction.

Daphne Odekerkenمشاهده پروفایل

پژوهشگر

• Computational Argumentation
• Responsible AI
• Human-in-the-Loop Decision Support Systems
• +۳ مورد دیگر

Daphne Odekerken is a Researcher at the AI & Data Science department of Utrecht University , with a focus on Responsible AI . She works at the National Police Lab AI , a collaborative initiative between the Dutch National Police, Utrecht University, and TU Delft, where she implements argumentation-based AI systems for law enforcement applications. Research Areas: Computational Argumentation, Human-in-the-Loop Decision Support, Legal AI, Algorithmic Complexity, and Music Information Retrieval Her publications (2022-2024) explore stability/relevance detection in incomplete argumentation frameworks, ASPIC+ reasoning under uncertainty, and the development of the PyArg visualization tool. Key contributions include efficient algorithms for high-complexity problems and their application in police case analysis. Recent work includes Groundbreaking analysis of justification status in precedent models (2024) Complexity classifications for ASPIC+ reasoning (2024) Interactive IAF visualization systems (2024) These reflect trends in transparent AI for critical decision-making environments. Scientific recognition includes the Donald Berman Best Student Paper Award (2023) . She has developed several open-source tools: PyArg for argumentation visualization DECIBEL for audio chord estimation ForgettingWeb for knowledge base simplification and maintains a web interface for public demonstrations.

• Bayesian Networks
• Probabilistic Graphical Models
• Sensitivity Analysis
• +۴ مورد دیگر

Silja Renooij is an Associate Professor at Utrecht University's Department of Information and Computing Sciences, specializing in Artificial Intelligence and Intelligent Systems . Her work bridges Bayesian networks , probabilistic graphical models , and human-centered AI , with a focus on uncertainty quantification, sensitivity analysis, and legal reasoning applications. Current affiliation: Utrecht University Department: Information and Computing Sciences Academic rank: Associate Professor Contact: s.renooij@uu.nl Research interests include: Bayesian network construction and sensitivity analysis Probabilistic reasoning in legal and medical domains Interpretable AI through scenario-based modeling Conflict detection in black-box systems Hybrid human-AI reasoning frameworks Probability elicitation and evidence evaluation Recent publications demonstrate her focus on explainable AI through MAP-independence analysis, legal evidence modeling , and robust decision support systems . Her work often combines argumentation theory with probabilistic graphical models , emphasizing human-AI collaboration in critical domains like healthcare and law.

Anna Schenfischمشاهده پروفایل

پژوهشگر ارشد

• Algebraic Topology
• Computational Geometry
• Topological Data Analysis
• +۳ مورد دیگر

Anna Schenfisch is a Research Fellow in the Faculty of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), working within the Applied Geometric Algorithms research group. Her primary affiliation is with the university's mathematics department, and she can be contacted at a.k.schenfisch@tue.nl. Her research focuses on the intersection of algebraic topology and computational geometry, with significant contributions to topological data analysis. Her core research interests center on K-theory applications to persistence modules, simplicial complex reconstruction, and topological descriptors. She investigates how algebraic structures like monoids and parameter spaces interact with geometric representations, particularly through zig-zag persistence frameworks. Her work on faithful sets of verbose persistence diagrams addresses fundamental questions about minimality and optimality in topological data representations. Current projects involve developing theoretical frameworks for multiparameter persistence modules and their computational implementations. Analysis of her 15 most recent publications (2022-2025) reveals a strong trajectory in applying algebraic topology to computational problems. Her research demonstrates increasing sophistication in bridging abstract K-theory with practical geometric algorithms, particularly in simplicial complex reconstruction and descriptor optimization. The work consistently targets foundational questions in topological data analysis while developing novel computational approaches. Scientific Awards: No specific awards, fellowships, or medals are mentioned in the provided sources. Advising and Grants: Anna has supervised at least one academic work as indicated by "Supervised Work (1)" in her institutional profile. The nature of this supervision (e.g., thesis advising) isn't specified. No grant funding sources are explicitly referenced in the available materials. Labs and Teams: She is an active member of the Applied Geometric Algorithms research group at TU/e, which focuses on computational topology and geometric data analysis. This group serves as her primary research environment for developing algorithms related to persistence modules and topological descriptors.