Find Academic Supervisors

• Machine Learning
• Parallel and Distributed Computing
• Graph Theory
• +6 more

Giuseppe Agapito is an Associate Professor and senior research scientist at Magna Graecia University of Catanzaro, working within the Department of Law, Economics and Sociology. His academic profile spans both teaching responsibilities and active research in computational fields. His research focuses on computational intelligence techniques applied to complex real-world problems, particularly in machine learning, parallel and distributed computing, and graph theory. His work demonstrates strong interdisciplinary applications, especially in biology and omics sciences where he develops methods to leverage the vast amounts of data being produced. Agapito has published over 100 papers in international journals and conference proceedings. His recent publications show a strong focus on graph-based machine learning approaches, sentiment analysis techniques, and bioinformatics applications. His research trajectory indicates a progression from bioinformatics applications toward more general machine learning frameworks with applications across multiple domains. As an active member of the academic community, he serves as a reviewer for scientific journals and participates as chair and program committee member for national and international conferences. His editorial work for PeerJ Computer Science demonstrates his standing in the academic community. His teaching responsibilities include courses on Computer Science and Data Analysis within the Department of Law, Economics and Sociology, suggesting an interdisciplinary approach that applies computational methods to social sciences domains.

Peter BonczView profile

Professor

• Database Systems
• Column-Store Databases
• Vectorized Query Processing
• +5 more

Peter Boncz is a Professor in the special chair of Large Scale Analytical Database Systems at Vrije Universiteit Amsterdam and leads the Database Architectures (DA) research group at CWI (Centrum Wiskunde & Informatica), the Netherlands' national research institute for mathematics and computer science. He serves on the CWI management team and is actively involved in multiple research initiatives and industry collaborations. Professor Boncz is internationally recognized as a pioneer of column-store databases, introduced through his PhD project MonetDB. His research spans database architecture, query processing optimization, and analytical database systems. His work on vectorized query processing with his first PhD student Marcin Zukowski has become foundational in modern analytical databases including BigQuery, Databricks, Snowflake, and DuckDB, which has millions of monthly downloads. Current research focuses include GPU data processing, vector search optimization, confidential computing, and graph data management. Boncz's recent publications reveal strong trends toward optimizing database systems for modern hardware architectures, particularly GPUs and cloud CPUs. His work bridges theoretical database concepts with practical implementation, focusing on performance optimization through innovative data layouts, compression techniques, and hardware-aware processing. The research shows a clear trajectory from foundational database concepts toward specialized optimization for emerging hardware and application requirements. VLDB Test of Time Award 2025 (second time, previously won in 2009) CIDR Test of Time Award 2024 ACM Fellow (2022) Humboldt Research Award (2013) ICTRegie Award (2006) Boncz has co-founded six spin-off companies in data systems, including MonetDB BV, and serves as an advisor to ventures like Databricks Corp. His research is supported by multiple external funding projects including Actian Research Grants, Databricks research agreements, and Motherduck Service Agreements. He has advised numerous students, with Marcin Zukowski being notably mentioned as his first PhD student who co-developed vectorized query processing. As leader of the Database Architectures research group at CWI, Boncz oversees a team focused on pushing the boundaries of database technology. The group maintains close ties with industry through projects with Databricks, Motherduck, and RelationalAI, while continuing to develop open-source technologies like DuckDB. The team is particularly active in GPU acceleration, confidential computing, and graph data management through the Linked Data Benchmark Council (LDBC), which Boncz founded.

Stephen McCombieView profile

Professor

• Maritime Cyber Threats
• Cyber Crime
• Digital Forensics
• +4 more

Stephen McCombie is a Professor of Maritime IT Security at NHL Stenden University of Applied Science. With a 14-year career as a Detective in the NSW Police, he co-founded their first computer crime investigation team. He holds certifications including CISSP, CFE, and ISSMP. His research focuses on maritime cyber threats, digital forensics, and cyber conflict, with particular emphasis on Eastern European cyber crime groups and ransomware dynamics. He has held leadership roles at IBM, National Australia Bank, and RSA Security while maintaining academic engagement at institutions like Macquarie University and National University of Singapore. Education: PhD thesis explored Eastern European cyber crime impacts on Australian banks. Notable contributions include ship honeynet projects for maritime threat intelligence and blockchain analysis for illicit Bitcoin transactions. His work bridges law enforcement, cybersecurity, and academia, addressing modern challenges like election interference and geopolitical cyber conflicts. Research interests span cyber threat intelligence, maritime security systems, and ransomware mitigation strategies. He actively publishes on topics such as dark net operations, cyber deception tactics, and cryptocurrency forensics. Grants and collaborations focus on improving maritime cybersecurity frameworks and international threat information sharing mechanisms.

MH MorrenView profile

Assistant Professor

• Natural Language Processing
• Meta-Analysis
• Methodological issues
• +3 more

Dr. MH Morren is an Assistant Professor of Marketing at Vrije Universiteit Amsterdam since 2012, specializing in analytical methods ranging from latent variable models to large language models. As Scientific Coordinator of Sustainability, she fosters interdisciplinary collaboration to address climate challenges. Affiliated with Health Economics Research Amsterdam (HERA) and Amsterdam Sustainability Institute (ASI), her research focuses on sustainable consumer behavior, policy impacts, and environmental legitimacy of brands. Teaching includes Natural Language Processing, Data Analysis in R, and Marketing Research Methods. Her work contributes to UN Sustainable Development Goals related to climate action and responsible consumption. Recent research explores prompts vs fine-tuning for value detection (2024), reinforcement learning for database optimization (2023), and cross-cultural behavioral theories (2021). She leads the 'Connecting Climate Discourse and Religious Language' project, integrating NLP with social sciences. No scientific awards explicitly mentioned. Supervises master's theses and teaches across undergraduate and graduate programs. Active in methodological innovations for sustainability research.

Anton FeenstraView profile

Associate Professor

• Structural Bioinformatics
• Protein Structure Prediction
• Computational Biology
• +3 more

Anton Feenstra is an Associate Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Bioinformatics department, as well as AIMMS and Integrative Bioinformatics. He holds a PhD (dr.) and an engineering degree (ir.). His research focuses on structural bioinformatics, protein structure prediction, computational biology, and bioinformatics algorithms. Key interests include protein-protein interactions, molecular dynamics, and knowledge graph applications in health and microbiota studies. Feenstra leads projects such as ELIXIR-NL (Digital Research Infrastructure) and has contributed to initiatives like BIOEXE and ENFIN. He teaches courses including Algorithms in Sequence Analysis and Fundamentals of Bioinformatics. His work spans over 86 publications, with recent contributions on protein interface prediction (PIPENN-EMB), microbiota-gut-brain axis analysis, and structural bioinformatics tools. Notable achievements include developing the PRALINE alignment toolkit and advancing machine learning methods for protein function prediction. Collaborations include work on Mycobacterium tuberculosis and SARS-CoV-2 protein analysis. His research aligns with UN Sustainable Development Goals, particularly in health and innovation.

Shiwei LiuView profile

Research Fellow

• Neural Networks
• Sparsity
• Subnetworks
• +6 more

Shiwei Liu is a Newton International Fellow at the University of Oxford, specializing in sparsity within neural networks. Previously, he earned his Ph.D. (cum laude) from Eindhoven University of Technology in 2022, with his thesis receiving the Informatics Europe Best Dissertation Runner-up Award in 2023. His research impacts efficient training/inference/transfer of large models, robustness, generative AI, and graph learning. Education : Ph.D. (cum laude) from Eindhoven University of Technology (2022) Research Focus : Shiwei's work explores sparsity in neural networks, particularly its applications to large language models (LLMs) and 3D medical image segmentation. His recent publications emphasize dynamic data pruning, neuron revitalization, and outlier-based sparsity optimization. Key Publications : His 2024 contributions include techniques for sparse feature fusion in medical imaging and layerwise sparsity in LLMs, alongside studies on pruning-induced performance degradation. Awards : Best Dissertation Runner-up, Informatics Europe (2023) Newton International Fellowship (2023) CPAL Rising Star (2023) Best Paper Award of LoG 2022 PhD Cum Laude/Best Thesis Award (2022) Collaborations : He collaborates internationally, with recent projects involving institutions in the Netherlands (Eindhoven University of Technology) and Greece (Interspeech 2024). His work spans 2016–2024, with 31 research outputs and 9 peer-reviewed articles in 2024 alone.

Dmitry BagaevView profile

Researcher

• Bayesian Inference
• Probabilistic Programming
• Reactive Message Passing
• +2 more

Dmitry Bagaev is a Researcher at Eindhoven University of Technology's Signal Processing Systems department, specializing in Bayesian inference and probabilistic programming. His work develops computational tools like RxInfer.jl and ReactiveMP.jl for scalable probabilistic modeling. Research spans Bayesian inference frameworks, reactive programming architectures, and applications in computational immunology. Recent publications focus on probabilistic programming languages (GraphPPL.jl), multi-agent trajectory planning, and SARS-CoV-2 T-cell receptor databases. Articles demonstrate consistent innovation at the intersection of computational statistics, biological data analysis, and real-time systems engineering. Awards and advising roles are not documented in available records. Research occurs within the BIASLab group, focusing on signal processing systems and probabilistic AI.

• Distributed Systems
• Serverless Computing
• GPU Programming
• +2 more

Sacheendra Talluri is a Visiting Fellow at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Network Institute. His research focuses on distributed systems, serverless computing, and GPU-accelerated data processing. Current affiliation: Faculty of Science, Computer Science Department, Vrije Universiteit Amsterdam Research areas: Distributed Systems, Serverless Computing, GPU Programming, Performance Engineering Talluri's recent work investigates large language model service reliability , serverless computing overheads , and GPU-optimized data processing . His research output includes methodologies for Kubernetes configuration analysis and serverless event triggers, contributing to cloud infrastructure optimization. He participates in the project 'Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe' (2023–2025), collaborating with researchers like Alexandru Iosup and R. van Bakel.

Ricardo da Silva TorresView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Prof. Ricardo da Silva Torres is a Professor in Data Science and Artificial Intelligence at Wageningen University & Research, Netherlands. Previously, he held professorships at NTNU (2019-2024) and the University of Campinas, Brazil (2005-2019). He earned his B.Sc. and Ph.D. in Computer Science from the University of Campinas (2000 and 2004, respectively). His research focuses on eScience projects involving multimedia analysis, machine learning, databases, and visualization. Education: B.Sc. in Computer Engineering, University of Campinas, Brazil (2000) Ph.D. in Computer Science, University of Campinas (2004) Research Interests: Multidisciplinary eScience projects Machine Learning applications in sports science and agriculture Knowledge graphs and digital twins for urban and agricultural planning Computer vision for environmental monitoring and aquaculture Notable Projects: KD-BIO: Large-scale AI infrastructure for green life sciences IUMENTA: Animal digital twin framework NorDark-DT: Urban lighting digital twin He serves as Associate Editor for Pattern Recognition Letters and is an IEEE member. His work bridges computational methods with real-world challenges in agriculture, sports, and urban systems.

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.

Dirk FahlandView profile

Associate Professor

• Process Mining
• Data Engineering
• Complex Systems Analysis
• +3 more

Dirk Fahland is an Associate Professor in Process Analytics on Multi-Dimensional Event Data at the Analytics for Information Systems group, Eindhoven University of Technology (TU/e), School of Mathematics and Computer Science. He combines formal methods with data-driven approaches to analyze complex distributed systems through event data. His current research focuses on process mining, data engineering, and multi-dimensional analysis of business processes. Academic Background: PhD from Humboldt-Universität zu Berlin and TU/e under Profs Wolfgang Reisig and Wil van der Aalst Post-Doc at TU/e on EU-funded ACSI project Research stays at Weizmann Institute, HPI Potsdam, and National University of Singapore Appointments: Assistant Professor (2013), Tenure (2016), Associate Professor (2019) Research Interests: Dirk's work centers on analyzing complex systems through event data by developing techniques for large-scale preprocessing, model synthesis, and multi-angle behavioral analysis. He explores object-centric process mining, anomaly detection, and knowledge graph applications for auditing. His research emphasizes balancing model accuracy with simplicity and integrating domain knowledge for explainable process analysis. Scientific Awards: Best Paper Award BIS 2011 Best Paper Award BPM 2011 Best Paper Award ICPM 2020 Best Paper Award ICPM 2021 Best Reviewer Award ICPM 2019 Educational Contributions: He manages the "Data Science in Engineering" Master's program, leads the "Data Challenge" course series at JADS, and teaches advanced process mining courses. He also contributes to BPMN visualization and performance monitoring education.

Raquel Garrido AlhamaView profile

Assistant Professor

• Computational modelling of language learning
• Language acquisition
• Language evolution
• +7 more

Dr Raquel Garrido Alhama is an Assistant Professor of Artificial Intelligence for text and human interaction at the Institute for Logic, Language and Computation (ILLC) , University of Amsterdam. She also co-directs the /k/omputation and Language Lab and previously served as Assistant Professor at the Cognitive Science and Artificial Intelligence department of Tilburg University. She earned her PhD from the ILLC under the supervision of Jelle Zuidema, Remko Scha and Carel Ten Cate, with a dissertation entitled Computational Modelling of Artificial Language Learning . Prior post-doctoral positions include the Max Planck Institute for Psycholinguistics and the Basque Center on Cognition, Brain and Language. Research Interests: Computational modelling of language learning and evolution Language acquisition, word learning and sentence processing Neural-symbolic integration and connectionist architectures Visual and auditory word recognition, syntactic parsing and part-of-speech tagging In recent years her work has converged on validating developmental milestones—such as the onset of productive determiner–noun combinations—using large-scale child corpora and neural models. She also investigates how multi-agent referential games can lead to emergent compositional languages, moving from image to richer graph representations. Scientific Awards: Best Article Award (2019) from Psychonomic Bulletin & Review for the review on computational models of rule learning Best Poster Award (2015) at the International Conference on Cognitive Modeling Teaching & Supervision: Dr Alhama has designed and lectured courses on advanced programming for cognitive science, computational linguistics, linear algebra and language acquisition at Tilburg University, University of Amsterdam, Radboud University and the University of the Basque Country. She currently supervises eleven MSc and BSc students in AI, Logic and Cognitive Science. Labs & Teams: She is the founding co-director of the /k/omputation and Language Lab together with Phong Le, where they use Language Emergence frameworks to explore the cognitive prerequisites for displacement and compositionality in human language.

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.