Find Academic Supervisors

• Hardware Security
• Internet of Things
• Embedded Systems
• +5 more

Thibaut Vandervelden is a Researcher at Vrije Universiteit Brussel's Faculty of Engineering, Department of Electronics and Informatics. His work focuses on hardware security, embedded systems, and cryptographic implementations for resource-constrained environments. With an h-index of 4, he has produced significant research output since 2019, primarily in Sensors, IEEE Access, and Future Generation Computer Systems. His research interests center on practical security implementations for IoT devices and embedded systems, with particular expertise in Rust programming for secure embedded development, cryptographic protocol optimization, and network security for low-power wireless networks. His work bridges theoretical security concepts with practical implementation challenges in constrained environments. Analysis of his recent publications reveals a strong trend toward secure embedded systems development, with increasing focus on Rust programming language applications, zero-knowledge proofs for privacy-preserving systems, and optimization of cryptographic operations for IoT devices. His research demonstrates consistent collaboration with colleagues including Ruben De Smet, An Braeken, and Kris Steenhaut. Scientific recognition includes: IACR RWC'21 Cryptohackathon on Functional Encryption: 2nd prize (2021) Vandervelden actively participates in research community building through organizing workshops like RustIEC and presenting at events focused on embedded systems security. His work has been referenced in Wikipedia pages and garnered attention across academic and professional platforms, with media contributions on cybersecurity research in Flanders. He leads and participates in research teams focused on wireless community development and the Internet of Batteryless Things, contributing to both theoretical frameworks and practical implementations in hardware security.

Christophe VandeviverView profile

Research Professor

• Geography of crime and crime control
• Crime science
• Sexual violence
• +5 more

Christophe Vandeviver is a Research Professor of Criminology (Ghent University Research Council BOFZAP) at the Department of Criminology, Criminal Law and Social Law, Ghent University, Belgium. He also serves as an International Research Fellow at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) and was previously Co-President (2021-2023) and Fellow (2019-2024) of the Young Academy of Belgium. His academic journey includes a PhD, Master's and Bachelor's degree in Criminology from Ghent University, and a Master of Science in Quantitative Analysis in the Social Sciences from KUB-HUBrussel. His research interests span geography of crime and crime control, crime science, sexual violence, and quantitative research methods. Vandeviver applies computational and quantitative approaches to criminological questions, with particular focus on spatial patterns of crime, criminal networks, police patrol strategies, and forensic intelligence systems. His work bridges theoretical criminology with practical applications for law enforcement and crime prevention. His recent publications demonstrate a strong focus on network analysis in criminology, exploring co-offending patterns, spatial dynamics of crime, and the application of computational methods to understand criminal behavior. He has produced significant work on police-citizen interactions, sexual violence prevention, and the impact of environmental factors on crime patterns, showing consistent methodological rigor across diverse research areas. 2024 Knack 'Jong Talent' Laureaat van de Academie 2024 Open Science Award 2023 Promotor Proximus of UGent Doctor Honoris Causa of Prof. P. Jeffrey Brantingham (UCLA) 2021 Voorzitter van de Jonge Academie 2020 Ghent University Research Council Research Professor (BOFZAP) Vandeviver has supervised numerous doctoral students across diverse criminological topics including co-offending networks, police violence, sexual assault prevention, and corruption analysis. His grant portfolio includes multiple FWO-funded projects, European framework programs, and international collaborations with institutions in the Philippines, Ecuador, and China. He leads the Computational and Quantitative Criminology lab at Ghent University and is deeply involved in the Ghent University Crime Lab and Institute for International Research on Criminal Policy. His current research focuses on developing forensic intelligence tools, studying wildlife crime in Ethiopia, and analyzing corruption tolerance in educational sectors.

Bjorn De SutterView profile

Professor

• Software Protection and Security
• Code Generation for Accelerators

Bjorn De Sutter is a Full Professor in the Computer Systems Lab within the Department of Electronics and Information Systems at Ghent University, Belgium. His research spans software protection/security (obfuscation, side channels, diversity) and code generation for accelerators like GPUs. Research interests include: Software protection mechanisms against reverse engineering and attacks Compiler techniques for heterogeneous architectures Multi-variant execution and software diversity Cryptographic key extraction and protection modeling Publications focus on software security (67%), systems (20%), and compilers (13%), with recent work in protection evaluation methodologies, GPU compilation, and diversified execution. Key trends include empirical security validation and adaptive protection techniques. Awards and honors: Barco Award for Graduation Dissertations (1998) HiPEAC Technology Transfer Awards (2013, 2018) ACM SIGSOFT Distinguished Paper & Best Paper Award at ICPC 2017 Advises 4 PhD students and 6 Master's candidates. Secured €2M+ funding from: EU FP7 (ASPIRE coordinator) FWO projects on software protection/compilers VLAIO industrial fellowships Leads the Computer Systems Lab focusing on secure compilation and architecture-aware optimization.

• Reinforcement Learning
• Artificial Intelligence
• Robotics
• +3 more

Ann Nowe is a Professor at the Department of Electronics and Informatics (ETRO) at Vrije Universiteit Brussel. Her research focuses on reinforcement learning, multi-agent systems, robotics, and energy management. She leads the Federated labs AI and Robotics, emphasizing interdisciplinary collaboration in artificial intelligence and sustainable robotics. With a prolific publication record (555+ outputs) and 152 active projects, her work spans theoretical advancements and applied solutions in AI ethics, explainability, and energy systems. Her research interests include AI security, neuro-symbolic integration, and federated robotics. Notable contributions include frameworks for explainable AI in multi-agent environments and predictive maintenance algorithms for wind turbines. She has received multiple awards, including the ALA Best Paper Award (2018) and Best Demonstration Award (2019). Dr. Nowe actively participates in conferences and workshops on trustworthy AI, co-organizing events like the World Conference on eXplainable AI (2025). Her work integrates ethical considerations and human-centric design principles across robotics, energy systems, and collective decision-making.

Dirk Van den PoelView profile

Professor

• Data Analytics
• Streaming Processing Frameworks & Analytics
• Internet of Things (IoT) & Industry 4.0
• +2 more

Dirk Van den Poel is a Professor of Data Analytics/Big Data at Ghent University, Belgium, specializing in statistical computing, social media analytics, and predictive modeling. He has co-founded pioneering academic programs including the first predictive analytics master’s program globally, the Master of Science in Statistical Data Analysis, and the Master of Science in Business Engineering/Data Analytics. Teaches courses: Statistical Computing, Big Data, Social Media Analytics Research focus: Analytical CRM, ensemble classification methods, big data 110+ ISI-indexed publications in journals like IEEE Transactions on Power Systems and Decision Support Systems His recent work explores topics such as rumor detection in social media, predictive maintenance using sensor data, and crisis response through service adaptation. He leverages machine learning and deep learning for applications in agriculture, healthcare, and energy sectors. Current projects involve topic modeling (BERTopic), credit scoring with interpretable models, and spatiotemporal crime prediction using mobile phone data. Dirk’s methodology integrates stream processing frameworks , reinforcement learning , and ensemble techniques .

• Domain-Specific Modeling Language Engineering
• Modeling & Meta-modeling
• Software Architecture Engineering
• +4 more

Vincent Englebert is a Professor at the University of Namur's Faculty of Computer Science, where he is affiliated with the Research Center on Information Systems Engineering (PReCISE) and the Namur Digital Institute (NADI). His office is located in the IT Building at Rue Grandgagnage, 21 in Namur, Belgium. He teaches courses including Organisational and Business Modelling, Software Architecture Engineering, and Software Engineering Laboratory for the 2023-2026 academic period. Bachelor and Master in Computer Science (University of Namur) Doctor of Science in Computer Science (2000), thesis: 'A smart Meta-CASE: towards an integrated solution' Englebert's research focuses on Domain-Specific Modeling Language (DSML) Engineering, Modeling & Meta-modeling, and Software Architecture Engineering, with additional interest in Computers and Aging applications. His work spans theoretical foundations of modeling languages to practical applications in business processes and aging populations. The fingerprint of his research shows strong emphasis on design (100%), engineering (78%), computer codes (70%), Reverse Engineering (63%), architecture (60%), tools (56%), modeling (55%), and transformations (50%). His recent publications (2020-2024) demonstrate continued productivity in model-driven engineering, with particular focus on software product lines, feature modeling, and applications for aging populations. These works show a clear trajectory from theoretical modeling foundations to practical implementations and societal applications. Best paper at International Conference Modelsward'2014 Englebert has supervised numerous research projects, including the current e-Anacarde: Digitalisation project (2024-2028) and previously led major initiatives like UsiXML and METADONE. He has been active in media and public engagement, particularly regarding computer science education in schools, with multiple expert commentaries in 2016. His external responsibilities have included significant leadership positions, notably as Dean of the Faculty of Computer Science (2015-2019) and Director of the PRECISE/IS research center (until 2011). He is active in research networks and collaborations, with recent external collaborations spanning multiple countries. His work with the NAM-IP Museum demonstrates ongoing engagement with cultural institutions, while his presentations on Alzheimer's disease and computer technology show commitment to applying informatics to societal challenges.

• Software Engineering
• Software Evolution
• Open Source Ecosystems
• +6 more

Tom Mens is a Full Professor at the University of Mons (UMONS), where he leads the Software Engineering Department and serves as President of the INFORTECH Research Institute. He teaches software engineering and programming languages to undergraduate and graduate students, and actively participates in doctoral training programs. His research focuses on several interconnected domains: Software Evolution : Studying long-term software system changes Open Source Ecosystems : Analyzing community dynamics and dependencies Model-Driven Engineering : Developing model-based software methodologies Software Quality : Ensuring reliability and maintainability Empirical Studies : Quantitative analysis of development practices Recent publications (2023-2025) demonstrate strong research trends in GitHub ecosystem analysis, including bot detection techniques, security vulnerabilities in CI/CD pipelines, dependency management challenges, and workflow automation studies. This work frequently involves developing specialized tools and creating benchmark datasets for empirical validation. Professor Mens has supervised multiple PhD students including N. Chidambaram (bot identification), T. Bosser (temporal event modeling), and M. Golzadeh (bot detection in social coding). He directs the Software Engineering Laboratory at UMONS and leads the INFORTECH Research Institute, fostering collaborations in software technology and complex systems research.

Doha OuardiView profile

Researcher

• Cybersecurity
• Security Tools
• Synchronous Communication
• +3 more

Doha Ouardi is a Researcher at the Faculty of Computer Science , Namur Digital Institute , specializing in Cybersecurity and Security Tools . Her work focuses on integrating security features into programming languages, particularly Scala, with a strong emphasis on synchronous communication and defense against man-in-the-middle attacks. Current Project : CYBEREXCELLENCE (2022–2027) – Walloon Region's cybersecurity initiative Key Collaborations : Collaborating with researchers like Jean-Marc Jacquet and Pierre-Yves Schobbens Research Trends : Her recent work highlights the intersection of security and software development tools , aiming to reduce vulnerabilities in programming frameworks. Activities : Actively participates in cybersecurity workshops (e.g., International CyberExcellence Workshop 2024) and doctoral events like Grascomp Doctoral Day.

Arda TezcanView profile

Assistant Professor

• Natural Language Processing
• Machine Translation
• Human-Machine Interaction
• +3 more

Arda Tezcan serves as Assistant Professor in Language Technology at Ghent University, where he bridges mathematics, artificial intelligence, and translation studies. His academic position falls within the Language and Translation Technology Team (LT3), evidenced by his involvement in multiple LT3 projects and publications. His research spans Natural Language Processing , Machine Translation , Human-Machine Interaction , and Translation Ethics , with particular focus on gender bias detection, scholarly communication, and real-world MT applications. Key themes include: Contextual analysis in translation quality assessment Ethical implications of neural machine translation Integration of fuzzy matches in specialized domains Machine translation for social impact (asylum seekers, scholarly communication) Language technology for educational purposes His 15 most recent publications (2023-2025) reveal strong emphasis on practical MT applications, with 60% addressing real-world implementation challenges in scholarly communication, asylum procedures, and language education. Gender bias analysis and domain adaptation through synthetic data represent emerging research threads. Tezcan actively leads and contributes to significant projects including: MATEO (Machine Translation Evaluation Online) MaTIAS (Machine Translation to Inform Asylum Seekers) IVESS (Intelligent Vocabulary and Example Selection for Spanish) MT evaluation for scholarly communication His teaching portfolio encompasses Advanced Language Processing with Python, Natural Language Processing, Programming, Machine Learning, and Translation Technology courses, reflecting his dual expertise in computational methods and translation studies.

Alessandra Teresa CignarellaView profile

Researcher

• Natural Language Processing
• Computational Linguistics
• Irony Detection
• +5 more

Alessandra Teresa Cignarella is a Senior Researcher at Ghent University , focusing on Natural Language Processing (NLP) . She is funded by the Marie Skłodowska-Curie Actions (2024-2026) for her project RAINBOW , which researches stereotypes towards the LGBTQIA+ community using multilingual NLP. Previously (2021-2023), she was a postdoctoral researcher at the University of Turin in the STERHEOTYPES project, analyzing racial hoaxes in Mediterranean Europe. PhD in Computer Science (NLP) (2021) from University of Turin and Universitat Politècnica de València . Co-founder of aequa-tech , an NLP startup addressing digital disparity through tools like Debunker-Assistant for fake news detection. Her research spans irony detection , stance analysis , hate speech , and multilingual NLP . Articles highlight work on datasets like TRIC (trilingual irony detection), Stereohoax (racial hoaxes), and HODIAT (homotransphobic hate speech). Her work integrates social psychology and computational linguistics to address digital disparity and ethical AI . She has received recognition through the Marie Skłodowska-Curie fellowship and has co-authored key publications in conferences like ACL 2024 and LREC-COLING 2024 . Her projects emphasize societal empowerment , including educational NLP programs for high school students.

• Computational Thinking
• Computer Science Education
• Social Robotics
• +3 more

Natacha Gesquière is a researcher affiliated with the University of Ghent (UGent), focusing on computational thinking, artificial intelligence in education, and interdisciplinary STEM learning. Her work bridges technology and pedagogy, particularly in K-12 contexts. Key research areas: Computational Thinking, Social Robotics, and AI-Driven Education Collaborates with UGent colleagues like Francis wyffels and Tom Neutens Active in developing educational frameworks and tools for teachers Her publications highlight applications of physical computing, social robotics, and universal quadratic forms in educational settings. She contributes to open-access resources and interdisciplinary teacher design teams.

Peter DawyndtView profile

Professor

• Computational Biology
• Bioinformatics
• Computer Science
• +7 more

Peter Dawyndt is a Professor in the Department of Applied Mathematics, Computer Science and Statistics at Ghent University's Faculty of Sciences, where he heads the Computational Biology Lab. His research bridges computer science and life sciences through interdisciplinary work on computational applications for (meta)genomics, (meta)proteomics, and educational data mining. The lab actively develops the Unipept platform for metaproteomics biodiversity analysis and the Dodona platform for programming education, demonstrating dual expertise in biological data science and educational technology. His research interests encompass computational biology, bioinformatics, and educational data mining with specific focus on metagenomics, metaproteomics, metatranscriptomics, and data visualization for life sciences applications, alongside learning analytics and educational technology for computer science education. The Unipept platform enables taxonomic analysis of metaproteome samples through tryptic peptide biomarkers, while the Dodona ecosystem includes tools like Dolos for plagiarism detection and Blink for Scratch debugging, all supporting active learning in coding education. Analysis of his 2023-2025 publications reveals two equally prominent research thrusts: advancement of bioinformatics tools (e.g., Unipept's expansion to non-tryptic peptides and metabolic pathway visualization) and innovation in educational technology (e.g., LLM applications for CS1 education and automated code review systems). This dual trajectory highlights his commitment to both fundamental biological discovery and transformative educational practices in computing. No scientific awards were documented in the provided source materials. While specific student advising details and grant information were not explicitly stated, his leadership of the Computational Biology Lab and extensive publication record suggest active mentorship of graduate students and researchers in developing computational tools for biological and educational applications. The Computational Biology Lab operates as an integrated team developing the Unipept and Dodona platform ecosystems. Current projects include enhancing metaproteomics analysis capabilities through missed cleavage support and expanding educational tools for plagiarism detection and virtual co-teaching, reflecting the lab's commitment to open-source software development at the computer science-life sciences education interface.

Philippe LambertView profile

Professor

• Survival Analysis
• Bayesian Statistics
• Statistical Modeling
• +4 more

Philippe Lambert is a Professor at University of Liège (ULiege) and Université catholique de Louvain (UCLouvain), with research focused on advanced statistical methodologies for complex data structures. His work spans survival analysis, Bayesian inference, and nonparametric modeling, with particular expertise in handling censored and grouped data. His research interests center on developing innovative statistical methods for challenging data scenarios. Lambert has made significant contributions to cure survival models, particularly with time-varying covariates, and has pioneered approaches for density estimation from grouped or censored data using Bayesian P-splines and Laplace approximations. His work bridges theoretical statistics with practical applications in demography, epidemiology, and health-related quality of life studies. The analysis of his recent publications reveals a strong focus on methodological advancements in survival analysis, particularly double additive models for censored data. His research demonstrates consistent innovation in computational statistics, with an emphasis on developing efficient Bayesian inference techniques that maintain statistical rigor while improving computational speed. The integration of Laplace approximations with P-splines has become a hallmark of his methodological contributions. Professor Lambert maintains an active research program with continuous publication output from 1993 through 2025, demonstrating sustained scholarly productivity. His work spans multiple disciplines including biostatistics, demography, and computational statistics, with applications ranging from fertility studies to proteomics data analysis. He has developed the DALSM R package for double additive location-scale modeling, which implements his methodological innovations for practical use by researchers.

• Semantic Web
• Knowledge Graphs
• Data Integration
• +3 more

Anastasia Dimou is an Assistant Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Technology, affiliated with the Declarative Languages and Artificial Intelligence (DTAI) research group at the De Nayer Campus in Sint-Katelijne-Waver. Her research centers on semantic web technologies and knowledge graphs, specializing in RDF mapping frameworks (RML), SHACL validation, and knowledge graph generation for machine learning applications. She develops methods for heterogeneous data integration, stream processing, and ensuring data quality through rule-based validation and inconsistency resolution. Recent publications (2019-2023) demonstrate expertise in RML ontology design, SHACL validation techniques, and systematic reviews of RDF graph generation. Key projects include 'Knowledge Graphs for Machine Learning Data Management' (2021-2023) and 'Knowledge Graphs for Data Integration' (2022-2026), focusing on scalable knowledge graph applications. Dr. Dimou serves on the Council of the Faculty of Engineering Technology, the Computer Science Department Council, and the POC Elektronica-ICT for the Faculty of Industrial Engineering Sciences, contributing to institutional governance and academic strategy. As part of DTAI, she collaborates on declarative language applications in artificial intelligence, with teaching responsibilities spanning Web AI, Data Engineering, and Knowledge Graph courses that bridge theoretical research with practical implementation.

• Context-oriented programming
• Software engineering
• Programming languages
• +8 more

Kim Mens is a Professor at Université catholique de Louvain (UCLouvain), affiliated with the Louvain School of Engineering (EPL), the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), and specifically the Pôle en ingénierie informatique (INGI). His research spans multiple areas of computer science with a particular focus on context-oriented programming, software engineering, and programming education. Professor Mens' research interests center around context-oriented programming, software evolution, and programming education. His work explores how software systems can dynamically adapt to changing contexts, with applications in self-adaptive systems, feature-based programming, and context-aware applications. In programming education, he investigates how to improve student learning through better feedback mechanisms, pattern mining in student code, and explicit instructional strategies. His recent publications (2022-2025) demonstrate a strong focus on both theoretical and practical aspects of context-oriented systems. His work spans software testing for adaptive systems, program query languages, automated feedback for programming education, and context-aware programming frameworks. His research shows a clear progression from foundational work in context-oriented programming to practical applications in software engineering and education, with increasing integration of AI techniques like BERT for educational applications. Professor Mens actively collaborates with researchers across Europe, particularly in Belgium, the Netherlands, and other European countries. His work appears in top software engineering and programming conferences and journals, demonstrating the significance and impact of his contributions to the field. His current research trajectory suggests continued innovation at the intersection of programming languages, software engineering, and computer science education.