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Toon HuysmansView profile

Assistant Professor

• Digital Human Modeling
• Physical Ergonomics
• 3D Anthropometry
• +5 more

Dr. Toon Huysmans is an Assistant Professor at the Department of Industrial Design, Faculty of Industrial Design Engineering, Delft University of Technology. He specializes in digital human modeling, ergonomics, and 3D anthropometry, applying these to product design, healthcare, and sports equipment. His research focuses on creating personalized solutions through advanced technologies like 3D scanning, AI, and statistical shape modeling. Education: PhD in Sciences (2009), University of Antwerp Master and Bachelor in Computer Science (2003/2001), University of Antwerp Research Interests: His work bridges engineering and design, emphasizing human-centric approaches. Key areas include ergonomic product development, PPE design, and biometric data analysis for safety and comfort. Recent projects involve 3D facial anthropometry for protective gear and dynamic foot modeling for orthotics. Media & Outreach: Featured in NOS, BNR, and other outlets for studies on children’s anthropometry and helmet design. Leads initiatives to improve EU safety standards through 3D body scanning projects. Collaborations: Involved in labs such as the Center of Design for Advanced Manufacturing (CDAM) and the Physical and Ergonomics Lab. Teaches courses on Biomechanics and Human Factors.

Anton J. WijsView profile

Associate Professor

• Parallel Software Development
• Formal Verification
• Model Checking
• +4 more

Anton J. Wijs is an Associate Professor at the Eindhoven University of Technology (TU/e) , affiliated with the Model-Driven Software Engineering group within the Department of Software Engineering and Technology . His research focuses on parallel software development and leveraging parallelism to enhance formal verification techniques. He holds leadership roles as a Board Member of the European Association for Programming Languages and Systems (EAPLS) and a member of the Steering Committee of ETAPS. Academic Background: Wijs earned his PhD in Computer Science from VU Amsterdam (2007), followed by postdoc positions at INRIA (France), TU/e, and RWTH Aachen University (Germany). He transitioned to an Assistant Professor role at TU/e in 2015 and became an Associate Professor in 2023. Research Interests: His work emphasizes scalable verification methodologies using GPUs, formal analysis of concurrent systems, and tools like mCRL2 . He collaborates with industry partners such as ASML and Codeplay Software to bridge academic research with practical applications. Awards & Grants: Notable achievements include the Best Student Paper Award (FACS 2017) , the Amazon Research Award (2022) , and an NWO grant for GPU-accelerated state space analysis. He co-led the GEARS project (2017–2022) , focusing on efficient parallel model checking. Teaching: Wijs teaches courses on Programming , Program Verification Techniques , and Digital Twins of Medical Devices . He actively supervises students and contributes to international conferences as a program chair and reviewer.

Kevin Godin-DuboisView profile

Researcher

• Reinforcement Learning
• Human-AI Interaction
• Evolutionary Robotics
• +3 more

Kevin Godin-Dubois is a Researcher at the Faculty of Science, Vrije Universiteit Amsterdam (VU), with affiliations to the Network Institute. His work focuses on advancing artificial intelligence (AI) through interdisciplinary research, particularly in reinforcement learning, human-AI interaction, and evolutionary robotics. He leads projects involving modular frameworks for AI experiments and benchmark generators for agent prototyping. Research Interests: His primary areas include reinforcement learning frameworks, human-AI collaboration systems, neuroevolutionary techniques, and embodied evolution in robotics. His work bridges theoretical AI advancements with practical applications in socially adept agents and modular system design. Recent Trends: His 2024-2025 publications emphasize modular frameworks (e.g., SHARPIE and AMaze), which enable scalable experimentation and benchmarking of AI agents. Earlier work (2017-2020) explored long-term evolutionary dynamics in artificial ecosystems and plant communities, showcasing adaptability in changing environments. Collaborations: Active in international workshops (e.g., ALIFE 2024) and open-source projects (ci-group/revolve2), emphasizing reproducibility and community-driven AI development.

Koen DerksView profile

Assistant Professor

• Auditing
• Bayesian Statistics
• Open-source Software
• +3 more

Dr. Koen Derks is an Assistant Professor at Nyenrode Business University, specializing in the application of Bayesian statistics to auditing. He is affiliated with the Faculty Research Center for Accounting, Auditing & Control, where his work focuses on improving audit methodologies through statistical innovation and open-source software. His research emphasizes Bayesian approaches to audit sampling, prior distribution integration, and the development of tools like JASP to bridge gaps between auditors and statisticians. Education: MSc in Psychological Methodology from UvA, PhD from Nyenrode Business University Key Roles: Member of Nyenrode's Examination Committee for Accounting, Controlling and Tax Research interests include Bayesian statistics, auditing techniques, machine learning applications, and generative art. His publications span topics like audit efficiency, statistical evidence quantification, and software tools for auditors. Derks is also involved in open-source projects like aRtsy, contributing to generative art algorithms.

• Human Factors of Automated Driving
• Human-Machine Interface Design
• Automated Vehicles
• +5 more

Pavlo Bazilinskyy is an Assistant Professor at the Department of Industrial Design within the Faculty of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). He is also affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI) and focuses on AI-driven interaction between automated vehicles, people inside automated vehicles, and other road users, with particular emphasis on human factors and communication systems. Dr. Bazilinskyy earned his PhD in Human Factors of Automated Driving with a focus on auditory interfaces from Delft University of Technology in December 2018. His educational background includes an Erasmus Mundus Double MSc in Dependable Software Systems from the University of St Andrews and Maynooth University (2014), and a BEng in Information Technology from South-Eastern Finland University of Applied Sciences (2012). Before his PhD, he had a background in software engineering and computer science. His research centers on Human-Machine Interface design for automated vehicles, specifically examining how automated vehicles communicate their intentions to vulnerable road users through various modalities. He investigates external communication systems, pedestrian-vehicle interactions, auditory interfaces, and the psychological aspects of human interaction with autonomous transportation. He is a strong advocate for open science practices in automotive research, having served as a data champion at TU Delft. His recent publications demonstrate a clear progression toward more sophisticated AI-driven approaches for traffic scenario generation, multi-modal interaction design, and holistic HMI frameworks that bridge in-vehicle and external communication systems. His work combines virtual reality experiments, real-world data analysis, and innovative approaches to making automated vehicles more understandable to human road users. His notable scientific achievements include: Applied Ergonomics Journal Best Paper award (2021) Convergence Health and Technology Open Research award (2021) Open Science award (2020) Dr. Bazilinskyy has supervised multiple research projects and teaches courses related to automotive societal factors and conversational design with multi-modalities. His research has been supported through various grants, including his initial position as a Marie Curie Fellow at TU Delft. He has also organized significant events such as the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2023) and serves as treasurer of the Marie Curie Alumni Association.

Burcu Kulahcioglu OzkanView profile

Assistant Professor

• Formal Methods
• Software Testing
• Concurrent Systems
• +7 more

Burcu Kulahcioglu Ozkan is an Assistant Professor and Delft Technology Fellow at the Delft University of Technology (TU Delft) Software Engineering Research Group (SERG). Her research bridges formal methods with software engineering to enhance the reliability of concurrent and distributed systems , with applications in blockchain systems and graph databases . Recipient of Amazon Research Award (2022) and Stellar Academic Research Grant (2023) Founder of the FORSE Lab , focusing on lightweight formal methods for software engineering Co-PI of Ripple’s UBRI program at TU Delft, targeting blockchain testing Co-leader of the TU Delft-JetBrains AI4SE collaboration track Her research spans model checking , debugging , and fuzz testing , addressing challenges in event interleavings and network faults in distributed systems. She has received multiple best paper awards , including OOPSLA’23 Distinguished Paper and ICGT’25 Best Software Science Paper . She serves on program committees for major conferences like ICSE’26 , CAV’25 , and ECOOP’25 , and has delivered invited talks at VLDB Summer School (2025), Sabanci University (2024), and Dagstuhl Seminars (2023).

Mairieli WesselView profile

Assistant Professor

• Software Engineering
• Computer Supported Cooperative Work (CSCW)
• Open Source Software (OSS)
• +3 more

Dr. Mairieli Wessel is an Assistant Professor at the Department of Software Science, Radboud University (Netherlands) , focusing on the intersection of Software Engineering (SE) and Computer Supported Cooperative Work (CSCW) . Her research explores how software bots can enhance collaboration in Open Source Software (OSS) communities. PhD in Computer Science from University of São Paulo (USP) (2021) Postdoctoral Researcher at Delft University of Technology (2021-2022) Visiting PhD Student at Concordia University (2021) Her recent work examines GitHub Actions , code review bots , and bot comment analysis . She serves as Guest Editor for IEEE Software and chairs tracks at MSR and CHASE conferences. 2024 Best Runner-up Paper (AST) 2024/2022 Distinguished Reviewer Awards (ICPC, MSR) 2020 IEEE TCSE Distinguished Paper Award (ICSME) Dr. Wessel actively supervises PhD students and develops tools like BotHunter for bot detection. Her practical experience includes software development roles at IBM Research and 4Makers STEM Education .

• Software Engineering
• Requirements Engineering
• Conceptual Modeling
• +7 more

Dr. Sergio Espana Cubillo is an Assistant Professor at Utrecht University within the Software Production research group. His work focuses on Requirements Engineering , Conceptual Modeling , and Software for Organizational Responsibility . He leads the Show your Heart project for sustainability reporting and contributes to SCENTISS (2023-2027) to support social entrepreneurship.

• Automated Software Testing
• Artificial Intelligence
• Program Verification
• +5 more

Dr. Wishnu Prasetya is an Assistant Professor at Utrecht University's Faculty of Science, Department of Software Technology. His research focuses on automated software testing, artificial intelligence applications in testing, program verification, and game technology. He leads projects including iv4XR (AI-enhanced automated testing) and IMPRESS (gamification in software engineering education), with demonstrated applications in computer game testing and extended reality systems. Research Interests: Prasetya's work spans automated testing methodologies enhanced by AI, formal verification techniques, and gamification. Key domains include: Agent-based testing frameworks for complex systems Model-based verification under uncertainty Player experience modeling in games Automated test generation for Java and XR systems Gamification of software engineering education Publication Trends: His recent articles (2020-2025) show strong focus on AI-driven testing approaches for games and extended reality, with innovations in agent-based test automation, formal verification of player experiences, and industrial applications of testing frameworks. Over 80% of recent publications involve multi-agent architectures or model-based validation techniques. Educational Contributions: Teaches courses on Program semantics and verification and Software testing and verification Developed gamification tools to improve student engagement in software engineering courses Tools & Frameworks: iv4XR/Aplib: Agent-programming testing framework T3: Automated testing tool for Java APSL: Protocol testing tool for complex systems

• Multi-core embedded systems
• System-level design
• Modeling and simulation
• +7 more

Andy D. Pimentel is a Full Professor at the University of Amsterdam, where he chairs the Parallel Computing Systems (PCS) group within the Systems and Networking Lab at the Informatics Institute. His research focuses on multi-core and multi-processor computer systems, with emphasis on design, programming, and run-time management. Dr. Pimentel earned his PhD in Computer Science in 1998 and MSc in Computer Science in 1993, both from the University of Amsterdam. His educational background laid the foundation for his extensive work in computer architecture and embedded systems. His research interests span a wide range of topics including multi-core embedded systems, system-level design and simulation, design space exploration, performance and power analysis, system dependability, hardware/software co-design, run-time resource management, and Edge AI. His work consistently addresses the extra-functional aspects of computing systems such as performance, energy consumption, and system dependability, while also considering the productivity of designing and programming these complex systems. Analyzing his recent publications reveals a clear trajectory toward sustainable and efficient computing systems. His work has evolved from foundational research in embedded systems design space exploration to cutting-edge research in Edge AI, distributed deep learning, and energy-efficient computing. His publications demonstrate strong interdisciplinary connections between computer architecture, artificial intelligence, and sustainable computing. IEEE CEDA Outstanding Service Recognition Award DATE Fellow Award Professor Pimentel has held significant leadership roles in the academic community, serving as Chair of the Board for the Advanced School for Computing and Imaging (ASCI) since 2021, and as a Board member of ICT Research Platform Nederland (IPN) since 2020. He has organized major conferences including serving as General Chair for Design Automation and Test in Europe (DATE) 2024 and IEEE/ACM Embedded Systems Week 2026. His extensive service to the community demonstrates his leadership in the field of computer architecture and embedded systems. At the University of Amsterdam, Professor Pimentel leads the Parallel Computing Systems group, which investigates the design, programming, and run-time management of multi-core and multi-processor systems. The group's research emphasizes modeling, analysis, and optimization of performance, power/energy consumption, and system dependability, while also focusing on improving the productivity of designing and programming these complex systems.

G. GousiosView profile

Assistant Professor

• Software Engineering
• Empirical Software Engineering
• Software Analytics
• +7 more

G. Gousios is an Assistant Professor in the Department of Software Technology at the Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. He previously held an assistant professor position at Radboud Universiteit Nijmegen from January 2015 to July 2016. His research focuses on empirical software engineering, software analytics, and big data applications in software development. His research interests include: Empirical analysis of software development practices Software analytics and data-driven development Agile methodologies and sprint planning automation Code review and pull request dynamics Dependency and build system analysis Predictive modeling for project delays The recent publications of G. Gousios reflect a strong trend in applying data science and machine learning techniques to real-world software engineering problems. His work spans automated sprint planning, call graph generation, delay prediction using Bayesian models, and empirical studies on pull request decisions. These contributions highlight a focus on improving software development efficiency, reliability, and maintainability through empirical and analytical methods. His research bridges the gap between theoretical models and practical tooling in software engineering. Scientific awards received: ASE 2024 ACM SIGSOFT Distinguished Paper Award He has supervised four academic works, indicating active involvement in student mentoring and research guidance. Although specific grant details are not mentioned, his consistent publication record in top venues and dataset creation suggest successful engagement in research funding and collaborative projects. He collaborates extensively with prominent researchers such as Arie van Deursen and Emad Shihab. G. Gousios contributes to the software engineering research community through dataset sharing, such as the 'Catcher' dataset for API misuse detection, promoting reproducibility and open science. His work is integrated into both academic and industrial software development contexts, emphasizing practical impact.

• Learning Analytics
• Computational Psychometrics
• Adaptive Learning Systems
• +7 more

Dr. Matthieu Brinkhuis is an Associate Professor at the Faculty of Science , Utrecht University , with a focus on Software Technology for Learning and Teaching . He serves as Director of Education for the Department of Information and Computing Sciences and is a Senior Fellow at the Center for Academic Teaching and Learning. His work bridges Learning Analytics , Computational Psychometrics , and Human-Centered AI . Research Themes : AI Labs, Governing the Digital Society, Human-Centered Artificial Intelligence Technical Expertise : Data Science, Learning Analytics, Adaptive Learning Systems, Process Mining His scholarly output includes pioneering methods like the Urning Algorithm for dynamic ability tracking and Federated Learning Analytics balancing privacy and performance. He leads the Utrecht Platform for Applied Data Science and contributes to Higher Education Research through large-scale online learning systems. Notable collaborations include projects on Privacy in SMEs , E-Assessment Validation , and Cognitive Diagnostic Models for actionable educational feedback. His work has been published in venues such as the British Journal of Mathematical and Statistical Psychology and Frontiers in Education .

• Artificial Intelligence
• Data Mining
• Process Mining
• +5 more

Bernard P. Veldkamp is a Full Professor at the University of Twente, affiliated with the TechMed Centre, Digital Society Institute, and Cognition, Data and Education. His work spans Artificial Intelligence, Data Mining, Text Mining, and Human AI Interaction, with applications in Health and Well-being, Education, and Societal Context of AI. Key research areas: Machine Learning, Natural Language Processing, Psychometric Models, and Sustainable Development Goals (SDGs) His recent publications focus on energy transition, PTSD detection, reading fluency assessment, and fraud reporting. He has secured grants like the CZ Research Grant (2009) and ECABO Grant (2013). Scientific Awards : Best Paper Presentation Award (2012) CZ Research Grant (2009) ECABO Grant (2013) His collaborative activities include oral presentations on therapy analysis, formative assessment, and text classification, reflecting interdisciplinary engagement with psychology, education, and computational methods.

Lina Ochoa VenegasView profile

Assistant Professor

• Software Engineering
• Cloud Computing
• Software Architecture
• +3 more

Lina Ochoa Venegas is an Assistant Professor in the Department of Software Engineering and Technology at Eindhoven University of Technology (TU/e), affiliated with the EAISI High Tech Systems initiative. Her research focuses on software architecture, API design, software evolution, and dependency analysis, with a particular emphasis on improving software quality, maintainability, and developer productivity. She holds a Ph.D. in Computer Science and has taught courses such as Programming, Programming for Applied Physics, and Software Evolution. Her work bridges theoretical advancements and practical applications in software engineering, including studies on breaking changes, architecture recovery, and educational technology for young learners. Her research interests span software architecture communication, API security, multi-lingual system dependencies, and the impact of breaking changes in open-source ecosystems. Notable contributions include the BreakBot tool for analyzing breaking changes and systematic reviews on harmful API uses and repair techniques. Awards: Best Teacher Award (Bachelor Data Science 2024), VERSEN PhD Award (2024). Grants & Projects: Supervision of 12 academic works and contributions to datasets on software evolution challenges. Labs & Teams: Active in TU/e’s EAISI initiative, collaborating on high-tech systems research.

Katja TumaView profile

Research Fellow

• Cybersecurity
• Threat Analysis
• Security Design
• +7 more

Katja Tuma is a Visiting Research Fellow at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Computer Systems department and the Network Institute. Her primary research focuses on cybersecurity, threat analysis, and secure software design. She explores the intersection of human factors, artificial intelligence, and traditional security practices to enhance system resilience. Affiliations: Faculty of Science, Network Institute, Computer Systems Key Research Areas: Threat Modeling, Security Compliance, Large Language Models in Security, Gender Dynamics in Security Training Her work emphasizes practical applications such as automated security repair, validation of security designs using data flow diagrams, and addressing human biases in security decision-making. Tuma has contributed to projects like a training framework for industrial cybersecurity skills and certification mechanisms for software supply chains. Recent publications highlight challenges in using LLMs for code generation, the role of gender in security evaluations, and improving threat analysis replication methodologies. She teaches courses on data structures for AI and security experiments, bridging theoretical concepts with real-world applications. Notable Projects: Personalized Cybersecurity Training Framework (2023), Assurance in Open Software (2020-2023) Her research also addresses emerging areas like AI-augmented security pipelines and uncertainty management in AI-driven threat intelligence, reflecting a commitment to both technical innovation and socio-technical security challenges.