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A. ChiumentoView profile

Assistant Professor

• distributed intelligence over networked systems
• mobile computing
• autonomous multi-agent systems
• +9 more

A. Chiumento is an Assistant Professor in the Pervasive Systems group at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente. His research focuses on developing adaptable wireless communication systems that balance user satisfaction, configurability, and durability through distributed intelligence and autonomous multi-agent frameworks. His expertise encompasses: Distributed intelligence over networked systems Mobile computing and IoT applications Autonomous multi-agent coordination Socially responsible engineering practices Energy-efficient wireless network design Deep Reinforcement Learning for system optimization

Le Viet DucView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Behavior Analytics
• +6 more

Dr. Le Viet Duc is an Assistant Professor at the Department of Computer Science, University of Twente, within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). His research integrates Machine Learning and Deep Learning into real-world behavior analytics, focusing on cognitive sensing systems that adapt to individuals and environments with minimal prior knowledge. His expertise spans embedded systems, mobile sensing, localization, and deep learning, with applications in smart cities, human/device tracking, and mental/infrastructure monitoring. He has developed innovative frameworks, including an infrastructure-independent sensing system (2012 Best Paper Award) and context detection algorithms (2014 Best Paper Award). Scientific Awards Best paper award 2012 Best paper award 2014 Dr. Duc supervises PhDs and Masters students in human and animal behavior monitoring, emphasizing location tracking and activity recognition in unstructured environments.

• Posttraumatic Stress Disorder
• Psychotraumatology
• Resilience
• +6 more

Dr. Mirjam van Zuiden is an Associate Professor in Clinical Psychology at the Faculty of Social and Behavioural Sciences, Utrecht University , specializing in psychotraumatology and PTSD research. Her work bridges clinical psychology, neuroscience, and endocrinology, focusing on precision prevention strategies for trauma-related disorders. She leads the 2-ASAP consortium , a longitudinal study developing sex-specific screening tools for PTSD using advanced statistical modeling and interdisciplinary methodologies. Her research explores three core areas: (1) psychobiological mechanisms of trauma susceptibility, (2) early risk detection via neuroendocrinological markers, and (3) oxytocin-based interventions to prevent chronic PTSD. This aligns with her involvement in the AI-aided knowledge discovery lab , integrating computational approaches with neuroimaging data from global consortia like ENIGMA-PGC. Co-investigator in Dutch Famine Birth Cohort (Hongerwinter) studies Principal investigator in HELIUS multi-ethnic urban health research Contributor to ABCD child development studies Van Zuiden serves on editorial boards for the European Journal of Psychotraumatology and Chronic Stress , and collaborates with institutions including Amsterdam UMC, Arq National Psychotrauma Center, and Erasmus Medical Center. Her teaching includes coordinating the Loss & Psychotrauma master's course and supervising clinical and research theses.

• Applied Data Science
• Human-centered Artificial Intelligence
• Machine Learning
• +5 more

Dr. Heysem Kaya is an Assistant Professor at Utrecht University's Faculty of Science , affiliated with the Social and Affective Computing research group in the Department of Information and Computing Sciences. With over 90 publications and 2600+ citations, his work bridges machine learning, speech processing, and affective computing to advance applications in mental health, child behavior analysis, and explainable AI. PhD in computational paralinguistics from Bogaziçi University (2015) Editorial board member of IEEE Transactions on Affective Computing Keynote speaker at University of Glasgow Social AI Workshop (2025) Leader of the student wellbeing task force at Utrecht's Faculty of Science (2023-2025) Research spans applied data science , human-centered AI , and computational paralinguistics , with notable contributions to emotion recognition, bias mitigation, and multimodal analysis of mental health indicators. Recent articles focus on fairness in AI, explainable models for depression severity, and synthetic data generation. Awarded six ComParE Challenges (INTERSPEECH), three ChaLearn Awards , and first runner-up in EmotiW 2015 , Kaya's work combines technical rigor with societal impact. His projects include the Turkish Audio-Visual Bipolar Disorder Corpus and federated learning for psychiatric violence prediction.

• Bayesian Networks
• Probabilistic Graphical Models
• Sensitivity Analysis
• +4 more

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.

Nicu N. GogaView profile

Researcher

• Computer Simulation
• Molecular Dynamics
• Robotics
• +5 more

Nicu N. Goga is an external researcher at the University of Groningen's Faculty of Science and Engineering, specializing in computational methods with applications spanning transportation systems, robotics, and renewable energy. His work integrates advanced simulation techniques across multiple engineering disciplines to solve complex real-world problems. Dr. Goga's research demonstrates exceptional interdisciplinary breadth, with core expertise in computer simulation and molecular dynamics. He has pioneered adaptive route optimization solutions for commercial fleets, developed affordable humanoid robotics platforms like Miha, and engineered EEG-based brain-computer interfaces for consumer electronics. His molecular dynamics work focuses on photoelectrochemical water splitting for sustainable hydrogen production, directly contributing to UN Sustainable Development Goals in clean energy and industry innovation. Analysis of his 2021-2025 publications reveals a strategic evolution from fundamental computational chemistry toward applied engineering solutions. While maintaining molecular dynamics as a foundational methodology, his recent work increasingly targets transportation logistics and human-robot interaction, showcasing a deliberate shift toward high-impact applications in commercial and assistive technologies. Affiliated with the Molecular Dynamics research group at Groningen, Dr. Goga operates within a robust collaborative network evidenced by international co-authorship patterns. His technical fingerprint spans computer simulation engineering, model checking, multiscale modeling, and distributed systems architecture, reflecting a versatile computational toolkit applied across diverse problem domains.

Daniel ScalenaView profile

Researcher

• Computer Science
• Large Language Models
• Language Modeling
• +3 more

Daniel Scalena is an academic researcher active in interdisciplinary domains spanning Computer Science and Arts and Humanities . His work focuses on Large Language Models (LLMs) , Language Modeling , and Subjective Disagreement analysis, with a theoretical approach to model generation and intermediate representations. Key research areas: Computer Science (100%), Arts and Humanities (100%) Active in Language Modeling (100%), Model Generation (100%), and Subjective Analysis (100%) Recent publications include: 2024 : Conference contribution to BlackboxNLP Workshop on Multi-property Steering of Large Language Models (27 pages) 2023 : Conference contribution to SemEval-2023 Task 11 on Subjective Disagreement (9 pages) Collaborations include the Association for Computational Linguistics (ACL) and ACL Anthology.

Mostafa Hadadian Nejad YousefiView profile

Researcher

• Machine Learning
• Distributed Systems
• Service-Oriented Computing
• +5 more

Mostafa Hadadian Nejad Yousefi is a Researcher at the Faculty of Science and Engineering , University of Groningen , specializing in Distributed Systems . His work bridges machine learning and distributed computing, focusing on adaptive systems and digital twins. His research interests include: Machine Learning Service-Oriented Computing Neural Networks Distributed Systems Digital Twins Big Data Bioinformatics Recent publications highlight his work on: Digital twin architectures for water distribution networks (2024) Self-adaptive service selection in ML pipelines (2024) Service-oriented principles for ML development (2023) Dynamically reconfigurable distributed pipelines (2021) Neural network-based genomics analysis (2021) Contact: m.hadadian@rug.nl

Julia BerezutskayaView profile

Assistant Professor

• Brain-Computer Interfaces
• Neurotechnology
• Decoding Brain Signals
• +4 more

Julia Berezutskaya serves as an Assistant Professor at Utrecht University within the Brain strategic program, leading the Utrecht-BCI lab dedicated to developing neurotechnology for individuals with motor impairments. Her research focuses on creating brain-computer interfaces (BCIs) to enable communication and computer control for people with severe movement disabilities, emphasizing patient-centered artificial intelligence applications in medical care. Her primary research areas include decoding neural signals for BCI applications, developing specialized BCI software and interactive games, and integrating functional electrical stimulation to reanimate paralyzed muscles. This work bridges fundamental neuroscience with clinical neurotechnology, targeting neurological and psychiatric disorders through innovative neuroengineering solutions that translate brain activity into actionable outputs for rehabilitation. Recent publication trends reveal a strong emphasis on electrode optimization for sensorimotor cortex recordings (2024), hybrid BCI-FES systems for motor restoration (2023), and advanced deep learning models for speech reconstruction from neural data (2023). These contributions demonstrate consistent progress in making BCIs more reliable, clinically applicable, and responsive to user needs across neurorehabilitation contexts. The Utrecht-BCI lab operates at the intersection of neuroscience, engineering, and AI, with a mission to elucidate neuronal mechanisms underlying healthy brain function to develop transformative neurotechnologies. Current projects focus on enhancing BCI accessibility through user-centered design while addressing ethical considerations in patient-centered AI deployment for neurological care.

• Artificial Intelligence
• AI & Law
• Computational Argumentation
• +9 more

Prof. Floris Bex (Utrecht University) is a leading scholar in AI & Law, focusing on explainable AI , computational argumentation , and responsible AI development . He holds a dual appointment at the Molengraaff Institute for Private Law and Department of Information and Computing Sciences , bridging legal theory with technical AI innovation. His work emphasizes transparency in high-risk domains like policing and judicial decision-making. Scientific Director, National Police Lab AI Member, Board of Advisory Scientific Research (WODC), Ministry of Justice Editorial roles in Artificial Intelligence and Law and Argument and Computation His research combines symbolic and subsymbolic AI to create systems that reason with legal arguments, including projects like ALGOPOL and the Formal Explanations with Computational Argumentation framework. He has developed an explainable AI system for crime report assessment that improves citizen trust through transparent rejections. Key publications span 2016–2024 , with recent work on argumentation-based explanations , incomplete evidence handling , and human-AI collaboration in policing . His projects integrate Bayesian networks with argumentation theory to address real-world challenges in digital justice and public safety. Scientific awards include: Editorial board member, Artificial Intelligence and Law Scientific Advisor to Dutch National Police Lead in ICAI network collaborations He teaches Computational Argumentation and Law and Computer Science , and has supervised numerous AI & Law projects involving students like Daphne Odekerken and Anton Borg.

• Natural Language Processing
• Image Captioning
• Multimodal Dialogue
• +5 more

Dr. Ece Takmaz is a Researcher at the Department of Languages, Literature and Communication , Utrecht University, affiliated with the Humanities and Institute for Language Sciences . She works in the Language, Logic and Information division under Jakub Dotlacil and Albert Gatt, focusing on Natural Language Processing , Multimodal Dialogue , and Eye-Tracking research. PhD, Institute for Logic, Language and Computation (ILLC), University of Amsterdam (2019-2024) M.Sc., Artificial Intelligence, University of Amsterdam (2017-2019) M.Sc., Cognitive Science, Middle East Technical University B.Sc., Computer Engineering, Bilkent University Her research investigates the intersection of vision and language integration , with a focus on eye-tracking and perception-action dynamics . Her work includes developing multimodal contextual models , analyzing CLIP's cognitive plausibility , and studying human signal variation in visuo-linguistic tasks. She has contributed to frameworks for audience-aware dialogue adaptation and transformer-based reading behavior prediction . Recent publications cover topics like semantic violation analysis (COOCO dataset, 2025), emotion decoding in art (CogSci, 2024), and visuo-linguistic predictability (EACL, 2024). She has received recognition for best shared task paper (CMCL, 2022). Best Shared Task Paper Award, CMCL 2022 Dr. Takmaz actively contributes to academic service as an Area Chair for CoNLL 2025 and reviewer for top NLP/ML conferences. She serves on the organization committee for CMCL 2024-2025 and the EACL Student Board (2020-2022).

• Hydrology
• Geophysics
• Data Science
• +3 more

Dr. Jude King is a researcher at Utrecht University 's Faculty of Geosciences within the Earth Surface Hydrology group. He holds a PhD in Airborne Electromagnetics (2022) and an MSc in Geology and Geophysics (University of Leeds, UK). His expertise lies in hydrology , geophysics , and data science , focusing on airborne electromagnetic surveys , 3D groundwater modelling , and salinity mapping in coastal zones. Research Interests : Coastal groundwater salinity dynamics Airborne electromagnetic inversion methods 3D numerical hydrogeological modelling Geostatistical uncertainty quantification Climate change impacts on subsurface water His publications emphasize groundwater-salinity interactions , AI-driven geophysical analysis , and sustainable water resource management . Recent work includes global 3D salinity mapping and regional coastal aquifer studies . Jude has collaborated on projects in the Netherlands , Iran , and Southern Africa .

• Seismology
• Global Seismology
• Earth's Deep Interior
• +7 more

Dr. Su Talavera Soza is a Researcher at the Faculty of Geosciences, University of Utrecht, specializing in the DES - Seismology group. Their work bridges seismology, machine learning, and geophysical inversion to study Earth's crust, mantle, and inner core. They employ seismic normal modes and surface waves with advanced instruments like superconducting gravimeters and ocean bottom seismometers. Key Expertise Areas: Seismology, Global Seismology, Earth's Deep Interior, Machine Learning, Inverse Problems, Tomography Skills: Python, Fortran, MATLAB, Data Analysis, Visualization Current research focuses on physics-informed machine learning for full-waveform inversion and planetary-scale structure analysis. They collaborate internationally across the USA and Europe, contributing to 15+ publications between 2020-2025, including high-impact journals like Nature and Nature Geoscience . Notably, they developed the FrosPy Python toolbox for normal mode seismology. While no formal awards are listed, their work on mantle attenuation models and inner core dynamics has been widely shared (214+ X posts) and discussed in Wikipedia. They hold a 2021 PhD from Utrecht University titled Observing seismic attenuation in the Earth’s mantle and inner core using normal modes , supervised by A. Deuss.

• Finance
• Energy Finance
• Commodity Finance
• +6 more

Dr. Thomas Walther is an Associate Professor of Finance at the Utrecht School of Economics, Utrecht University. He previously served as Assistant Professor for Energy Finance at the University of St. Gallen and holds a Research Fellow position at the Faculty of Business and Economics, Technische Universität Dresden. Dr. Walther received his B.Sc. (2010) and M.Sc. (2013) in Industrial Engineering and Management and a PhD (2017) in Business & Economics from Technische Universität Dresden. His academic journey includes visiting researcher positions at International University HCMC and John von Neumann Institute. His research spans energy & commodity finance, climate finance, financial econometrics, and sustainable finance . Dr. Walther employs advanced quantitative methods including time series analysis and machine learning techniques to address pressing issues in risk management and market dynamics. His work integrates traditional financial theory with modern computational approaches to analyze complex market phenomena. His recent publications demonstrate a strong focus on sustainable finance applications, with increasing attention to climate risk, clean energy technologies, and explainable artificial intelligence in financial decision-making. The research shows a clear trajectory toward addressing sustainability challenges through rigorous financial analysis and innovative methodological approaches. AMFET Best Paper Award (2016) Best PhD Paper in Finance (2021) Campbell Watkins Energy Journal Best Paper Award (2022) Certificate of Appreciation (2016) Dr. Feldbausch-Förderpreis (2018) Dr. Walther serves as Associate Editor for Finance Research Letters since 2019 and was ranked among the top scientists in business administration under 40 years in Germany, Austria, and Switzerland in 2024. He initiated the European Sustainable Finance PhD Workshop in 2023 and co-founded the Dutch Sustainable Finance Network in 2024, demonstrating strong commitment to building academic communities around sustainable finance research. His technical expertise includes advanced programming skills in Matlab, R, and Python, which he applies to complex financial data analysis and modeling problems. His research network spans multiple countries and institutions, reflecting the international nature of his scholarly contributions.

• Higher Education Research
• Teacher Education
• Pedagogy and Education
• +5 more

Dr. Despoina Georgiou is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences, Department of Pedagogical and Educational Sciences, specializing in teacher education, evidence-based teaching, and technology integration in primary education. She holds a PhD (2020) and Master's (2018) from Ludwig Maximilian University of Munich. PhD: Research-Practice Gap in Teacher Education Master's: Research on Teaching and Learning Her research focuses on university teacher professional development , technology adoption in education , and challenge-based learning methodologies . Recent publications analyze AI-based critical thinking assessment, pandemic-era technology acceptance, and vocational education problem-solving skills. Article trends show concentrated research in: Technology education for primary schools Evidence-based teaching practices Challenge-based learning frameworks Teacher professional development Self-efficacy in educational settings Interdisciplinary pedagogical approaches Scientific contributions include: 2025: EARLI Special Interest Group 4 Coordinator Various peer-reviewed publications with Mendeley readership metrics She has co-authored 10+ research outputs since 2020, including systematic reviews, mixed-methods studies, and empirical analyses across educational domains.