Find Academic Supervisors

• Transportation Systems
• Behavioral Modeling
• Machine Learning
• +5 more

Rico Krueger is an Associate Professor at the Department of Technology, Management and Economics within the Transport Division at the Technical University of Denmark (DTU). His research focuses on developing models and technologies at the intersection of behavioral modeling, machine learning, and simulation to improve human-centric and sustainable transport systems. Key areas include travel demand forecasting, multimodal systems, and emerging technologies like virtual reality for understanding human decision-making. Education: Ph.D. in Civil and Environmental Engineering from UNSW Sydney (Australia), followed by postdoctoral research at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a prestigious European Research Council Starting Grant (2024–2029) for his project IMMERSION , exploring human decision-making through choice and process data integration. Research emphasizes interdisciplinary approaches, combining data science with behavioral theories to address challenges in urban mobility, sustainability, and public health. His work bridges theoretical advancements with practical applications, such as optimizing policy interventions during pandemics and enhancing ride-sourcing systems. Awards: ERC Starting Grant for IMMERSION project. Grants: Focus on behavioral modeling and pandemic-related transport policies. Labs/Teams: Active in DTU’s Intelligent Transport Systems Section, leading interdisciplinary projects on mobility and health.

Ïo Valls-RatésView profile

Assistant Professor

• Virtual Reality
• Public Speaking
• Second Language Acquisition
• +7 more

Ïo Valls-Ratés is an Assistant Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark. They hold a PhD in Oral Communication from Universitat Pompeu Fabra (2023) and a PhD in Language Sciences. Their research focuses on virtual reality (VR) applications for public speaking training, exploring how VR impacts voice modulation, body language, and anxiety reduction. They also specialize in educational tools for improving oral communication skills among teachers and students. Assistant Professor, Centre for Industrial Electronics (2023–present) Project lead for VirtuVoce (2023–2024) and collaborator on Oralitat (2018–2021) Active in VR-based speech training, prosody analysis, and teacher development programs Key research areas include Virtual Reality , Public Speaking , and Second Language Acquisition . Their work emphasizes nonverbal communication, stress reduction, and intergenerational charisma dynamics. Recent articles analyze the correlation between voice and body language in VR environments and the efficacy of self-guided VR training for students. Collaborations span Denmark, the Netherlands, and Spain, with external partnerships in educational technology. Media coverage highlights their contributions to VR for speech anxiety, educational gaming, and tools like VirtuVoce , which empower students' voices through immersive simulations.

Anders Nymark ChristensenView profile

Associate Professor

• Image Analysis
• Computer Vision
• Medical Imaging
• +7 more

Anders Nymark Christensen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing. His work bridges computer vision, medical imaging, and materials science, with a strong focus on explainable AI in clinical applications and structural analysis of complex materials. Institution: Technical University of Denmark (DTU) School: Department of Applied Mathematics and Computer Science Department: Visual Computing Role: Associate Professor Christensen’s research interests include image analysis, computer vision, explainable AI, ultrasound imaging, fetal medicine, and fiber orientation in composites. He applies advanced computational techniques to solve real-world problems in healthcare and materials engineering, particularly through structure tensor analysis and deep learning. His recent publications highlight a strong trend in developing AI tools for clinical decision support in fetal medicine, structural analysis of food and composite materials, and ultrasound imaging. These works span journals such as Scientific Reports , Food Structure , and IEEE Access , reflecting interdisciplinary innovation. Clinical validation of explainable AI for fetal growth scans Characterization of anisotropy in mozzarella cheese Determining fetal orientations from ultrasound video Structure tensor analysis in composites He supervises several PhD students in projects related to AI in skin lesion analysis, cancer detection, and ultrasound imaging. His collaborative network includes key researchers at DTU such as Anders Bjorholm Dahl, Vedrana Andersen Dahl, and Mads Nielsen. He has been involved in significant research grants and is active in both medical and engineering domains, contributing to open datasets and reproducible research. Christensen leads and contributes to numerous active projects, including: Decision Support AI for Skin Lesions (2024–2027) Fighting Cancer with Generative AI (2024–2027) SONAI: Explainable AI in the Clinic (2022–2026) Deep learning for identifying biomarkers in medical images (2022–2025) His work supports UN Sustainable Development Goals related to health, innovation, and responsible consumption, particularly through advancements in medical diagnostics and sustainable materials.

• Learning Analytics
• Machine Learning
• Computer Vision
• +1 more

Martin Thoft Jensen serves as a Lecturer in the Department of Computer Science at the University of Copenhagen, affiliated with the Image Analysis, Computational Modelling, and Geometry section within the SCIENCE AI Centre. His work bridges theoretical research and practical applications in computational sciences. His research focuses on Learning Analytics, Machine Learning, and Computer Vision, with emphasis on developing open platforms for Multimodal Learning Analytics (MMLA) to enhance educational data interpretation. This intersects Educational Technology through AI-driven solutions for real-world learning environments. Recent contributions include designing the open-source mBox platform for multimodal data analysis in educational settings, presented at LAK24. This work demonstrates trends toward accessible, user-centered analytics tools leveraging computer vision and machine learning for pedagogical innovation. No scientific awards are documented in available sources. Student advisement and grant activities are not referenced in institutional materials, suggesting primary focus on teaching and platform development rather than supervised research or funded projects. Based in DIKU's Image Analysis section, Jensen utilizes departmental resources including high-performance compute clusters and specialized labs supporting computer vision research, geometric modeling, and AI applications for scientific and societal challenges.

• Human-Centred Computing
• Educational Technology
• Multimodal Learning Analytics
• +4 more

Viktor Holm-Janas is a Research Fellow at the Department of Computer Science (DIKU), University of Copenhagen, affiliated with the Human-Centred Computing section. His work bridges computational technology with human activities and well-being, focusing on educational settings and user interaction. Research Interests: Viktor investigates multimodal learning analytics, quantitative ethnography, and didactic theory applications in technology-enhanced education. He explores how human observations, data visualization, and interface design can support collaborative learning and physics education. Publications: His recent articles address topics like holographic analysis of classroom sound, active learning observation networks, and physics education tools. These contributions span journals such as Postdigital Science and Education and eLife , with a focus on educational technology and data-driven insights. Labs and Collaborations: Viktor collaborates within the Human-Centred Computing section, which emphasizes inclusive and innovative research environments. He contributes to projects like SAStutorials.org, advancing scientific data analysis tools.

• Human-Centred Computing
• Multimodal Learning Analytics
• Educational Technology
• +3 more

Zaibei Li is a PhD Fellow at the Department of Computer Science (DIKU) of the University of Copenhagen , affiliated with the Human-Centred Computing section. Their research intersects Educational Technology , Multimodal Learning Analytics (MMLA) , and Human-Computer Interaction . Key Research Areas: Multimodal Learning Analytics for spoken language acquisition Collaboration analytics in hackathons and educational settings Design of smart, inclusive, and equitable learning systems Recent Work: 2024 contributions to EC-TEL , ICALT , and LAK24 conferences Development of open MMLA platforms and frameworks for human-centric analytics Contact: Email: zali@di.ku.dk Address: Sigurdsgade 41, 2200 København N.

Juan C VasquezView profile

Professor

• Microgrids
• Renewable Energy Systems
• Power Electronics
• +6 more

Professor Juan C Vasquez is a faculty member at Aalborg University's Department of Energy Technology, serving as Co-Director of the Center for Research on Microgrids (CROM). Recognized as a Highly Cited Researcher (2017-2023), his work spans technical and interdisciplinary domains in energy systems, with a focus on microgrids, renewable integration, and cybersecurity. His research includes smart grid control, power electronics reliability, and AI-driven energy management systems. He also explores computational creativity in electroacoustic music, evidenced by performances at ISEA 2025 and ACMC 2024. Key subtopics involve fault diagnosis, EV infrastructure, and resilience analysis for disaster-struck communities. Scientific accolades include a First Prize at the 'Città di Barletta' competition. His publications bridge energy technology and digital arts, reflecting a unique interdisciplinary approach to sustainable energy systems and computational sound design.

Lisette Espín-NoboaView profile

Visiting Professor

• Computational Social Science
• Network Science
• Algorithmic Fairness
• +9 more

Lisette Espín-Noboa is a Visiting Professor at the Institute of Human-Centred Computing at Graz University of Technology and a senior postdoctoral researcher at the Complexity Science Hub (CSH) in Vienna and the Department of Network and Data Science at Central European University (CEU). Her work bridges computational social science, network science, and AI for social good, with a focus on fairness and inequality. Her research centers on algorithmic fairness in network-based systems, particularly how social structures bias ranking and recommendation algorithms. She investigates when such biases are deemed unfair, aligning with UN SDGs 5 (Gender Equality), 10 (Reduced Inequalities), and 16 (Peace, Justice and Strong Institutions). At CEU, she collaborates on generating high-resolution poverty maps using satellite imagery, mobility data, and machine learning to address SDGs 1 (No Poverty), 3 (Good Health), and 11 (Sustainable Cities). Her methodological expertise includes multimodal data fusion, graph neural networks, and network modeling. The recent articles reflect a strong trend in using computational methods to address societal challenges—particularly poverty estimation, algorithmic bias, and public health insights from digital traces. Her work consistently applies network science and machine learning to promote equity and transparency in AI systems. Scientific Awards: No awards mentioned in the provided text. Lisette has been actively involved in major research projects such as Humanized Algorithms and MAMMOth , focusing on mitigating algorithmic biases in social networks. She has led survey initiatives on network fairness in academic hiring and academic careers in Europe, indicating engagement with systemic equity issues in science. She mentors through teaching and open science, sharing materials on GitHub and launching a podcast to make her research accessible. She is affiliated with research teams at CSH, CEU, and TU Graz, contributing to collaborative efforts in algorithmic fairness and network inequality. Her work with the netin package and open-source teaching materials demonstrates a commitment to reproducible and inclusive science.

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +4 more

Katrine Harder Clemmensen is a Professor at the Department of Mathematical Sciences , University of Copenhagen . She specializes in statistical modeling , machine learning , and statistical evaluation of AI systems , with particular emphasis on low-resource domains , explainable modeling , and fair health/life science applications . External Employment: Co-founder & Chief Science Officer at Interhuman AI Research Trends (2024-2025): Focus on health applications of ML/AI Developing fair/explainable algorithms for clinical use Interdisciplinary work in psychiatry , microbiome analysis , and neuroimaging Methodological contributions to clustering , self-explainable models , and emotion recognition Selected Publications (2025): Gene co-expression networks in scRNAseq data Computational psychiatry applications in OCD Remote sensing for crop health microbiome studies Neurostimulation techniques beyond RGB light LLM bias analysis in STEM education

Jesper Bláfoss IngvardsonView profile

Associate Professor

• Transportation Systems Modelling
• Transportation Science
• Public Transport
• +9 more

Jesper Bláfoss Ingvardson is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), specializing in Transportation Systems Modelling and Transportation Science. His work contributes to sustainable urban mobility and public transport planning. Education: M.Sc. in Transport & Logistics, DTU (2009–2012) B.Sc. in Civil Engineering, DTU (2006–2009) His research focuses on travel behavior, public transport route choice, transit-oriented development (TOD), and the use of smart card data for behavioral modeling. He investigates how urban design, perceived safety, and station characteristics influence mode choice and ridership. His work integrates psychology, data science, and transport engineering to improve public transport attractiveness. The 15 most recent publications reflect a strong trend in using big data—especially smart card data—to model passenger behavior, reduce estimation bias, and evaluate policy impacts in cities worldwide, including Santiago, Addis Ababa, and Copenhagen. The research spans behavioral modeling, sustainable transport adoption, and emotional responses in urban mobility. Scientific Awards: P.H. Bendtsens Traffic Research Award (2019) RA's Research Award (2018) Jesper actively supervises PhD students and leads research projects such as 'Network Optimisation for Connected, Cooperative and Automated Mobility' and 'Behavioural Modelling for Public Transport.' He has been the principal investigator of the 'BELMOPAN' project, focusing on big data sources for passenger behavior modeling. His grants and projects often involve interdisciplinary collaboration with institutions and urban planners. He is involved in multiple research networks and has presented at numerous international conferences, contributing to the academic discourse on sustainable urban transport and behavioral modeling.

Luca RossiView profile

Associate Professor

• Digital Media
• Networks
• Big Data Analysis
• +9 more

Luca Rossi is an Associate Professor at the Department of Digital Design, IT University of Copenhagen, where he coordinates the Human-Centred Data Science research group and contributes to the Networks, Data, and Society (NERDS) group. His work focuses on digital media, networks, and sociotechnical systems. Research Interests Rossi's research spans social media analysis climate change communication political polarization big data ethics human-computer interaction coordinated disinformation His 15 most recent publications examine visual communication in climate debates, LLM-generated data challenges, TikTok network patterns, and hate speech moderation. Awards Research Environment of the Year 2022 Projects Current roles include: Local PI for EU CHANSE PolarVis Co-Investigator in DEEPFAKE (Meta) Participant in VIRT-EU (European Commission) Contributor to DaR (Velux Fonden) Collaborator in SafeNet (European Commission)

• Database Systems
• Distributed Systems
• Event-Based Systems
• +5 more

Yongluan Zhou is a Professor at the Department of Computer Science , University of Copenhagen , where he co-heads the Data Management Systems Lab (DMS Lab) and serves as Head of Studies for the MSc in Computer Science . His academic journey includes a PhD from the National University of Singapore (NUS) (2007), a postdoc at ETH Zürich (2007–2008), and prior roles as Associate Professor at University of Southern Denmark (SDU) (2008–2017). PhD in Computer Science, National University of Singapore (2002–2007) Postdoc, ETH Zürich (2007–2008) Zhou's research focuses on database systems and distributed systems , with recent emphasis on event-driven systems , scalable stream processing , and big graph analysis . His work bridges theoretical foundations and practical implementations, addressing challenges in data consistency, fault tolerance, and resource optimization in cloud and microservice environments. The trends in his 15 most recent publications (2025–2024) highlight advancements in asynchronous choreographies , blockchain consensus protocols , GPU-accelerated graph processing , and microservices data management . These works integrate formal methods with empirical validation, emphasizing scalability, security, and efficiency in distributed environments. He actively contributes to academic governance as a member of the DEBS Steering Committee (2024–), SSDBM Steering Committee (2022–), and the EDBT Association Executive Board (2020–).

Guido ToselloView profile

Associate Professor

• Injection Moulding
• Additive Manufacturing
• Manufacturing Engineering
• +7 more

Guido Tosello is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark. His research focuses on advanced manufacturing technologies, including injection molding, additive manufacturing, and process optimization. He leads projects addressing sustainability-by-design, digital manufacturing systems, and precision engineering. Tosello has supervised multiple PhD students and contributed to over 395 publications, with recent work emphasizing data-driven modeling, oxide classification in silicon crystal growth, and root cause analysis using AI techniques. His research interests span injection molding, additive manufacturing integration, process chain optimization, and quality control. Key projects include Digital Moulding 4.0 (zero-defect injection molding) and improving silicon crystal growth efficiency through digital tools. Tosello actively collaborates on industry 4.0 frameworks and has pioneered methods for smart polymer acoustofluidic chip manufacturing. Notable contributions include over 40 projects and 395+ publications, with recent articles focusing on sustainability in manufacturing processes, AI-driven defect analysis, and additive manufacturing applications in extrusion tools. His work aligns with UN Sustainable Development Goals, emphasizing environmental and industrial progress. Tosello advises PhD students in areas like digital manufacturing, polymer tooling, and silicon crystal growth. His lab integrates advanced simulation tools and experimental validation to bridge design, production, and sustainability challenges.

Juan Manuel RodriguezView profile

Assistant Professor

• Recommender Systems
• Web Services
• Deep Learning
• +3 more

Juan Manuel Rodriguez is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. His affiliation is within The Technical Faculty of IT and Design, specializing in Data, Knowledge, and Web Engineering. Dr. Rodriguez holds a PhD in Computer Science and has a strong focus on research areas including Recommender Systems, Web Services, Deep Learning, and Social Media Analysis. His work often intersects with applications in information retrieval, healthcare systems, and crisis management. His research explores topics such as misinformation detection, transformer-based news recommendation, and clinical data analysis using knowledge graphs. He has contributed to projects like HEREDITARY (2024–2027), which integrates heterogeneous data for gut-brain research. Rodriguez has also engaged in media collaborations, including work highlighted in 'Udforsker Mars: Studerende fra Aalborg samarbejder med NASA' (June 2024). Key technical interests include leveraging neural networks for user behavior modeling, optimizing retrieval systems, and addressing ethical challenges in AI. His recent publications (2020–2025) span domains from pandemic mental health tracking to parking availability prediction, reflecting a multidisciplinary approach to computational challenges.

Dalin ZhangView profile

Associate Professor

• EEG-based Emotion Recognition
• Time Series Forecasting
• Graph Neural Networks
• +4 more

Dalin Zhang is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He holds a position within the Technical Faculty of IT and Design. His research focuses on machine learning, sensor data analysis, and their applications in healthcare, smart cities, and human-computer interaction. Key areas include EEG-based emotion recognition, time series forecasting, and graph neural networks. He leads the PREDICTION-ENABLED DYNAMIC SCHEDULING OF HOUSEHOLD ELECTRICITY USAGE project (2023–2024), demonstrating expertise in energy systems optimization. His work bridges theoretical advancements with practical applications, such as developing lightweight models for real-time traffic flow prediction and creating benchmarks for emotion recognition systems like LibEER. Research interests span: EEG signal processing for emotion and intention recognition Efficient time series analysis and forecasting Graph neural networks and adversarial defense mechanisms Data privacy in wearable sensor systems Recent publications highlight innovations like Pure-GNN (graph neural networks against adversarial attacks), LightCast (traffic forecasting), and Self-Supervised EEG Representation Learning. He advises two PhD students but their names are not listed in the provided texts. His work often emphasizes computational efficiency and real-world deployment across healthcare, smart infrastructure, and consumer electronics.