Find Academic Supervisors

• Digital pathology
• Image analysis
• Human computer interaction
• +3 more

Darren Treanor is an Adjunct Professor at Linköping University's Department of Biomedical and Clinical Sciences (BKV), affiliated with the Division of Inflammation and Infection. He is a practicing NHS pathologist specializing in liver and gastrointestinal pathology with dual qualifications in medicine and computing. His research focuses on digital pathology, image analysis, and human-computer interaction in medical contexts, addressing challenges like algorithmic bias in healthcare AI and inclusivity in medical imaging datasets. His work emphasizes translating computational methods into clinical practice, particularly in improving diagnostic accuracy and equity through AI-driven solutions. Recent publications highlight advancements in tumor detection algorithms and multi-scanner compatibility for AI systems in pathology. He actively contributes to consensus initiatives on ethical AI development in healthcare. No academic awards or grants are explicitly listed in the provided materials. His current role does not involve formal student supervision as per available data, though collaborations with interdisciplinary teams are evident from co-authorship patterns.

Pierangelo DellacquaView profile

Associate Professor

• Artificial Intelligence
• Computer Graphics
• Image Processing
• +4 more

Pierangelo Dellacqua is a Senior Associate Professor at Linköping University, affiliated with the Department of Science and Technology (ITN) within the Media and Information Technology school. His research focuses on Artificial Intelligence, Computer Graphics, and Human-AI Interaction, with a particular emphasis on developing neural architectures like NEMO for emotional decision-making systems. He contributes to projects involving empathy-aware systems, digital forensics, and road safety frameworks using Virtual Reality. His academic work spans interdisciplinary collaborations, including applications in social care, autonomous systems, and cybersecurity. Dellacqua’s publications in 2024 reflect advancements in emotional behavior trees, neural computation models, and VR-enhanced traffic safety solutions. He is part of the Computer Graphics and Image Processing research group, which integrates theory and methodology for visual information technology. Advising and grants details are not explicitly documented here, but his involvement in MIT’s collaborative research environment underscores his role in both foundational and applied research projects.

• Data Mining
• Information Visualization
• Knowledge Representation
• +3 more

Aida Nordman is an Associate Professor and Senior Lecturer in Computer Science at Linköping University's Department of Science and Technology (ITN). Her research focuses on data mining algorithms, knowledge representation, and information visualization, with applications in interactive systems and human-computer interaction. She teaches courses in computer programming and data structures. Notable research areas include user-steered pattern mining, visualization techniques for noisy event sequences, and eye-tracking analysis in decision-making processes. Her work explores how visualization tools can enhance user interaction with large datasets and how expert knowledge can be integrated into mining algorithms. Collaborations involve applications in air traffic control and simulation log analysis. Nordman contributes to research groups like the Information Visualization (INV) and Media and Information Technology (MIT), advancing interdisciplinary approaches to data-driven problem-solving. Key research outputs include studies on multi-level visual exploration strategies, topic modeling for work phase extraction, and decision-making analysis in en-route control systems. These contributions highlight her expertise in bridging computational methods with real-world decision-support systems.

Ingrid KockumView profile

Professor

• Genetic Epidemiology
• Multiple Sclerosis
• Medical Genetics and Genomics
• +3 more

Ingrid Kockum is a Professor of Genetic Epidemiology at Karolinska Institutet, Department of Clinical Neuroscience, specializing in Multiple Sclerosis (MS) research. She leads the Genetic Epidemiology of Neuroinflammatory Disease research group and has been a Professor since 2016. Her roles include Director of Doctoral Studies in the Department and Vice-chair of the Nordic Society for Human Genetics and Precision Medicine. She holds grants from the Swedish Medical Research Council, Horizon Europe, and others. Affiliations: Karolinska Institutet, Department of Clinical Neuroscience. Education: BA in Natural Sciences (University of Cambridge, 1989); PhD in Immunogenetics (Karolinska Institutet, 1995); Docent in Genetics (Karolinska Institutet, 2007). Research Focus: Identification of genetic susceptibility variants for MS, genetic control of disease severity, interactions between genes and lifestyle factors, viral infections' role in MS, and biomarker development. Current projects include the WISDOM and MultipleMS initiatives. Grants: Includes Swedish Research Council grants for MS biomarkers, environmental factors, and computational methods. She coordinates EU-funded projects like Horizon Europe's WISDOM. Team: Supervises 4 PhD students and co-supervises 3. Her lab integrates genetics, epigenetics, and multi-omics data to advance precision medicine in MS.

Ricardo BernardoView profile

Senior Lecturer

• Energy Engineering
• Sustainable Development
• Building Performance
• +3 more

Ricardo Bernardo is a Senior Lecturer at Lund University's Division of Energy and Building Design, part of the Lund Institute of Technology (LTH). He is a member of LTH's Profile Area: The Energy Transition, focusing on sustainable energy solutions and building performance. His work aligns with UN Sustainable Development Goals, particularly in advancing energy efficiency and climate resilience in urban environments. Key research interests include energy renovation of residential buildings, lifecycle assessment methodologies, and the integration of digital tools for sustainable design. He leads or participates in projects such as COBUILD NEZERO (EIT Climate KIC-funded) and Klimathållbara stadsdelar (Swedish Energy Agency), emphasizing practical applications of research in urban climate neutrality. Bernardo collaborates internationally, with recent publications addressing energy-efficient building workflows, experiential learning in simulations, and user interactions with sustainable technologies. His work bridges academic research and real-world implementation, contributing to policy and industry practices.

Agneta GulzView profile

Professor

• Cognitive Science
• Educational Technology
• Human-Computer Interaction
• +3 more

Agneta Gulz is a Professor of Cognitive Science at Lund University's Department of Philosophy, leading the Educational Technology Group. Her research focuses on designing technology-enhanced learning environments, with an emphasis on educational games, pedagogical agents, and cognitive development in early childhood. She actively contributes to interdisciplinary projects involving collaboration between cognitive science, education, and computer science. Her work integrates theories from learning sciences, human-computer interaction, and developmental psychology to create inclusive and adaptive educational tools. Key areas of exploration include the role of gestures in learning, feedback mechanisms in digital systems, and the impact of virtual reality on collaborative learning environments. She has supervised numerous graduate students in these domains, fostering a research community committed to evidence-based educational innovation. Dr. Gulz's projects often involve empirical studies with preschool and school-aged children, addressing challenges like attention regulation, language comprehension, and information literacy. She has led initiatives such as the 'Educational Technology Group' and contributed to national research portals, demonstrating her commitment to advancing both theoretical and applied aspects of cognitive science in education.

• Information Visualization
• Visual Analytics
• Multidimensional Data
• +5 more

Rafael Messias Martins is a Researcher at Linnaeus University, affiliated with the Department of Computer Science and Media Technology within the Faculty of Technology. He holds an MSc in Computer Science from the University of São Paulo and a PhD in Computer Science from the University of Groningen. His primary research focuses on Information Visualization and Visual Analytics, particularly emphasizing Multidimensional Data and Networks. He is a core member of the Information and Software Visualization (ISOVIS) research group and leads multiple ongoing and completed research projects, including InfraVis (a national research infrastructure for data visualization) and initiatives addressing medication risks and carbon mitigation in forestry. His work bridges theoretical advancements in visualization with practical applications in education, healthcare, and environmental science. Education MSc in Computer Science, University of São Paulo, Brazil PhD in Computer Science, University of Groningen, Netherlands Research Interests His research explores the intersection of visualization techniques with complex data analysis, emphasizing: Interactive visual analytics for high-dimensional data Machine learning interpretability through visualization Educational data analytics for K-12 institutions Applications in healthcare (e.g., medication risk prediction) and environmental science (e.g., carbon footprint reduction) Development of national visualization infrastructures (InfraVis) Recent Trends in Articles His recent work emphasizes: Enhancing trust in machine learning models through visual explanations Optimizing visualization tools for educational stakeholders Algorithmic fairness in urban planning simulations Scalable dimensionality reduction techniques for streaming data Grants & Collaborations He has coordinated projects such as IDEAL (interaction design curriculum development), TimberVis (3D timber structure visualization), and seed projects addressing carbon mitigation and medication risks. Collaborations span academic, industrial, and governmental partners in Sweden and internationally. Labs & Teams He leads the ISOVIS group, which develops open-source tools like SBGTool (student grouping analytics) and FeatureEnVi (feature engineering visualization). The group also contributes to InfraVis, a national platform for visualization resources.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Joe Steinhauer is a Senior Lecturer in Computer Science at the University of Skövde, affiliated with the School of Informatics and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, and their applications in driver intention recognition, telecommunication networks, and healthcare systems. Research Interests: Joe’s work spans AI-driven solutions for transportation safety (e.g., driver and pedestrian behavior prediction), anomaly detection in telecommunication networks, and data fusion techniques. His recent projects include developing advanced driver assistance systems (ADAS) for trucks and AI methods for climate-adapted water management. Publications: Joe has published extensively on topics such as deep learning models for driver intention recognition, topic modeling for network monitoring, and uncertainty quantification in AI systems. His work bridges theoretical advancements and real-world applications in automotive safety and smart infrastructure. Grants/Projects: Key projects include I2Connect (2023–2026), STRATUS (2021–2023), and CogniDrive (2023–2024). These initiatives aim to enhance road safety, water infrastructure resilience, and driver situation awareness through AI and data-driven approaches. Labs/Teams: He collaborates within research groups focusing on vehicular AI, telecommunication analytics, and applied machine learning at the University of Skövde and industry partners like Volvo and CIDAN.

Thomas PedersonView profile

Professor

• Human-Computer Interaction
• Augmented Reality
• Wearable Computers
• +3 more

Thomas Pederson is a Professor at University West, affiliated with the Open Lab. His research focuses on designing systems that integrate seamlessly into real-world tasks through wearable and mobile technologies, emphasizing an egocentric interaction paradigm. He explores how human perception, cognition, and action shape future wearable systems, particularly in healthcare, manufacturing, and tourism contexts. His work spans over two decades, with a strong emphasis on ubiquitous computing and artificial intelligence. Key contributions include studies on subtle guidance in augmented reality (AR), wearable assistants for surgeons, and IoT-based attention management systems. Pederson collaborates widely, with notable co-authors such as S Jalaliniya, N Tobisková, and L Malmsköld. Pederson’s research interests are organized under broader themes: Human-Computer Interaction (HCI), Augmented Reality (AR), Wearable Computers, Ubiquitous Computing, Artificial Intelligence (AI), and Peripheral Interaction. His recent work addresses sustainable infrastructure design, tourism inclusion, and invisible AI systems that operate through peripheral attention. His articles reflect trends in AR applications across diverse domains, including industrial assembly, surgical support, and multiplayer gaming. He also investigates the ethical and design implications of blending AI with HCI, particularly in contexts where systems must be unobtrusive yet effective. Despite no explicit awards listed, his prolific output and collaborations indicate significant contributions to wearable computing and AR research. Pederson has advised no formal students listed in the provided texts. Grants or funding specifics are not mentioned, but his focus on applied research suggests involvement in projects related to smart environments, clinical systems, and manufacturing innovation. He is also involved in organizing workshops and conferences, such as the 2020 NordiCHI event and the 2010 TEI Proceedings, highlighting his role in advancing interdisciplinary HCI discourse.

Jonas LundbergView profile

Senior Lecturer

• Machine Learning
• Program Analysis
• Software Engineering
• +3 more

Jonas Lundberg is a Senior Lecturer in Computer Science at Linnaeus University since 2000, holding a Ph.D. in Theoretical Physics (Umeå University, 1994) and a second Ph.D. in Computer Science (Linnaeus University, 2012). He teaches foundational and advanced courses in programming, machine learning, and compiler design. His research focuses on data-intensive computing, program analysis, and cross-disciplinary projects like the Nordic Tweet Stream (NTS), analyzing social media data from Nordic countries. He leads the Computer Engineering program and participates in CDIO initiatives. Education: Ph.D. in Theoretical Physics, Umeå University (1994) Ph.D. in Computer Science, Linnaeus University (2012) Research interests include machine learning applications in software engineering, program analysis techniques for large codebases, and digital humanities projects leveraging big data. He co-founded the DISA research center (2016) to address challenges in data collection, analysis, and utilization. Recent work involves developing memory-efficient program analysis frameworks and detecting anti-democratic discourse in social media. Key projects include managing the Master's program in Computer Science and advancing self-adaptive systems engineering. His contributions span 70+ publications in venues like IEEE Transactions, Journal of Systems and Software, and conferences such as SAC and DHN. He is affiliated with research groups like Data Intensive Software Technologies (DISTA) and co-leads the Nordic Tweet Stream initiative. His work bridges computational methods with humanities, emphasizing real-time data analysis and cross-disciplinary collaboration.

Görel HedinView profile

Professor

• Software Engineering
• Computer Sciences
• Object-Oriented Languages
• +5 more

Görel Hedin is a Professor at the Department of Computer Science, Lund University, within the Faculty of Engineering, LTH. She holds key roles including Deputy Head of Department, Assistant Head of Department, and Project Manager, while leading the Software Development and Environments (SDE) division. Her affiliations include ELLIIT (Linköping-Lund IT initiative) and the Wallenberg Autonomous Systems Program (WASP). She actively contributes to UN Sustainable Development Goals through technology-driven solutions. Teaching includes courses such as EDAN65 Compilers and EDAN70 Project in Computer Science, where she supervises compiler-related projects. Her research spans object-oriented languages, domain-specific language design, generative implementation techniques, static analysis frameworks, and pervasive systems. She emphasizes agile methodologies and secure communication in healthcare and IoT contexts. Research interests include: Advanced compiler optimization Tooling for software development environments Live program analysis techniques Adaptive developer tool interactions Integration of static analysis into code analysis Cyber-physical system development She has been honored with awards such as the Distinguished Artifact and Paper Awards in 2024 for her work on fixed-point attributes and the Best Demonstration Award in 2020 for healthcare IoT contributions. Her advising includes supervision of doctoral candidates like McCabe and Kuang. Key grants involve leadership in the Swedish Foundation for Strategic Research-funded ADAPT2 project and initiatives on cloud-based language tooling. She also manages projects like Explainable Declarative Programming Analysis and Cloud Tooling for Cyber-Physical Systems. As the Head of the SDE division, she drives research in software development tools and methodologies. Her involvement extends to editorial roles at ACM SIGPLAN conferences and chairing the AITO Dahl Nygaard Prize committee.

Xavier de LunaView profile

Professor

• Causal Inference
• Machine Learning
• Statistics
• +5 more

Xavier de Luna is a Professor of Statistics at Umeå School of Business, Economics and Statistics (USBE) at Umeå University in Sweden. He leads the Stat4Reg Lab and is part of the Umeå SIMSAM Lab and the SINGS graduate school. His research focuses on causal inference, machine learning, and statistical methodology applied to social and health sciences. He serves as Editor of the Scandinavian Journal of Statistics (2025-2027) and has held roles as Associate Editor for Biometrics, Computational Statistics and Data Analysis, and Observational Studies. Education: PhD in Statistics from Swiss Federal Institute of Technology (1996). Prior roles include Head of the Department of Statistics at Umeå University (2009-2011) and affiliation with institutions like IFAU and the Institute of Labour Market Policy Evaluation. Research interests span causal machine learning, covariate selection, and register-based studies in health and social sciences. Key projects include using generative AI for open science and machine learning for causal discovery. His work addresses challenges like unobserved confounding and high-dimensional data. Notable contributions include methodological advances in causal inference (e.g., covariate balancing, neural networks for causal effects) and applications in health economics, retirement effects, and brain imaging studies. He has advised over 15 PhD students and secured grants from the Swedish Research Council and Wallenberg Foundation. Labs/Teams: Stat4Reg Lab (causal inference), Umeå SIMSAM Lab (register-based research), SINGS (interdisciplinary graduate school).

Olov AnderssonView profile

Assistant Professor

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +5 more

Olov Andersson is an Assistant Professor and WASP Fellow in AI for Autonomous Systems at KTH Royal Institute of Technology, leading the Division of Robotics, Perception and Learning. His research focuses on Embodied AI for autonomous robots and vehicles, combining advancements in Vision-Language Models (VLM), Large Language Models (LLM), and real-world navigation challenges. Key projects include the DARPA SubT Challenge-winning team CERBERUS and the EU H2020 Heron project for robotic road repair. He supervises multiple PhD students and postdocs, including Timon Homberger, Finn Lukas Busch, and Jesper Eriksson. Research interests emphasize full-stack autonomy in dynamic environments, including planning, mapping, and navigation. Notable contributions include the OneMap real-time open-vocabulary mapping system and self-supervised scene flow methods like Seflow. He has been recognized for technical leadership in autonomous systems through awards like the WASP Fellowship. Professional activities include co-chairing the 2024 IROS workshop on robot perception in dynamic environments and advising the Swedish Prime Minister’s AI initiative. Teaching roles span multiple graduate courses in machine learning, robotics, and systems engineering at KTH.

Miguel Serras VascoView profile

Researcher

• Multimodal Perception
• Reinforcement Learning
• Representation Learning
• +4 more

Miguel Serras Vasco is a postdoctoral researcher at KTH Royal Institute of Technology in Stockholm, Sweden, affiliated with the Robotics, Perception and Learning (RPL) division. He holds a PhD from Instituto Superior Técnico, University of Lisbon (2023), where his work earned the Best PhD Thesis in AI in Portugal award. His research focuses on multimodal perception, reinforcement learning, and aligning artificial agents with human perception. He previously worked as an RSS Pioneer and research intern at Sony AI. Education: PhD in Artificial Intelligence, Instituto Superior Técnico, University of Lisbon (2023) Research Interests: Vasco’s work bridges robotics, neuroscience, and AI, emphasizing embodied agents that co-exist with humans. He explores representation learning, human-aligned image models, and sample-efficient reinforcement learning. His recent projects include super-human autonomous racing agents and olfactory perception modeling with transformers. Key Contributions: Vasco co-developed the GT Sophy racing agent, achieved outstanding results in visual decoding from brain activity, and proposed methods like FLoRA for preference-based RL. His work on NeuralSolver advances algorithm extrapolation in reinforcement learning. Awards: Best PhD Thesis in AI in Portugal (APPIA, 2023) Outstanding Paper Award at RLC 2024 (for autonomous racing research) Grants/Advising: Vasco advises students on multimodal and reinforcement learning topics. He actively organizes conferences like the Reinforcement Learning and Video Games Workshop (RLVG) at RLC 2025 and collaborates with institutions like INESC-ID (Lisbon). Labs/Teams: Associated with the Collaborative Autonomous Systems unit at KTH and previously with the GAIPS Lab (INESC-ID, Lisbon).

• Robotics
• Computer Vision
• Machine Learning
• +3 more

Riccardo Tedoldi is a Researcher and Industry doctoral student at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning. His work focuses on advanced robotics and AI perception systems. He can be reached at rtedoldi@kth.se.