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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.

Linnéa StenlidenView profile

Professor

• Artificial Intelligence in Education
• AI Literacy
• Educational Technology
• +4 more

Linnéa Stenliden is a Professor at Linköping University's Department of Behavioural Sciences and Learning (IBL), affiliated with the Division of Learning, Aesthetics, and Natural Science (LEN). Her work focuses on the transformative impact of AI on education, particularly in primary and teacher education contexts. She leads interdisciplinary projects exploring AI literacy, educational robotics, and embodied learning processes. Her research investigates how AI technologies reshape learning environments, teacher practices, and student engagement. Notable initiatives include the BookBot Project (social robots for reading motivation) and frameworks for AI literacy in K-12 education. She collaborates internationally, recently participating in European-Japanese digital transformation dialogues and AI ethics seminars. Stenliden supervises PhD students Carl-Johan Stenberg and Cornelia Linderoth. She contributes to Linköping University's AIST Lab, a newly funded platform for AI-driven education research. Her work has been highlighted in media including The Evening Standard and academic journals like Computers and Education Open.

• Cloud Computing
• Big Data Pipelines
• Machine Learning
• +4 more

Mihhail Matskin is a Professor at the Royal Institute of Technology (KTH) within the Department of Software Logic & Computer Systems. He specializes in cloud computing, big data pipelines, machine learning, and distributed systems. His work emphasizes optimizing cloud resource allocation, developing graph-based cost models, and advancing AI-driven systems like SQL recommendation engines and relation extraction frameworks. He actively contributes to KTH's educational mission through roles as examiner and course manager in advanced computer science and engineering courses, including Distributed AI, Cloud Storage Optimization, and Big Data Workflow Design. His research spans over two decades, focusing on scalable data management solutions, edge computing applications in healthcare, and semantic analysis of social media. Notable projects include the DataCloud initiative for big data pipeline orchestration and the DEF-PIPE DSL visualization framework. Matskin’s work bridges theoretical advancements with practical implementations, addressing challenges in reproducibility of LLM-based systems, containerized edge computing, and constraint programming for robotics. His academic contributions include pioneering studies on cloud cost modeling, rule-based storage tiering, and contrastive learning for NER tasks. He has advised numerous advanced-level degree projects across computer science, embedded systems, and communication technologies. Matskin’s lab focuses on innovative solutions at the intersection of distributed systems, AI, and data engineering.

Sanjay SomanathView profile

Research Fellow

• Social Sustainability
• Computational Design
• Geographic Information Systems
• +3 more

Sanjay Somanath is a Research Fellow at Chalmers University of Technology's Department of Architecture and Civil Engineering. His research focuses on computational approaches to urban social sustainability, specializing in activity-based accessibility modeling, GIS applications, and digital tool development for neighborhood planning. He collaborates with sustainable building researchers including Alexander Hollberg and Holger Wallbaum. Education PhD (2024) - Chalmers University of Technology: 'Instruments of Inquiry for Urban Social Sustainability' M.Arch Digital Architecture and Tectonics (2018) - University of Nottingham B.Arch (2017) - BMS College of Engineering, Bangalore Research Focus Somanath's work centers on operationalizing urban social sustainability through digital frameworks. He develops computational instruments like activity-based accessibility models that simulate resident behaviors to evaluate neighborhood equity. His research bridges social theory with technical implementation, creating tools for participatory urban planning and sustainability assessment. Recent work explores digital twins for multi-domain urban simulation, machine learning for urban quality inference, and procedural generation of 3D city models. These contributions advance data-driven approaches for evaluating social infrastructure coverage and environmental impacts. Projects & Collaborations Developed Twinable: Interactive tool for socially sustainable neighborhood planning Contributed to DecarbonAIte: AI-based urban modeling for energy and environmental factors Collaborates with Sustainable Building research group at Chalmers

Jonas LöwgrenView profile

Professor

• Interaction Design
• Information Design
• Collaborative Media Design
• +4 more

Jonas Löwgren is a Professor of Interaction and Information Design at Linköping University, Sweden. He holds a Ph.D. in Computer Science and has been a prominent academic since the 1990s, contributing significantly to interaction design theory and practice. Löwgren has led the development of influential academic programs, including the Master’s program in Interaction Design at Malmö University and the international Master’s program in Design at Linköping University. His research focuses on collaborative media design, interactive visualization, and the theoretical foundations of interaction design, emphasizing human-centered approaches and the role of technology in societal challenges. Education: Löwgren earned his M.Sc. (1987), Ph.D. (1991), and Docent (1995) in Computer Science from Linköping University. He has held academic positions at multiple institutions, including Malmö University (1999–2015) and Blekinge Technical University (2000–15 as Adjunct Professor). Research Interests: Löwgren’s work explores how technology can be designed to foster meaningful interactions, with a focus on sustainability, collaborative systems, and the ethical dimensions of design. He has pioneered studies on interactive visualization tools for education and urban planning, emphasizing inclusivity and citizen engagement in smart city development. Grants & Leadership: He has managed numerous research projects, such as the Substrate project (2009–15) and the News Media Concept Lab (2010–11). He has served as a faculty opponent for over 30 doctoral defenses and contributed to academic leadership roles, including steering committees for research centers like Internet of Things and People (2013–15). Labs & Teams: Löwgren leads the Media Design and Image Reproduction unit at Linköping University and collaborates with industry partners on projects like the Pinpoint visualization tool (2008) and the Avatopia cross-media community initiative (2001–03).

Daniel JönssonView profile

Associate Professor

• Visualization
• Machine Learning
• Medical Imaging
• +3 more

Associate Professor Daniel Jönsson works at the intersection of visualization and machine learning at Linköping University's Department of Science and Technology (ITN) and Media and Information Technology (MIT) division. He leads research in transforming complex datasets into intuitive visual representations, with applications in medical imaging and neuroscience. As a core developer of the Inviwo visualization software, he emphasizes interdisciplinary collaboration across fields like AI and radiology. His work includes developing tools like VisualNeuro for brain cohort studies and exploring neural network interpretability. Research focuses on visualization techniques for high-dimensional data, including fMRI analysis and neural network exploration. Notable achievements include an honorable mention at the IEEE VIS conference (2016) for brain imaging visualization work. Collaborations with CMIV (Center for Medical Image Science and Visualization) highlight his commitment to clinical and medical research applications. Publications emphasize combining visualization with machine learning, addressing challenges in data readiness for AI projects and improving neural network analysis. His contributions span software development, algorithm design, and cross-disciplinary methodological advancements in data-driven decision-making.

• Climate Science
• Environmental Governance
• Sustainability Transformations
• +5 more

Tina Schmid Neset is a Professor at Linköping University's Department of Thematic Studies (TEMA), specializing in climate science and environmental governance. Her work focuses on sustainability transformations, digital technologies for climate adaptation, and participatory methods. She leads research on AI applications in weather warning systems and urban resilience, with a particular emphasis on visualization platforms like the Norrköping Decision Arena. Her research portfolio includes projects such as AI4ClimateAdaptation, CitizenSensing, and Visual Water, addressing issues like climate services, urban heat islands, and phosphorus security. She collaborates with international partners across academia and industry, contributing to EU initiatives like MIRACLE and NORD-STAR. Key grants include a SEK 20 million award from the Marianne and Marcus Wallenberg Foundation for climate adaptation projects. Tina’s work bridges environmental science with societal challenges, emphasizing co-creation and stakeholder engagement to advance climate justice and equitable urban development.

• Science Education
• Technology Education
• Chemistry Education
• +3 more

Astrid Berg is an Associate Professor at Linköping University, affiliated with the Department of Behavioural Sciences and Learning (IBL) and the Division of Learning, Aesthetics, and Natural Science (LEN) within the Faculty of Educational Sciences. Her research focuses on science and technology education, particularly in early primary education contexts. She leads the Technology and Science Education Research (TESER) unit, exploring innovative teaching methods such as digital animation and programming integration in classrooms. Her work emphasizes children's mechanistic reasoning in chemistry and their understanding of abstract scientific phenomena through hands-on activities. Recent studies include investigations into primary students' conceptions of thermal expansion and their interactions with robots/programming. She has secured SEK 2.5 million in funding from the Swedish Institute for Educational Research for projects on animation tools in chemistry education. Berg collaborates extensively with researchers like Magnus Hultén and Cecilia Axell, publishing in journals such as Chemistry Education Research and Practice and International Journal of Technology and Design Education . Her research bridges disciplinary boundaries between natural sciences, technology education, and pedagogical theory.

Sten GrillnerView profile

Professor

• Neurosciences
• Motor Control
• Neural Circuits
• +4 more

Sten Grillner is a Senior Professor at Karolinska Institutet's Department of Neuroscience, focusing on the cellular and evolutionary basis of motor behavior. His research integrates neurophysiology, computational modeling, and evolutionary biology to understand neural circuits controlling movement, with a focus on lamprey models and basal ganglia function. His work explores how motor systems evolved, including the role of pH-sensing neurons and the neural basis of action selection. Key research areas include: motor control mechanisms; evolution of vertebrate forebrain structures; modeling neural networks (e.g., striatum and spinal circuits); and the role of dopamine and other neuromodulators in behavior. His group's projects are funded by the Swedish Research Council, EU initiatives like the Human Brain Project, and Karolinska Institutet. Publications span over 50 years, with recent work on basal ganglia microcircuits, computational neuroscience frameworks, and evolutionary insights from lamprey models. His team's interdisciplinary approach bridges experimental and theoretical neuroscience, contributing to understanding both normal motor function and neurological disorders.

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.

• Medical Imaging
• Image Processing
• Medical Visualization
• +3 more

Örjan Smedby is a Professor at KTH Royal Institute of Technology, specializing in Medical Image Processing and Visualization. His research focuses on developing precise and efficient computational methods for medical decision-making, particularly in automated segmentation of medical images and quantitative analysis of bone structure. Key areas include tumor segmentation, bone graft analysis, and improving imaging techniques for clinical applications such as cancer treatment evaluation and osteoporosis assessment. His work integrates advanced machine learning techniques like deep learning and generative models to enhance image quality, reduce radiation exposure, and automate diagnostic processes. Collaborations involve interdisciplinary teams addressing challenges in oncology, neurology, and orthopaedics. Research Interests: - Medical Image Segmentation - Deep Learning Applications in Radiology - Quantitative Bone Microstructure Analysis - AI-Driven Diagnostic Tools - Iterative Reconstruction Algorithms His publications span topics such as optimizing PET/CT imaging protocols, Alzheimer’s disease neuroimaging, and breast cancer diagnostics. The research emphasizes translating computational methods into clinical workflows to improve healthcare efficiency and accuracy.

• Artificial Intelligence
• Machine Learning
• Blockchain Technology
• +4 more

Zeinab Shahbazi is a Senior Lecturer in the Department of Computer Science at the Faculty of Natural Science. With nine years of expertise, her research focuses on AI-driven solutions for complex data challenges, including machine learning, blockchain, and natural language processing. Current role: Senior Lecturer in Computer Science Research affiliations: Faculty of Natural Science Her work integrates advanced AI techniques to address real-world problems such as climate misinformation detection, security patch optimization, and healthcare data security. She leads collaborative projects like the 2024-2025 International Staff Exchange in Emerging Technologies. Recent research emphasizes interdisciplinary applications, spanning climate informatics, cybersecurity, and recommendation systems. Over 38 publications highlight her contributions to machine learning frameworks and blockchain-based solutions.

Birger JohanssonView profile

Researcher

• Cognitive Modeling
• Robotics
• Artificial Intelligence
• +4 more

Birger Johansson is a Researcher in Cognitive Science at the Department of Philosophy, Lund University. He leads the Lund University Cognitive Robotics Lab and focuses on building robots to study human-like adaptability, attention, and consciousness. His work integrates cognitive modeling, AI, and robotics to explore how robots can interact socially and autonomously in dynamic environments. Research interests include humanoid robotics, cognitive architectures (e.g., Ikaros framework), consciousness studies, and the ethical implications of social robots. He supervises projects like 'Non-Verbal Signals of Trust in Robots' and contributes to initiatives such as Robotdagen and the eSSENCE collaboration. Teaching includes courses on robots in society (KOGB02) and cognitive science fundamentals (KOGM01). His research spans over 50 publications, emphasizing topics like pupil dynamics, memory systems, and energy-efficient robot behavior. Collaborations involve interdisciplinary teams and international conferences. Labs/Teams: Manager of Lund University Cognitive Robotics Lab and affiliated with LU Profile Area: Natural and Artificial Cognition. Active in organizing robotics events and mentoring student projects.

• 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.

• Data Science
• Machine Learning
• Machine Vision
• +8 more

Roger G Nyberg is a Senior Lecturer at Dalarna University's School of Information and Engineering, Department of Information Science. He holds a PhD from Edinburgh Napier University (2016) and teaches courses in artificial intelligence, data science, software engineering, and project management. As head of the Informatics main field of studies, he oversees programs in Systems Science, Graphic Design and Web Development, and Digital Services. His research focuses on Data Science, Machine Learning, Machine Vision, and Monitoring, with applications in transportation, healthcare, and e-commerce. Key areas include automating decision-making processes, analyzing road conditions using acoustic and visual data, and studying hygrothermal transfer in building materials. Recent publications highlight work on gravel road condition evaluation using multimodal deep learning, CO2 emissions in parking systems, and exercise interventions for musculoskeletal pain. Nyberg supervises undergraduate and advanced theses and actively contributes to academic boards and committees at Dalarna University.