Find Academic Supervisors

Ester Appelgren is an Associate Professor in Journalism and Head of School at the School of Natural Sciences, Technology and Environmental Studies at Södertörn University. She holds a PhD in Media Technology and focuses on Data Journalism, Media Leadership, and Digital Integrity. Her work bridges technological innovation with ethical journalistic practices, particularly in crisis communication and public service media contexts. Educatio: PhD in Media Technology. Research interests include data-driven storytelling, media management during crises, and the evolving role of interactivity in journalism. She chairs the Journalism division of Nordmedia and serves on editorial boards for Digital Journalism and Journalistica . Her recent research explores pandemic-era media responses, ethical use of sensor data in journalism, and the paternalistic implications of interactive media. She has published widely on Nordic journalism trends and media convergence challenges. Currently, she is not participating in active research projects but remains active in editorial and academic leadership roles. Key contributions include analyzing media leadership during the pandemic, evaluating digital transformation in Swedish public service news, and critiquing technological determinism in journalism studies. Her work emphasizes balancing innovation with ethical accountability in data journalism practices.

• Data Journalism
• Media Leadership
• Digital Integrity
• +3 more

Tatiana Mikhaylova is a Lecturer at the University of Gävle. She holds a PhD from Uppsala University (2022) with a thesis exploring the historical and policy dimensions of private tutoring in Russia across Imperial, Soviet, and post-Soviet eras. Her research focuses on educational policy, shadow education systems, curriculum theory, and the role of quantification and visualization in shaping educational practices. She has conducted comparative studies between Sweden and Russia, examining how supplementary tutoring interacts with formal education systems. Her work analyzes the sociopolitical construction of educational policies, particularly how visual and numerical frameworks (e.g., Bloom’s Taxonomy, standardized testing metrics) influence educational governance and discourse. Key themes include historical pedagogical practices, the interplay between public and private educational sectors, and the ethical implications of AI integration in teacher training programs. Recent publications address topics like AI’s role in teacher professional development, the visualization of educational knowledge structures, and the quantification of reading comprehension benchmarks. She frequently collaborates with researchers such as David Pettersson and Emma Sundström Sjödin on projects examining Nordic educational policy and historical curriculum debates. Mikhaylova’s research has been presented at venues including the Nordic Education Research Association (NERA) conferences and the International Standing Conference for the History of Education (ISCHE). Her work bridges historical analysis with contemporary policy critique, emphasizing the need for critical perspectives on educational metrics and visual representation strategies.

• Educational Policy
• Curriculum Studies
• Shadow Education
• +3 more

Anders Brandt is an Associate Professor (Docent) in Geospatial Information Science and Senior Lecturer in Geomatics at the University of Gävle, Sweden, within the Department of Computer and Geospatial Sciences under the Faculty of Engineering and Sustainable Development. His research focuses on flood risk mapping uncertainties, agent-based modeling of pedestrian movement, and spatial decision-support systems for sustainable urban planning. He holds a PhD in Physical Geography from the University of Copenhagen and has extensive international collaboration experience. Education: PhD in Physical Geography (University of Copenhagen, Denmark), focusing on fluvial geomorphology of Costa Rica’s Reventazón River Master’s Degree in Physical Geography (Uppsala University, Sweden) Research Interests: Brandt’s work addresses uncertainties in flood modeling, spatial decision analysis, and geospatial data applications for urban resilience. His projects include the 'Big Data Methodology for Experiential and Cognitively Sustainable Urban Growth' initiative, emphasizing ecosystem service mapping and spatial MCDA methods. His agent-based modeling research explores emergent urban path systems to optimize pedestrian infrastructure. Publications Trends: Recent work spans flood risk visualization, blue-green infrastructure roles in climate resilience, and urban form complexity metrics. His 2025 papers highlight innovative applications of agent-based modeling and systematic reviews of climate hazard mitigation strategies. Advising & Grants: While no formal advisee list is provided, his collaborative publications suggest involvement in student projects. His grants include international initiatives like Mongolia’s land administration capacity-building programs. Labs/Teams: Co-founded GeoVega , a consulting firm specializing in flood risk mapping and geospatial solutions. Active in educational initiatives like harmonizing GIS curricula in Swedish universities.

• Geospatial Information Science
• Flood Risk Mapping
• Uncertainty Analysis in Environmental Modeling
• +6 more

Joakim Lindblad is a Professor at the Department of Information Technology, Uppsala University , and holds affiliated roles as Senior Research Associate at the Mathematical Institute of the Serbian Academy of Sciences and Arts, and Head of Research at Topgolf Sweden AB. With over two decades of expertise in image analysis and machine learning , his work bridges computational methods with biomedical applications. Key affiliations: Uppsala University, Serbian Academy of Sciences, Topgolf Sweden Specializations: Deep Learning, Multimodal Image Registration, Quantitative Microscopy His research focuses on reliable image processing frameworks that integrate intensity and spatial information , particularly for biomedical applications . Recent publications highlight innovations in autofluorescence-based cancer detection , self-supervised one-class learning for sparse instance identification, and rotation-equivariant CNNs for robust analysis of cytology images. Recent article trends demonstrate expertise in multimodal image analysis (2024: 3 papers), oral cancer detection (2025: 2 papers), and multiscale biomedical imaging . His 2025 work on the Uppsala Storytelling Dataset introduces novel frameworks for multimodal dataset creation in AI research. While no scientific awards are explicitly mentioned, his extensive publication record (2000-2025) across top venues like Pattern Recognition , PLOS ONE , and IEEE Transactions indicates significant academic impact. His methodological contributions span stochastic distance transforms , fuzzy set defuzzification , and multimodal image registration techniques. Collaborative work with researchers like Nataša Sladoje and interdisciplinary teams has produced innovations in automated cytology analysis , TEM image enhancement , and AI-driven medical diagnostics . His 2021-2022 projects introduced contrastive learning approaches for multimodal image registration and explainable AI frameworks for infant engagement analysis.

• Image Analysis
• Artificial Intelligence
• Deep Learning
• +5 more

Tino Weinkauf is a Professor of Visualization and Head of the Division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm. His work bridges computer science and applied mathematics, with a focus on visualization and topological data analysis. He leads research in visualizing complex data from fields like fluid dynamics, neurobiology, and human-computer interaction. Education: Ph.D. in Computer Science (not explicitly stated in provided texts, but inferred from career trajectory). Research interests include flow visualization, topological methods for data analysis, and interactive visualization techniques. He develops tools like the TopoInVis Toolkit (TTK) and contributes to infrastructure such as the Swedish Research Infrastructure for Visualization Support (InfraVis). His work emphasizes applications in turbulence modeling, biomedical imaging, and user-centered design. Teaching: Responsible for courses such as Advanced Topics in Visualization and Computer Graphics , Information Visualization , and Introduction to Visualization and Computer Graphics . Supervises degree projects in Computer Science and Engineering across specializations like Machine Learning and Interactive Media Technology. Publications focus on topological data analysis, flow segmentation, and algorithm optimization. Notable projects include binary segmentation of turbulent flows and interactive reward tuning systems for preference elicitation. Labs/Teams: Leads the Division of Computational Science and Technology at KTH, fostering interdisciplinary research in computational methods and visualization technologies.

• Visualization
• Computer Graphics
• Flow Visualization
• +3 more

Daniel Edler is a Researcher and Postdoctoral Fellow at the Department of Physics, Umeå University. His work focuses on network science, biodiversity analysis, and ecological modeling, with a particular emphasis on developing computational tools for community detection and biogeographical mapping. He contributes to interdisciplinary research, integrating methods from computer science, ecology, and information theory. Edler leads the development of Infomap Bioregions, a tool for mapping biogeographical regions using species distribution data, and CoordinateCleaner, which standardizes biological occurrence records. His research also explores threats to Madagascar’s biodiversity and the interplay between socio-political factors and biodiversity data availability through tools like Bio-Dem. He has co-developed raxmlGUI 2.0, a phylogenetic analysis interface, and contributes to the Infomap software package for network analysis. Edler’s publications highlight themes in higher-order network flows, multilayer community detection, and ecological network modules. His work appears in journals such as Science , American Journal of Botany , and Methods in Ecology and Evolution . He is an active member of the Complex Systems research group at Umeå University.

• Network Science
• Biodiversity
• Ecological Modeling
• +3 more

Richard Brenner is a Professor and Head of Department at the Department of Physics and Astronomy , Uppsala University. He is a key member of the ATLAS detector team at the CERN Large Hadron Collider (LHC) , focusing on instrumentation development and real-time data processing for dark matter detection. His work bridges semiconductor detector signals with machine learning systems , emphasizing radiation resistance in high-energy environments. Role: Head of Department of Physics and Astronomy Affiliation: Uppsala University and CERN Research Focus: Dark Matter, Higgs Boson, Particle Physics His recent 15 publications (2025) span topics like dark matter searches , Higgs boson production , vector boson fusion , and machine learning applications in data analysis. Keywords include High Energy Physics , Experimental Physics , and Quantum Interactions , with subfields such as Collider Physics , Detector Engineering , and Theoretical Modeling

• Particle Physics
• High Energy Physics
• Instrumentation Development
• +7 more

Elina Eriksson is an Associate Professor at the Department of Media Technology and Interaction Design (MID) within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. She holds additional affiliations with Green Leap, Digital Futures, and the KTH Climate Action Center. Dr. Eriksson co-leads the Sustainable Futures Lab research group and serves as programme responsible for the new Sustainable Digitalisation master's programme launching in autumn 2024. Dr. Eriksson earned her PhD from KTH with the thesis 'Situated Reflexive Change - User-centred Design in(to) Practice' in 2013, while also working at Uppsala University during her doctoral studies. Her academic journey demonstrates a consistent focus on the intersection of technology and sustainability. Her research centers on sustainability and human-computer interaction (S-HCI), specifically exploring how computing can facilitate the transition to a more sustainable world. She investigates change processes and practices across multiple domains, with particular emphasis on energy transitions, academic travel emissions, sustainable behavior change, and futures thinking for sustainability. Dr. Eriksson approaches these challenges through both theoretical frameworks and practical interventions. Analysis of Dr. Eriksson's publication record reveals a strong trajectory in sustainable computing, with increasing focus on climate action and systemic change in recent years. Her work bridges technical computing research with social science perspectives, emphasizing practical applications for sustainability challenges across academic, organizational, and everyday contexts. Dr. Eriksson currently manages the project 'Beyond the event horizon: tools to explore local energy transformations' and contributes to 'Decreased CO2-emissions in flight-intensive organisations' and 'HabitWise - Creating Sustainable Everyday Habits.' She teaches courses including 'ICT and Sustainability,' 'Leading Complex Change Processes,' and 'Sustainable ICT in Practice,' while examining various degree projects across computing disciplines. As co-leader of the Sustainable Futures Lab, Dr. Eriksson fosters interdisciplinary research that examines how digital technologies can support sustainable practices and transitions. The lab serves as a hub for innovative approaches to sustainability challenges through human-centered computing research.

• Sustainability
• Human-Computer Interaction
• ICT for Sustainability
• +6 more

Federica Viola is a Researcher at Linköping University, affiliated with the Department of Health, Medicine and Caring Sciences (HMV) and the Division of Diagnostics and Specialist Medicine (DISP). She is part of the Cardiovascular Magnetic Resonance Group (CMR) and the HEART4FLOW initiative. Her primary research focuses on improving cardiovascular 4D flow MRI data quality, hemodynamic modeling, and applying deep learning techniques for automated analysis in clinical settings. Key affiliations include the Center for Medical Image Science and Visualization (CMIV), which develops advanced imaging tools for healthcare. She collaborates with faculty members such as Professors Tino Ebbers, Petter Dyverfeldt, and Carljohan Carlhäll on projects integrating imaging and computational models to study cardiovascular diseases like hypertension and diabetes. Her research emphasizes personalized medicine, combining 4D flow MRI with mathematical models to assess diastolic function, quantify blood flow dynamics, and evaluate treatment effects. Recent work addresses challenges in reproducibility of cardiac models and automated segmentation using AI. Her contributions span cardiovascular imaging technology, hemodynamic analysis, and interdisciplinary collaborations in the Circulation and Metabolism (CircM) strategic network. She actively publishes in journals like Scientific Reports , Journal of Cardiovascular Magnetic Resonance , and Frontiers in Cardiovascular Medicine .

• Cardiovascular Imaging
• 4D Flow MRI
• Cardiovascular Modeling
• +5 more

Mario Romero is a Senior Associate Professor and Head of Unit at the Department of Science and Technology (ITN) within Linköping University. His research focuses on immersive visualization, media and information technology, and advanced manufacturing processes. He leads initiatives in digital twinning for production lines, leveraging 3D Gaussian splatting and vision models. Currently holds the position of Head of Unit, overseeing academic and research activities within his department. No scientific awards or grants are explicitly mentioned in the provided text. Mario Romero has no listed advisees or students in the given data. His work is affiliated with the Department of Science and Technology (ITN) at Linköping University, though specific lab or team affiliations are not detailed here.

• Immersive Visualization
• Media and Information Technology
• Computer Science
• +3 more

Ulrica Nylen is an Associate Professor in Business Administration (Management) at Umeå School of Business, Economics and Statistics (USBE), Umeå University. Her work focuses on teaching organizational behavior, leadership, and qualitative research methods, while actively collaborating with societal actors on applied research projects. Research Interests: She investigates professional identity development, particularly among Swedish police officers in international roles; interprofessional teamwork in human services sectors; and collaboration dynamics across organizations. Her methodological focus includes advancing qualitative research techniques. Current projects include studies on women entrepreneurship in Västerbotten and university-society partnerships. Awards: Nordeas thesis award (Diversity and Inclusion) Collaborations & Grants: Engages in multi-stakeholder projects with public agencies and NGOs, emphasizing applied research. Notable collaborations include evaluations of healthcare delivery systems and social service innovations. Labs/Teams: Works within interdisciplinary teams at USBE and partners with regional organizations like Västerbottens läns landsting to implement research into practice.

• Management
• Organizational Behavior
• Leadership Studies
• +4 more

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +5 more

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +3 more

Alexandra Herlitz is an Associate Professor in Art History and Visual Studies at the Department of Cultural Sciences, University of Gothenburg. She also serves as a museum educator at Gothenburg Museum of Art since 2006. Doctoral degree in Art History (2013), University of Gothenburg Academic training at University of Stuttgart, Heinrich-Heine University Düsseldorf, and University of Hamburg Her research focuses on 19th-century Scandinavian art historiography , artists' colonies , artistic national identity , and transnational art networks between Scandinavia and Germany. She explores these themes through publications, podcast episodes, and archival research. Recent publications highlight Nordic rural artists' communities , gender dynamics in art history , and critical analysis of art forgery . Her work spans from 17th-century Scandinavian art to contemporary feminist perspectives , with particular attention to under-documented networks and alternative historiographies. Herlitz co-hosts the art history podcast Konsthistoriepodden , covering topics from medieval religious art to modernist movements , demonstrating her commitment to public art education and multimedia scholarship .

• Critical historiography
• Scandinavian art history
• Sociology of art
• +4 more

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +8 more