Anna Dreber is the Johan Björkman Professor of Economics at Stockholm School of Economics and Editor at the Journal of Political Economy Microeconomics. Her research focuses on meta-science, investigating the credibility of scientific results through replication studies and prediction markets. She has led large-scale replication projects in experimental economics, psychology, and social sciences. Additional research examines gender differences in economic preferences and hormonal influences on decision-making. She teaches Gender Economics and develops meta-science methodologies through Lab2 research hub and Institute for Replication.
Anna Dreber Almenberg is the Johan Björkman Professor of Economics at the Stockholm School of Economics (SSE), specializing in meta-science and behavioral economics. She holds a chaired professorship and is actively involved in advancing research credibility through initiatives like Lab2 and the Institute for Replication. Her work focuses on replication studies, predicting replication outcomes, and investigating economic preferences influenced by biological factors like testosterone. She serves as an Editor at the Journal of Political Economy Microeconomics, emphasizing credible results over clear outcomes. Dr. Dreber Almenberg’s research spans experimental economics, including large-scale studies such as a testosterone administration trial involving 1,000 participants. She is a Wallenberg Scholar and a member of prestigious academies (KVA and IVA). Her recent work critiques selective reporting of placebo tests in economics and explores design heterogeneity in replications. Her key contributions include high-powered replications of asset market results and collaborations on hormone administration studies (e.g., contraceptive pill effects). She advocates for pre-registration and transparency in research, reflected in her editorial role and involvement with journals like Nature Human Behaviour.
Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.
Andrey Anikin is an Associate Professor and Researcher in Cognitive Science at the Department of Philosophy, Lund University. His work focuses on vocal communication and emotions, exploring how the voice conveys information beyond linguistic codes through nonverbal vocalizations and acoustic phenomena. Research Interests Dr. Anikin investigates how vocal qualities like roughness, laughter, and screams convey emotional and social information. His research takes a cognitive and biological approach, examining sensory biases and auditory attention in vocal communication. His work aims to illuminate the evolutionary origins and universal features of vocal communication across human cultures and animal species. Key areas include emotion perception, nonverbal vocalizations, acoustic analysis, and vocal communication systems. Research Output Trends Dr. Anikin's recent publications (2025) demonstrate a strong focus on acoustic properties of vocalizations across species. His work bridges biology, psychology, and acoustics with methodological innovations in analyzing nonlinear phenomena, voice roughness, and formant structures. His research shows increasing interdisciplinary collaboration, particularly with zoologists and signal processing experts, while maintaining a core focus on the cognitive mechanisms underlying vocal communication. Research Projects What makes baby cries impossible to ignore? (2024-2026): Funded by the Swedish Research Council, this active project investigates the sensory mechanisms behind infant cry perception. Sensory biases in nonverbal communication (2021-2023): A completed project funded by the Swedish Research Council that examined how sensory systems shape nonverbal communication. Research Environment Dr. Anikin is affiliated with the Cognitive Zoology Group and the Lund University Cognitive Science (LUCS) program. He contributes to the LU Profile Area: Natural and Artificial Cognition. His work connects with the UN Sustainable Development Goals through interdisciplinary research on communication and cognition, with implications for understanding human well-being and social interaction.
Ericka Johnson is a Professor and Deputy Prefect at Linköping University, working within Gender Studies in the Department of Thematic Studies. She is affiliated with the Center for Medical Humanities and Bioethics (CMBS), Bodies Hub, and the P6: Body, Knowledge, Subjectivity research collective. Her work bridges Science & Technology Studies, medical humanities, and gender studies, with a focus on how data representation intersects with AI systems and how technologies 'refract' invisible discourses to make them visible. Johnson's research program investigates how the world becomes data, exploring connections between ontologies, epistemologies, and AI. She employs feminist science studies frameworks to examine medical technologies and material-discursive practices around the body. Her metaphor of refraction—comparing how technologies reveal hidden discourses to how prisms refract light into visible spectra—has become influential in feminist technoscience research. She is particularly known for identifying 'intersectional hallucinations' in synthetic medical data, where AI systems generate data that misrepresents complex, overlapping identities. Her major projects include 'Social complexity and fairness in synthetic medical data' (funded by WASP-HS and Vinnova), which examines how machine learning-generated data can overrepresent 'standard' patients while underrepresenting minorities, and 'The Constant Torment' project exploring prostate anxiety and its relationship to masculinity, resulting in her book 'A Cultural Biography of the Prostate.' Her recent publications span critical data studies, human-robot interaction, and the sociotechnical dimensions of AI, consistently examining how technologies shape and are shaped by social, cultural, and gendered contexts. As a supervisor, Johnson mentors doctoral students Isabel García Velázquez, Alexandra Gribble, and Dominika Lisy, as well as postdoctoral researcher Maria Arnelid. Her research is supported by major grants from WASP-HS (NetX) and Vinnova, focusing on fair and representative synthetic data, and she participates in the Wallenberg Autonomous Systems Program (WASP) Humanities and Society initiative. Johnson is actively involved in interdisciplinary research communities including the Center for Medical Humanities and Bioethics, Bodies Hub (researching bodies, identity, and gender), and the P6 research collective. These frameworks support her collaborative work at technology's intersection with gender, society, and healthcare, with practical implications for developing more equitable AI systems in medical contexts.
Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.
Professor Oskar Hansson is a senior consultant neurologist at Skåne University Hospital and full professor of neurology at Lund University, Sweden. He leads the Swedish BioFINDER studies, focusing on early diagnosis of Alzheimer's and Parkinson’s diseases through biomarker development. Co-director of Lund University's neuroscience research area, he also oversees clinical research at the Memory Clinic. Lund University (2017-present): Full Professor of Neurology Skåne University Hospital (2012-present): Senior Consultant Neurologist His research emphasizes clinical and translational studies, particularly in Neurodegenerative Diseases , Biomarker Development , and Neuroimaging . Key contributions include validating Tau PET imaging and blood-based biomarkers for early Alzheimer's detection. Recent publications highlight applications in Alzheimer's disease subtyping , polygenic risk scores , and Lewy body disorder biomarkers (2025 publications in Nature Communications , The Lancet , and Alzheimer's Research and Therapy ). Current projects include active research on amyloid immunotherapies and tau pathology biomarkers. 2024 : Torsten Söderberg Professorship 2023 : De Leon Prize, ERC Advanced Grant, NIH/NIA R01 grant 2023 : Elsa and Alfred Eriksson Award Hansson leads the Clinical Memory Research platform and oversees multiple initiatives including the MultiPark Parkinson's research program and Proactive Ageing profile area at Lund University.
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 .
Hanna Boström is an Assistant Professor at the Department of Chemistry, Stockholm University , leading a research group focused on crystal engineering and structure-property relationships in coordination polymers, particularly Prussian blue analogues and Hofmann complexes . Her work bridges fundamental crystallography with application-driven materials science, emphasizing switchable properties under variable conditions like temperature and pressure. She employs techniques such as X-ray crystallography and magnetic measurements to explore materials for environmental and sustainable chemistry applications. Research Focus : Spin crossover, polar materials, synthesis-structure correlations, Jahn-Teller distortions Group Members : PhD students Lara Janus and Elina Elvelo Key Projects : "Tilt engineering of Prussian blue analogues towards multiferroic materials" Publications highlight her contributions to understanding negative thermal expansion, X-ray/radiation effects, and defect-driven properties in molecular frameworks. While no specific awards are listed, her work has attracted attention in sustainable material synthesis and environmental applications.
Karl Palmskog is a Lecturer at KTH Royal Institute of Technology in the Division of Theoretical Computer Science and the STEP research group. His work focuses on program verification and proof engineering, with particular emphasis on developing techniques and tools based on proof assistants for constructing functionally correct and secure software systems. Palmskog received his Ph.D. in Computer Science in 2014 from KTH, advised by Mads Dam, and his M.Sc. in Computer Science and Engineering from KTH in 2007. Prior to his current position, he was a postdoc at The University of Texas at Austin and University of Illinois at Urbana-Champaign. His research interests span programming languages, software engineering, and formal verification, with a particular focus on developing techniques and tools based on proof assistants. He is an avid user of the Coq proof assistant for both proving and programming, often complemented by OCaml, and also utilizes HOL4 and other ML family dialects. His work bridges theoretical foundations with practical applications, particularly in the domains of blockchain systems, distributed systems, and automotive software verification. Analysis of his recent publications reveals a strong focus on Coq-based verification, with significant contributions to proof engineering tools and methodologies. His work includes developing tools for regression proving, change impact analysis, mutation testing for Coq projects, and lemma name suggestion using deep learning. There's also a growing trend toward applying formal methods to real-world systems like blockchain protocols and automotive software. Palmskog has been involved in several research projects, including Coq-community Proof Engineering and Distributed Components. His past projects include Trustfull (SSF), Model-based Event Driven Scalable Programming for the Mobile Cloud (NSF), Highly Adaptable and Trustworthy Software (EU FP7), and 4WARD Future Internet (EU FP7). As an educator, Palmskog has served as examiner, course responsible, teacher, and assistant for various courses including Algorithms, Data Structures and Complexity; Degree Projects; Game Theory; Parallel and Distributed Computing; and Programming Paradigms. His work on Chip, a Coq formalization of change impact analysis, demonstrates his commitment to creating practical, certified tools that bridge formal methods with software engineering practice.
Anna Sparrman is a Professor at the Department of Thematic Studies - Child Studies (TEMAB) at Linköping University, Sweden, and holds a concurrent position as Professor II in Children's Culture at the Department of Art and Cultural Studies, Inland Norway University of Applied Sciences (2021-2025). Her academic work bridges visual culture, childhood studies, consumption, and sexuality, with a particular focus on how norms and values are created in the everyday practices of children's lives. Sparrman's research is unified by the concept of visual culture as a social theory that questions taken-for-granted aspects of visual everyday practices. She examines how norms and values are created and reproduced through visual interactions: Who gets to look at whom and who gets to be seen in society? Her work focuses on children aged 6-12 years and how children "are made" both by children themselves and through societal conceptions of childhood. She has developed the theoretical framework "Child Studies Multiple," which views children as unstable, changing, hybrid, and complex rather than solid and universal. Her research spans interconnected fields including children and sexuality, children and consumption, children's cultural heritage, and visual methodology. Her current projects investigate how to integrate children's cultural heritage into Swedish cultural heritage and examine children as professional influencers and internet celebrities in the digital age. Methodologically, she combines visual ethnography, photo-elicited focus group discussions, image analysis, and internet-based visual methods, with special attention to the productivity of research methods themselves. Sparrman serves on the Advisory Board for the Childism Institute, the Editorial Board for Childhood journal, and the Advisory Board for the research project "(Re)configuration of parenthood: Political agendas entangling everyday family life" at Roskilde University. She has also participated in media discussions on topics such as "The pedophile gaze" and children's rights to cultural heritage participation.
Olli-Pekka Kallioniemi is a Professor of Molecular Precision Medicine and Director of Science for Life Laboratory (SciLifeLab) at Karolinska Institutet. He leads the Precision Cancer Medicine research group within the Department of Oncology-Pathology, focusing on translational research to improve cancer treatment through ex vivo drug testing and multi-omics profiling. His work bridges laboratory discoveries with clinical applications, emphasizing actionable insights for personalized therapy. Research Interests: Dr. Kallioniemi’s group develops functional precision medicine approaches to identify therapeutic vulnerabilities in cancers like AML, ovarian, prostate, and renal cancers. Techniques include patient-derived 2D/3D cell models, organoids, and comprehensive genomic/drug-response profiling. Their work progresses through three translational phases: model feasibility (Phase I), representativity optimization (Phase II), and clinical actionability (Phase III). Selected Achievements: - Directed 75 million SEK in grants from the Swedish Cancer Society (2022) - Pioneered functional drug testing platforms for solid tumors and hematologic malignancies - Coordinated multi-institutional collaborations between Karolinska Institutet and the Institute for Molecular Medicine Finland Labs/Teams: His group operates at SciLifeLab (Stockholm) and the Institute for Molecular Medicine Finland (Helsinki), with sub-teams like the Functional Systems Oncology group led by Dr. Tom Erkers. Core personnel include postdoctoral researchers, PhD students, and clinical assistants.
Yumin Link is an Adjunct Associate Professor at Linköping University's Department of Biomedical and Clinical Sciences (BKV), affiliated with the Faculty of Medicine and Health Sciences. He is part of the Center for Social and Affective Neuroscience (CSAN) and the Division of Cell and Neurobiology (CNB). His research focuses on medical imaging techniques like Optical Coherence Tomography (OCT), neurological disorders, and clinical neuroscience. Key areas include retinal imaging in idiopathic intracranial hypertension, multiple sclerosis progression, and intermachine reproducibility in OCT measurements. Publications (2023–2025) highlight advancements in neuroimaging and diagnostic reliability. No explicit awards or grants are listed, though ongoing collaborations with clinical teams suggest active research participation. Yumin Link advises via two emails: yumin.link@liu.se (academic) and yumin.link@regionostergotland.se (clinical).
Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.
Marjan Firouznia is a Principal Research Engineer at Linköping University , affiliated with the Division of Diagnostics and Specialist Medicine (DISP) under the Faculty of Medicine and Health Sciences . With a PhD in Electrical Engineering from Amirkabir University of Technology and postdoctoral experience at institutions like Case Western Reserve University, she specializes in advancing machine learning models for precise segmentation of cardiac structures including the left atrium , epicardial fat , and fibrosis using CT and MRI scans. Her work aims to improve diagnostic accuracy and treatment planning in cardiovascular care. Marjan's research focuses on medical imaging , deep learning , and computational anatomy , with recent publications on FractalRG , FK-means , and Poincare-guided UNet for cardiac structure segmentation. Her academic contributions span 15 recent publications , emphasizing fractal geometry , chaos theory , and optimization algorithms in biomedical applications. She actively develops open-source datasets and tools, such as the FK-means codebase , to support reproducibility in medical AI research.
