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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.

Anders HanssonView profile

Professor

• Climate Science
• Environmental Policy
• Sustainability
• +4 more

Anders Hansson is a Professor and Head of Unit in the Department of Thematic Studies - Environmental Change (TEMAM) at Linköping University. His research focuses on interdisciplinary studies of Negative Emission Technologies (NETs), particularly BECCS and biochar, addressing climate change mitigation through technical, social, and economic lenses. He leads the Linköping University Negative Emission Technologies (LUNETs) research group, funded by major grants from the Wallenberg Foundation, Swedish Research Council, and others. His work explores the feasibility, governance, and ethical implications of NETs in Sweden, the Nordic region, Tanzania, and globally. Education: PhD from Linköping University, with prior roles including Senior Lecturer (2008), Docent (2013), and Senior Associate Professor (2021). Research Interests: Climate policy, carbon dioxide removal, socio-technical systems, sustainability transitions, and global environmental governance. He critically examines NETs deployment in developing countries and the role of Integrated Assessment Models (IAMs) in climate science. Grants & Projects: Over SEK 50 million in competitive grants, including projects on climate emergency temporalities, solar geoengineering transparency, and BECCS implementation barriers. Notable funders: Marcus and Amalia Wallenberg Foundation, Swedish Energy Agency, Formas. Labs/Teams: Leads CSPR (Climate Science and Policy Research), STRIPE (Socio-technical research of infrastructures), and collaborates with global institutions like the Leibniz Institute. Engages stakeholders through dialogue platforms to integrate societal perspectives into research.

• Machine Learning
• Statistical Inference
• Sequential Monte Carlo Methods
• +3 more

Alessandro Mastrototaro holds a PhD in Applied and Computational Mathematics from KTH Royal Institute of Technology. His research focuses on advancing statistical learning methods, particularly sequential Monte Carlo (SMC) and variational inference techniques for state-space models. He currently teaches Regression Analysis (SF2930) at KTH, serving as course responsible and instructor. His work emphasizes online learning algorithms that enable real-time parameter estimation and particle proposal adaptation in dynamic data environments. Key contributions include the development of the Online Variational Sequential Monte Carlo algorithm and the ALVar estimator for adaptive variance estimation in particle filters. Publications span machine learning conferences (e.g., ICML) and journals like the Journal of the American Statistical Association, showcasing expertise in computational statistics and algorithmic innovation.

• Radiation Therapy
• Brachytherapy
• Medical Physics
• +4 more

Åsa Carlsson Tedgren is a Researcher at the Department of Oncology-Pathology , Karolinska Institutet, and leads the Clinical Radiation Therapy research group. She is affiliated with the Karolinska University Hospital's radiotherapy department, collaborating with oncologists and physicists on brachytherapy , external beam radiation therapy , and dual-energy CT applications. Her work spans radiation dosimetry , treatment planning , and detector response analysis . She supervises numerous PhD students, including Freja Alpsten Simon Dahlander Elias Lindbäck Ricardo Palanco Zamora others and contributes to ESTRO and AAPM educational programs. Her recent publications focus on heterogeneity handling , automated treatment plan validation , and prognostic modeling in brachytherapy. Key research themes include Model-based dose calculation algorithms Monte Carlo simulations Prostate and ocular tumor treatment optimization Clinical audits and calibration standards Machine learning for medical imaging with a strong emphasis on technical innovation and radiation safety in clinical settings.

• Applied Mathematics
• Materials Science
• Computational Modeling
• +3 more

Jens Wittsten is a Researcher affiliated with the Department of Engineering at the University of Borås' Academy of Textiles, Technology and Economics. He serves as the main supervisor for doctoral student Markus Klintborg and holds office in room C801. His work bridges applied mathematics, materials science, and computational engineering. Research interests include modeling phenomena in moiré heterostructures (e.g., twisted graphene layers), semiclassical quantization in strained lattices, and seismic data processing techniques. He has contributed to understanding electronic phase transitions, magic angles in bilayer graphene systems, and numerical methods for wave propagation modeling. His publication trends reflect interdisciplinary focus: recent works address both fundamental physics (e.g., Hofstadter butterfly studies) and applied engineering challenges (e.g., warehouse optimization via GPU-accelerated routing). Jens advises one doctoral candidate and maintains an active research portfolio spanning over 25 peer-reviewed articles since 2010. His methodological innovations include contributions to seismic apparition techniques and dealiasing algorithms.

Shahina BegumView profile

Professor

• Artificial Intelligence
• Multimodal Machine Learning
• Explainable AI (XAI)
• +4 more

Shahina Begum is a Professor in Artificial Intelligence at Mälardalen University (MDU), leading the Artificial Intelligence and Intelligent Systems group. She focuses on developing intelligent systems for medical and industrial applications, with expertise in multimodal machine learning, explainable AI, and decision support systems. She holds a PhD in AI from MDU (2011) and has led numerous research projects, including initiatives funded by the Swedish Knowledge Foundation and KK-Stiftelsen. Her teaching roles include designing and leading over 21 courses/learning modules in AI and machine learning, both on-campus and distance-based. She is the main responsible for AI content in MDU's 'Bachelor program in Applied AI' and has spearheaded MOOC courses like 'Basic Knowledge on ML' and AIClass. She also leads industrial collaboration projects such as IntoDeep, KIT, and PROMPT, addressing Industry 4.0 challenges. Research interests span sustainable AI development, trustworthy AI systems, and AI for manufacturing/predictive maintenance. She has received notable recognition, including the Prospect grant (2011) and inclusion in the Royal Swedish Academy's top 100 sustainable AI researchers (2020). Her work emphasizes bridging academic research with industry needs through collaborative initiatives. She has published extensively on multimodal learning, explainable AI, and applications in transportation, healthcare, and manufacturing. Her awards include the Best Paper Award at the 2024 conference for work on second-order learning. She actively contributes to academic governance, serving on evaluation committees, conference organizing committees, and funding review boards.

• Breast cancer research
• Translational oncology
• Molecular analyses of tumor tissue
• +7 more

Theodoros Foukakis serves as Associate Professor and Senior Physician at Karolinska Institutet's Department of Oncology-Pathology in Stockholm, Sweden. He leads the Translational Breast Cancer Research Group, a multidisciplinary team comprising clinicians, bioinformaticians, epidemiologists, biostatisticians, AI engineers, and molecular biologists. His research focuses on understanding treatment resistance mechanisms in breast cancer through molecular analyses of tumor tissue and blood samples, with the goal of developing new predictive biomarkers for treatment response. Foukakis' research interests center on translational breast cancer research, particularly in the areas of neoadjuvant therapy, treatment resistance mechanisms, and biomarker development. His work spans both HER2-positive and triple-negative breast cancer subtypes, with significant contributions to clinical trials investigating novel treatment approaches including immunotherapy combinations and antibody-drug conjugates. He has pioneered research in applying machine learning and AI techniques to analyze tumor microenvironment characteristics and optimize treatment selection based on molecular profiles. His work bridges clinical practice with cutting-edge genomic and computational approaches to personalize breast cancer treatment. Analysis of his recent publications (2024-2025) reveals a strong focus on precision oncology approaches, with particular emphasis on biomarker development, treatment response prediction, and optimizing therapeutic strategies through AI applications. His work spans clinical trials (PANTHER, KEYNOTE, ARIADNE), biomarker discovery (SAMHD1, immune microenvironment characterization), and implementation of machine learning models for treatment optimization. A significant portion of his research addresses the critical challenge of balancing treatment efficacy with minimizing toxicity through tailored approaches. Foukakis actively mentors doctoral students and postdoctoral researchers in his translational breast cancer research group. His grant portfolio includes leadership of the ARIADNE trial, a randomized phase IIB study investigating trastuzumab deruxtecan and biology-driven selection of neoadjuvant treatment for HER2-positive breast cancer. His research has been supported by multiple national and international funding sources focused on advancing precision medicine in breast cancer care. The Translational Breast Cancer Research Group led by Foukakis operates as a comprehensive research ecosystem that integrates clinical data with molecular and computational analyses. The group collaborates extensively with other research teams at Karolinska Institutet and participates in numerous international clinical trials. Their work on the ARIADNE study represents a novel approach to treatment selection based on intrinsic molecular subtypes as determined by Prosigna® analysis, potentially enabling both treatment de-escalation for some patients and escalation for others based on molecular characteristics.

• Photogrammetry
• Bundle Adjustment
• 3D Measurements
• +3 more

Niclas Borlin is an Associate Professor at the Department of Computing Science, Umeå University. His research specializes in photogrammetry, bundle adjustment algorithms, and 3D measurement techniques, with applications spanning computer vision, remote sensing, and geospatial analysis. He maintains an active role in academic research with no indications of retirement or part-time status. Research Focus: Borlin's work centers on developing computational methods for photogrammetric data processing. Key areas include: Bundle adjustment optimization and modular algorithm design Terrestrial and UAV-based photogrammetry for environmental/architectural documentation Laser scanning calibration and error quantification Medical imaging techniques for orthopedic applications Awards: Talbert Abrams Award (2020) for contributions to photogrammetry His publications demonstrate consistent focus on improving accuracy in 3D measurements through computational innovations, with recent work emphasizing open-source tools like the Damped Bundle Adjustment Toolbox (DBAT).

• Complex Analysis
• Algebraic Geometry
• Holomorphic Automorphisms
• +2 more

Andreas Lind is an Associate Professor at Mid Sweden University, affiliated with the Department of Engineering, Mathematics and Subject Didactics (IMD) in Sundsvall. His research focuses on complex analysis and algebraic geometry. Research Interests: Holomorphic Automorphisms Danielewski Surfaces Stein Spaces Polynomial Hulls Envelopes of Holomorphy Complex Dynamics Publications showcase expertise in geometric analysis, holomorphic group actions, and complex structure preservation. Key works include the 2023 study on Lie group actions on Danielewski surfaces and a 2015 paper detailing the overshear group structure.

Daniel GnadView profile

Assistant Professor

• Artificial Intelligence
• AI Planning
• Model Checking
• +3 more

Daniel Gnad is an Assistant Professor at Linköping University's Department of Computer Science (IDA), working within the Artificial Intelligence and Integrated Computing Systems (AIICS) division. He specializes in "Planning and model checking" within artificial intelligence, focusing on theoretical foundations and algorithmic approaches to complex planning problems. Dr. Gnad completed his Computer Science studies at Saarland University, earning his MSc before continuing as a PhD student under Prof. Jörg Hoffmann. In 2022, he joined the RLPLab at Linköping University as a postdoctoral researcher, becoming an assistant professor in 2023. His research primarily centers on AI planning, model checking, and decoupled state-space search methodologies. His recent publications demonstrate significant advancements in planning algorithms, pattern databases, and numeric planning techniques. Gnad's work bridges theoretical computer science with practical applications in artificial intelligence, particularly in developing more efficient search algorithms for complex planning problems. His research often involves transforming planning problems into more tractable forms through abstraction techniques and novel search topologies. Dr. Gnad has received numerous prestigious awards recognizing his contributions to the field: ICAPS 2022 Best Dissertation Award for "Star-Topology Decoupled State-Space Search in AI Planning and Model Checking" SoCS 2022 Best Paper Award for "Additive Pattern Databases for Decoupled Search" SPIN 2018 Best Paper Award for "Star-Topology Decoupling in SPIN" Dr.-Eduard-Martin-Preis for the best dissertation of the Faculty for Mathematics and Computer Science in 2021 Multiple recognitions at the International Planning Competition (IPC) 2018 for the "Saarplan" planning system As a researcher at AIICS, Dr. Gnad contributes to Linköping University's strong position in artificial intelligence research. His work is part of the broader research activities within the Wallenberg Autonomous Systems Program (WASP), one of Sweden's largest research initiatives. While specific details about his advisees aren't provided in the available information, his research group likely contributes to the department's doctoral programs in computer science. Dr. Gnad's laboratory work focuses on developing and analyzing planning algorithms, with applications ranging from theoretical computer science to practical AI systems. His recent integration of techniques like pattern databases with numeric planning represents an important direction in making planning algorithms more versatile for real-world applications.

• Signal Processing
• Statistical Signal Processing
• System Identification
• +5 more

Tomas McKelvey is a Full Professor and Deputy Head of the Department of Electrical Engineering at Chalmers University of Technology, where he leads the Signal Processing research group. He has been with Chalmers since 2000 and has held a full professor position in Signal Processing since 2006. Professor McKelvey received his Electrical Engineering education at Lund University between 1987 to 1991 and earned his PhD in Automatic Control at Linköping University in 1995. Between 1995 and 1999, he held research and teaching positions at Linköping University, where he became docent in 1999. He was a visiting researcher at the University of Newcastle, Australia between 1999 and 2000 before joining Chalmers University of Technology. Professor McKelvey's research spans multiple domains within signal processing and control systems. His primary interests include model-based signal processing, statistical signal processing, system identification, and automatic control. His work has significant applications across several fields including radar systems, biomedical engineering, power systems, and automotive propulsion. He has developed innovative methods for signal processing in challenging environments, particularly in radar and power systems applications, often bridging theoretical advances with practical implementation challenges. His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods, particularly for applications in radar systems, power grid stability, and automotive control. His work shows increasing sophistication in handling non-stationary signals, developing efficient computational methods, and creating robust solutions for real-world engineering problems. Professor McKelvey has been actively involved in numerous research projects, as indicated by his extensive publication record spanning multiple disciplines. His leadership position as Deputy Head of Department reflects his significant standing within the academic community. His research group maintains strong connections with both academic and industrial partners, as evidenced by the applied nature of much of his work. The cross-disciplinary nature of his publications indicates collaborations across engineering fields and with medical researchers, particularly in the biomedical applications of microwave technology.

• Wave Energy Converters
• Marine Technology
• Computational Fluid Dynamics (CFD)
• +3 more

Claes Eskilsson is a researcher at Chalmers University of Technology , specifically in the Department of Mechanics and Maritime Sciences under the Marine Technology division. His work focuses on computational fluid dynamics (CFD) , wave energy converters , and mooring system dynamics for marine applications. Research Projects: MIDWEST: Multi-fidelity decision tools for wave energy systems (2015-2018) Assessment of tidal turbine noise pollution (2015-2016) Including nonlinear/viscous effects in wave energy modeling (2015-2017) Forankringslösninger for wave energy devices (2015-2017) SDWED: Structural design of wave energy devices (2013-2014) His research interests include: Computational modeling of marine systems Wave energy converter hydrodynamics High-order numerical methods (spectral/hp elements, Discontinuous Galerkin) Cavitation and erosion analysis in marine flows Multiphysics modeling of floating structures The publications span topics in wave energy converter dynamics, mooring system analysis, and CFD methodology. Key trends include 2013-2015 developments in spectral/hp element methods for coastal engineering, and 2015-2020 advancements in multi-fidelity modeling of ocean energy systems. He has collaborated extensively with institutions such as Royal Institute of Technology (KTH) , Lund University , and Technical University of Denmark (DTU) , with funding from agencies including the Swedish Energy Agency and Danish Energy Agency .

Rickard KarlssonView profile

Lecturer

• Swedish as a second language
• Learner language assessment
• Language development in adults
• +4 more

Dr. Rickard Karlsson works as a Lecturer at Linköping University's Department for Swedish as a Second Language, Rhetoric and Language Support (SAROS) under the Department of Culture and Society (IKOS). His teaching focuses on Swedish language didactics, grammar, and assessment of learner languages, with supervision across academic levels. PhD in Languages and Cultures of Europe Upper Secondary School Teacher in Swedish as a Second Language Research spans empirical analysis of adult language acquisition , historical linguistics , and multilingualism ideologies . Google Scholar publications reveal interdisciplinary contributions to particle filter algorithms and automotive sensor systems from 2001-2025. Notable collaborations include Fredrik Gustafsson and Per-Johan Nordlund. Recent publications (2025-2016) merge automotive engineering and historical Linguistics, covering tire diagnostics, cultural exchange patterns, and vibration-based navigation. This dual expertise reflects his transition from technical research to language education, maintaining academic connections across disciplines.

• Hydropower Turbines
• Cavitation Modeling
• Computational Fluid Dynamics (CFD)
• +7 more

Håkan Nilsson is a Full Professor in Fluid Dynamics at Chalmers University of Technology. His research focuses on computational fluid dynamics (CFD) with applications in hydropower systems, particularly turbine flow analysis, cavitation modeling, and generator cooling air dynamics. He utilizes OpenFOAM for numerical simulations and develops advanced algorithms for mesh deformation and flow control. Academic Rank: Full Professor Institution: Chalmers University of Technology Primary Research: Hydropower Turbines, Cavitation, CFD, Machine Learning, Multiphase Flow His recent work integrates machine learning with CFD for optimizing turbine operations and predictive maintenance in hydropower plants. Key projects include ALPHEUS and investigations into contra-rotating pump-turbine systems for low-head energy storage. Collaborations span experimental validation with experts in PIV, laser Doppler velocimetry, and industrial partners in renewable energy. Research trends highlight 15+ years of publications on turbulence modeling, vortex dynamics, and fluid-structure interaction in hydraulic systems. Sub-fields include Francis turbine transients, Kaplan turbine rotor-stator interactions, and applications in biomedical fluid dynamics and welding processes. Scientific awards are not explicitly mentioned.

• Inverse Problems
• Machine Learning for Imaging
• Numerical Analysis
• +5 more

Ozan Öktem is a Professor at KTH Royal Institute of Technology , specializing in applied mathematics with a focus on inverse problems, machine learning, and numerical analysis. He works in the Division of Numerical Analysis, Optimization and Systems Theory and develops theory and algorithms for solving inverse problems, particularly in medical imaging and cryogenic electron microscopy (Cryo-EM). His research integrates mathematical analysis, machine learning, and numerical methods to address challenges in recovering hidden model parameters from indirect observations. He emphasizes regularization techniques to stabilize ill-posed problems and computational feasibility for large-scale applications. Key areas include tomographic reconstruction, deep learning-based methods, and applications in biomedical imaging. Recent publications highlight his work on learned primal-dual architectures for CT, Riemannian geometry in protein dynamics analysis, and regularization strategies for Cryo-EM. Collaborations span computational biology, medical imaging, and optimization. He serves as course responsible for advanced courses in differential equations, inverse problems, and scientific computing.