جست‌وجوی استاد و پژوهشگر

Måns Magnussonمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +۴ مورد دیگر

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

• Electromagnetism
• Waveguide Theory
• Nonlinear Optics
• +۳ مورد دیگر

Eugene Smolkin is a Lecturer at the University of Gävle , specializing in Electromagnetism and Mathematical Physics . His research focuses on waveguide theory, nonlinear optics, and numerical methods for electromagnetic wave propagation in complex materials. Primary affiliation: University of Gävle Academic rank: Lecturer Research areas: Electromagnetism, Waveguide Theory, Nonlinear Optics Smolkin’s work investigates TE-polarized waves , leaky wave spectra , and graphene-coated structures . He develops numerical methods to analyze electromagnetic modes in inhomogeneous and anisotropic media, with applications in metamaterials and chiral waveguides. His publications (2015–2025) emphasize nonlinear wave propagation , dielectric layers , and inverse problems in open and shielded waveguides. Collaborative efforts with Yury Shestopalov and Yury Smirnov highlight interdisciplinary approaches.

David Black-Schafferمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• Parallel Programming
• +۴ مورد دیگر

David Black-Schaffer is a Professor at Uppsala University's Department of Information Technology, specializing in computer systems research. As of 2023, he serves as Dean of Research for the Faculty of Science and Technology. His work bridges software and hardware innovations to enhance data movement efficiency in computer systems, with applications commercialized through a startup and integrated into industry standards like OpenCL. Black-Schaffer earned his PhD in Electrical Engineering from Stanford University in 2008, focusing on many-core processor programming. His career spans roles at Apple Inc. (contributing to OpenCL standards), postdoctoral research at Uppsala University, and academic progression from assistant to full professor (2010–2017). He has held leadership roles including Head of the Division of Computer Systems (2022) and department representative on the faculty Advisory Committee for Research (2021). His research spans computer architecture, memory systems, parallel programming, and simulation techniques. Recent publications (2024–2020) explore garbage collection, cache optimization, memory contention, NUMA systems, and instruction scheduling. Key trends include software-hardware co-design for power efficiency, reuse-aware data placement, and machine learning for performance modeling. Knut & Alice Wallenberg Foundation: Wallenberg Academy Fellowship Prolongation (2020–2025), Wallenberg Academy Fellow (2016–2021) Swedish Research Council (VR): Project Grant (2019–2024), Young Researcher Grant (2015–2018), Framework Grant (2012–2017) European Research Council: ERC Starting Grant (2017–2022) Teaching Awards: Uppsala Engineering and Science Student Union Pedagogical Prize (2012), Uppsala University Pedagogical Prize (2016), Uppsala Technical Physics Students' Teaching Award (2019) Other Grants: ScalableLearning flipped classroom project (2012–2020), Arm Ltd. collaborations on memory system designs He pioneered flipped-classroom teaching through the ScalableLearning project, impacting over 80,000 students. His research is conducted in collaboration with institutions like Arm Ltd., with past contributions to Apple's OpenCL implementation and UPMARC research center.

• Outdoor Sound Propagation
• Community Noise
• Urban Acoustics
• +۵ مورد دیگر

Jens Forssén is an Assistant Professor in the Department of Technical Acoustics , Vibroacoustics research group at Chalmers University of Technology . His work focuses on outdoor sound propagation , community noise , and urban acoustics , with a particular emphasis on the influence of meteorology , terrain , and soil properties on noise levels. Research Interests: Outdoor acoustics, urban soundscapes, road and train traffic noise, wind turbine noise, noise barriers, auralization techniques, and health impacts of environmental noise. Recent Trends: Analysis of low-frequency noise in urban environments, 3D noise mapping , digital twinning for urban design, and sonic crystal applications in noise reduction. Scientific Contributions: Publications on noise propagation models , auralization methods , traffic noise assessment , and health impact studies related to urban sound environments. Labs & Collaborations: Collaborates with the LISTEN project , SONORUS project , and HOSANNA project to develop urban noise mitigation strategies. His work includes partnerships with researchers like Wolfgang Kropp , Leon Müller , and Maarten Hornikx .

• Epidemiology
• Biostatistics
• Cancer Survival Analysis
• +۳ مورد دیگر

Paul Lambert is a Visiting Professor at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, specializing in population-based cancer survival analysis and biostatistical modeling. His work focuses on flexible parametric models , competing risks , and multistate survival frameworks to address challenges in cancer prognosis and treatment effects. Key research areas: Epidemiology , Biostatistics , Cancer Survival Affiliation: Karolinska Institutet, Sweden His recent publications analyze Nordic cancer trends (2002-2021), cardio-oncology risks , and survival extrapolation in clinical trials. He supervises advanced statistical training and leads grants from the Swedish Research Council and Swedish Cancer Society. Scientific Awards : No explicit honors listed. Grants include projects on statistical methods for cancer registry data (Swedish Research Council, Swedish Cancer Society).

George Baravdishمشاهده پروفایل

دانشیار

• Inverse Problems
• Machine Learning
• Medical Imaging
• +۶ مورد دیگر

George Baravdish is a Senior Associate Professor and Head of Unit at the Department of Science and Technology (ITN), Linköping University. He leads the Physics, Electronics and Mathematics (FEM) unit and holds a PhD in Applied Mathematics from Linköping University (1995), focusing on Ill-posed and Inverse Problems under Prof. V. G. Maz'ya. His research spans Inverse Problems , Machine Learning , and Mathematical Oncology , with applications in medical imaging, signal processing, and telecommunications. Notable projects include Mathematics for Machine Learning (focusing on neural ODEs) and Mathematical Oncology (modeling brain tumor growth). Baravdish has supervised two doctoral students and co-supervised two more. He has secured funding as a main or co-applicant for 15 research projects. His work bridges theoretical mathematics and practical applications in healthcare and technology. Current research themes include: Image Processing : Generalizations of the p-Laplace operator for noise reduction Telecommunications : 3D object reconstruction using Wi-Fi signals Inverse Problems : Theoretical advancements and algorithm development He contributes to interdisciplinary collaborations, including AI-driven solutions for healthcare and signal analysis.

• Biomedical Engineering
• Neuroscience
• Medical Technology
• +۵ مورد دیگر

Jerker Westin is an Associate Professor and Senior Lecturer in Computer and Information Science at Dalarna University's School of Information and Engineering. His work focuses on developing sensor-based technologies and computational methods to improve Parkinson's disease management. He specializes in motor state quantification, algorithmic dosing optimization for levodopa treatment, and wearable sensor systems. His research integrates biomedical engineering, computer science, and clinical neuroscience. Notable contributions include devising motion sensor indices for motor fluctuations and creating algorithms for personalized levodopa administration. He collaborates across disciplines to advance telemedicine frameworks and home-based assessment tools for neurodegenerative disorders. Over 70+ publications demonstrate his expertise in sensor systems for movement analysis, pharmacokinetic modeling of levodopa, and digital biomarkers for Parkinson's progression. His work emphasizes translating technical innovations into clinical practice to enhance patient care.

Björn Forsbergمشاهده پروفایل

استادیار

• Structural Bioinformatics
• Cryo-Electron Microscopy (Cryo-EM)
• Computational Biology
• +۷ مورد دیگر

Björn Forsberg is an Assistant Professor in the Department of Physics, Chemistry and Biology (IFM) at Linköping University, where he leads a research group in structural bioinformatics. He is affiliated with SciLifeLab Linköping and the National Supercomputer Centre (NSC), and is part of the Wallenberg National Program for Data-Driven Life Science (DDLS), supported by the Knut and Alice Wallenberg Foundation. His work bridges computational science and molecular biology, focusing on the development of novel methods for analyzing cryo-electron microscopy (cryo-EM) data. His research centers on understanding molecular life through data-driven approaches. He develops computational tools to analyze ensemble cryo-EM data, using spatial filtering and local correlation metrics to identify sources of structural variation and attribute them to biological mechanisms. His lab leverages high-performance computing resources, including the Berzelius supercomputer, to process large-scale datasets and improve the resolution and interpretability of molecular models. This work has broad implications for understanding diseases like Alzheimer's and for drug discovery. The recent publications highlight a strong trend in advancing cryo-EM methodology, particularly through software development (e.g., RELION) and the integration of machine learning and GPU computing. His work spans fungal metabolism, ion channel dynamics, chloroplast ribosomes, and foundational algorithmic improvements in 3D reconstruction. The research combines structural biology, bioinformatics, and computational physics to extract biological meaning from complex data. Scientific Awards and Recognition: Selected for the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) Supported by the Knut and Alice Wallenberg Foundation through DDLS and the PALS (Program for Academic Leaders in Life Science) network Björn Forsberg is actively building his research team, advising PhD and master’s students, and seeking postdoctoral researchers with backgrounds in computational biology, computer science, or related fields. His lab collaborates with leading experts in structural biology and uses national research infrastructures to push the boundaries of data-driven life science. He has no listed formal grants yet, but his program is funded through major foundation support. His research is conducted at the intersection of the Department of Physics, Chemistry and Biology (IFM), SciLifeLab Linköping, and the National Supercomputer Centre (NSC), forming a multidisciplinary environment for innovation in computational structural biology.

• Computational Fracture Mechanics
• Solid Mechanics
• Damage and Fracture Modeling
• +۳ مورد دیگر

Martin Fagerström is a Professor at the Department of Material and Computational Mechanics, Chalmers University of Technology. His research focuses on computational modeling of damage and fracture in lightweight materials, particularly fiber-reinforced polymers, with applications spanning crashworthiness, sports engineering, and health technology. He serves as Co-director of the Health Engineering Area of Advance and coordinator of Chalmers Sports & Technology. Affiliation: Chalmers University of Technology Department: Material and Computational Mechanics Research Areas: Computational fracture mechanics, damage modeling, composite materials, finite element analysis, sports and health engineering His recent publications emphasize multi-fidelity data fusion, machine learning integration with transfer learning, and advanced finite element techniques for predicting elasto-plasticity and delamination in woven composites. Projects led or co-led include SCALE (circular aluminum alloys), REaL-tIme (anisotropic carbon composites), and LIGHTer Academy (lightweight vehicle technology).

Matthew Hayesمشاهده پروفایل

دانشیار

• Galaxy Formation and Evolution
• Cosmic Reionization
• Interstellar Medium (ISM)
• +۴ مورد دیگر

Matthew Hayes is an Associate Professor in Astrophysics at Stockholm University 's Department of Astronomy and a Wallenberg Academy Fellow. His research focuses on the formation and evolution of galaxies, particularly during the reionization epoch, using multi-wavelength observations (JWST, Hubble, ESO) and computational modeling. Research Pillars: Cosmic reionization, Lyman-alpha emission physics, stellar feedback mechanisms, circumgalactic medium properties, and diagnostics of ionizing radiation Key Instruments: James Webb Space Telescope, Hubble Space Telescope, VLT/MUSE, Nordic Optical Telescope His recent work uses UV spectral diagnostics to trace the ionization state of the intergalactic medium and constrain the escape fraction of Lyman continuum photons. Current teaching includes graduate-level courses on the Physics of the Interstellar Medium (AS7001) and Observational Techniques in Astrophysics II (AS7004) featuring hands-on radio/optical observations. Scientific Contributions: Developed novel methods for measuring ionized bubble sizes around high-redshift galaxies Advanced understanding of Lyman-alpha as a probe for galactic winds and outflows Calibrated UV diagnostic tools for metallicity and ionization parameters in the JWST era Funding Sources: Swedish Research Council, Knut and Alice Wallenberg Foundation, Swedish National Space Board

Sara Blichnerمشاهده پروفایل

پژوهشگر ارشد

• Atmospheric Science
• Aerosol-Climate Interactions
• Biogenic Volatile Organic Compounds
• +۳ مورد دیگر

Sara Blichner is a Postdoctoral Fellow at Stockholm University's Department of Environmental Sciences, focusing on critical atmospheric processes that influence climate. Her research primarily centers on aerosol-cloud-climate interactions, with particular emphasis on biogenic volatile organic compounds (BVOCs) and their role in climate feedback mechanisms. Working within the atmospheric science research group at Stockholm University, she contributes to advancing our understanding of how natural processes affect climate systems. Dr. Blichner's research interests span atmospheric aerosol dynamics, new particle formation processes, and Earth System Model evaluation. She investigates how biogenic emissions from forests influence cloud formation and climate, with significant work on boreal and tropical forest ecosystems. Her research addresses fundamental questions about pre-industrial aerosol conditions, current climate impacts, and future climate projections under changing emission scenarios. She employs both observational data and sophisticated modeling approaches to understand complex atmospheric processes that remain among the largest uncertainties in climate prediction. Analysis of her publication record reveals a strong focus on process-based evaluation of climate models, particularly examining aerosol-cloud interactions in forested regions. Her work demonstrates how observational constraints can reduce uncertainty in Earth System Models, with significant contributions to understanding BVOC-climate feedbacks. Her research spans multiple disciplines including atmospheric physics, climate science, and environmental chemistry, with particular emphasis on nanoparticle growth mechanisms and their climate relevance. While no specific scientific awards are mentioned in the available information, her publications in high-impact journals including Nature Communications, Science Advances, and Reviews of Modern Physics indicate significant contributions to her field. Her research appears to be supported by collaborative projects involving multiple institutions and field measurement campaigns, particularly in boreal forest regions. Dr. Blichner's work contributes to critical climate science questions at the intersection of atmospheric chemistry and climate modeling, with implications for understanding both historical climate conditions and future climate projections as anthropogenic emissions change.

• Product Design
• Geometry Assurance
• Tolerancing
• +۲ مورد دیگر

Martin Roth is a Postdoctoral Researcher in the Geometry Assurance & Robust Design research group at Chalmers University of Technology, working within the Product Development department under the School of Mechanics and Maritime Sciences. His research focuses on geometry assurance of products with mega-cast parts, collaborating with the automotive industry to optimize methods and tools for advanced variation simulation and geometry assurance of complex assemblies. Dr. Roth's primary research interests include: Product Design Geometry Assurance Tolerancing Optimization Simulation His recent scholarly output demonstrates a strong trend toward integrating advanced computational methods with industrial applications, particularly in developing holistic frameworks for tolerancing in product design and closing gaps in the digital thread using the Quality Information Framework standard. His work combines sampling-based tolerance-cost optimization techniques with practical manufacturing challenges, especially for mega-cast aluminum parts in automotive applications. Dr. Roth is actively involved in two major VINNOVA-funded research projects: Digital synchronization of geometry data for efficient value chains (DigiSync) (2024-2027) Geometrical robustness for mega casted aluminum part (GROMCAP) (2023-2026)

• Electrical Engineering
• Bioelectricity
• Computational Electromagnetics
• +۱۲ مورد دیگر

Luca Giaccone is an Associate Professor in the Department of Energy (DENERG) at Politecnico di Torino, where he is also a member of the Academic Senate and the Committee for Teaching Coordination. He is affiliated with the College of Electrical and Energy Engineering and leads the CADEMA Research Group. His academic work focuses on computational electromagnetics and human exposure to electromagnetic fields. Research Interests: Bioelectricity and human exposure to low-frequency electric and magnetic fields Computational electromagnetics and numerical dosimetry Simulation engineering, modeling, and optimal design using MATLAB and Python Electromagnetic compatibility (EMC) and safety of medical devices Wireless power transfer (WPT) systems, especially in automotive applications Recent Research Trends: His recent publications center on data-driven modeling of electric motors, surrogate modeling for fast exposure assessment, and the electromagnetic safety of medical implants near WPT systems. He frequently employs learning-based human models and investigates uncertainties in dosimetry, especially under pulsed or multi-frequency exposures. Scientific Awards and Recognition: Mentoring Polito Project (M2P), awarded by Politecnico di Torino on February 16, 2023 Advising and Grants: He supervises PhD student Junlong Liu in Electrical, Electronics, and Communications Engineering. He leads or participates in multiple funded research projects, including WPT-SAFE (2025–2027, national PRIN), SAFECHARGE (2024–2026, international), and various corporate consulting projects on electromagnetic exposure from welding devices, photovoltaic systems, and industrial cabins. He has served as Scientific Manager on over ten consulting and research contracts since 2015. Labs and Teams: He is a key member of the CADEMA Research Group (DENERG), focusing on computational electromagnetics and electromagnetic safety applications in energy, medicine, and the environment.

Tünde Fülöpمشاهده پروفایل

استاد

• Theoretical Plasma Physics
• Magnetic Fusion
• Laser-Produced Plasmas
• +۳ مورد دیگر

Tünde Fülöp serves as Professor of Physics at Chalmers University of Technology and holds the prestigious Wallenberg Scholar designation from the Knut and Alice Wallenberg Foundation. Her research bridges fundamental plasma phenomena with high-impact applications in energy, space, and materials science. Her primary research focuses on theoretical plasma physics, specifically magnetic fusion plasmas and laser-produced plasmas. She investigates systems with extreme density and temperature variations where multi-scale processes interact, requiring sophisticated modeling approaches from computationally intensive simulations to simplified dimensional models. Current work centers on two critical challenges: (1) controlling runaway electrons in 100-million-degree fusion reactors to prevent wall damage akin to welding flames, with immediate applicability to existing experiments and future power plants; and (2) optimizing laser-produced particle and light sources to generate efficient, stable ultra-short electron and light pulses for advanced particle acceleration applications. Scientific Awards: Wallenberg Scholar As leader of a dedicated research group, Fülöp directs efforts to develop predictive toolsets for electron acceleration and radiation control in laboratory plasmas. Her Wallenberg Scholar funding supports cross-disciplinary plasma research with significant implications for sustainable fusion energy development. The group's work integrates computational modeling with experimental validation to address fundamental challenges in plasma behavior across multiple scales.

• Mathematical Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jimmy Olsson is a Professor in the Department of Mathematical Statistics at KTH Royal Institute of Technology. His research focuses on generative models, statistical computational methods, and dynamic systems analysis, particularly in the context of artificial intelligence and machine learning. He specializes in state-space models, sequential Monte Carlo methods, and Markov chain Monte Carlo techniques. His work emphasizes foundational contributions to hidden Markov models, computational algorithms for statistical inference, and the intersection of mathematical statistics with modern data science challenges. Key research areas include developing efficient computational tools for analyzing complex, time-dependent data streams, with applications in AI and machine learning. Olsson’s publications span advanced topics such as diffusion models, particle filtering, and online variational learning, reflecting his expertise in bridging theoretical statistics with practical computational methods. He collaborates with researchers across disciplines, including engineering, computer science, and applied mathematics.