جست‌وجوی استاد و پژوهشگر

Annelie Hedströmمشاهده پروفایل

استاد

• Water and Sanitation Technology
• Sustainable Sewage Systems
• Stormwater Treatment
• +۵ مورد دیگر

Annelie Hedström is a Professor at Luleå University of Technology, working within the Department of Civil, Environmental and Natural Resources Engineering, specifically in the Architecture and Water division. Her research focuses on water and sanitation technology, with an emphasis on developing resource-efficient, robust and sustainable sewage systems. Professor Hedström's research interests span multiple areas of water systems engineering. She investigates sewage network condition assessment methods, sewage renewal coordinated with street renewal, make-up water, and innovative pipeline laying techniques. Her work on resource-efficient small sewage systems includes field studies examining treatment for micropollutants and microplastics, low-intensity natural purification systems, and source-separating sewage systems with nutrient recovery. She also researches purification processes for bathing, dishwashing and laundry water, water recycling, and advanced purification components for stormwater such as chemical precipitation, membrane filters and zeolite filters. Her recent publications demonstrate a strong focus on stormwater treatment technologies, wastewater management in cold climates, and infrastructure assessment methods. The research shows increasing emphasis on multi-criteria decision support systems, climate adaptation in Arctic areas, and advanced monitoring techniques for sewer networks. Professor Hedström leads and participates in numerous significant research projects, including ArcticSewlutions for wastewater management in sparsely populated cold climate areas, Smart Sewers for acoustic assessment of sewer lines, and VA-NET for vibration-based assessment of municipal pipeline networks. Her work combines theoretical modeling with extensive field studies, particularly focusing on the practical implementation challenges of sustainable water systems. She collaborates extensively with municipal authorities, industry partners, and international research teams to address real-world water infrastructure challenges.

• Powder Metallurgy
• Materials Science
• Additive Manufacturing
• +۳ مورد دیگر

Christopher Hulme is an Associate Professor of Powder Metallurgy and Rapid Solidification at the Department of Materials Science and Engineering, KTH Royal Institute of Technology. His research focuses on understanding the physical mechanisms of metal powder atomization, including droplet formation and solidification, and linking production conditions to powder properties. He employs computational fluid dynamics, stochastic modelling, and thermodynamic analysis, supported by experimental techniques like shadowgraphy and water modelling. His work also examines powder behavior, developing new testing methods for flow and spreading properties, and explores sustainability and equality in engineering education. Key research areas include atomization process optimization, powder flow dynamics, additive manufacturing feedstock development, and X-ray source innovation. His teaching responsibilities span courses such as Casting Processing, Material Selection, and Metal Powder Characterization, emphasizing practical methods and ethical engineering practices. He has contributed to over 50 peer-reviewed publications, with recent work addressing rotating anode X-ray erosion, nickel silicide alloy optimization, and humidity effects on steel powder flowability. Teaching Roles: Course responsible for Metal Powder Production, Materials Processes I, and Material Selection Research Themes: Atomization Science, Powder Behavior Modelling, Sustainable Manufacturing

• Physical Activity Measurement
• Energy Expenditure Analysis
• Behavioral Science in Health
• +۳ مورد دیگر

Professor Daniel Arvidsson holds a dual role as a researcher and teacher at the University of Gothenburg’s Department of Food and Nutrition and Sport Science, affiliated with Sahlgrenska Academy. His research focuses on physical activity measurement, energy expenditure analysis, and behavioral science related to diet and exercise. He leads interdisciplinary projects at the Center for Health and Performance and collaborates with the Center for Lifestyle Intervention, including the LI-PAD initiative for obesity management. His work spans epidemiological studies, clinical trials, and intervention research. He also teaches methodology in physical activity measurement and statistics. Key contributions include advancing accelerometer-based health metrics and exploring circadian rhythms’ impact on cardiovascular health. His research emphasizes translating data into actionable public health strategies.

Antoine Honoréمشاهده پروفایل

پژوهشگر

• Predictive Modeling
• Clinical Informatics
• Machine Learning
• +۴ مورد دیگر

Antoine Honoré is a Researcher (Postdoc) at KTH Royal Institute of Technology, working in the Division of Information Science and Engineering. He collaborates with Prof. Ming Xiao at KTH and Prof. Volker Lauschke at Karolinska Institutet's Department of Physiology and Pharmacology. His research focuses on developing predictive models for clinical contexts, emphasizing generalizability, calibration, and implementation of deep learning architectures. Key projects include graph-based models for genetic variant analysis in drug transporter proteins, advancing personalized medicine. Education: He holds a PhD from KTH Royal Institute of Technology under Assoc. Prof. Saikat Chatterjee and Prof. Eric Herlenius at Karolinska Institutet, with a thesis on deep learning for neonatal sepsis detection. His work bridges signal processing, machine learning, and clinical informatics. Research Interests: Predictive modeling in healthcare, machine learning applications in neonatal sepsis detection, pharmacogenomics, and integration of clinical data sources. He has pioneered LSTM and HMM-based models for vital sign analysis, contributing to early sepsis identification in preterm infants. Publications: Over 15 peer-reviewed articles, including work on DANSE algorithms (IEEE Transactions on Signal Processing), neonatal sepsis detection (Acta Paediatrica), and pharmacogenomic variant prediction (2025). His research spans signal processing, generative models, and clinical decision support systems. Labs/Teams: Active in Prof. Ming Xiao's research group at KTH and collaborates with Karolinska Institutet's Physiology and Pharmacology department. His work integrates AI with healthcare systems to improve clinical outcomes through data-driven approaches.

Felix Hoمشاهده پروفایل

مدرس

• Biochemistry
• Chemical Biology
• Chemistry Education
• +۳ مورد دیگر

Felix Ho is a Lecturer at Uppsala University's Department of Chemistry - Ångström , specializing in Molecular Biomimetics and Biophysical/Bioinorganic Chemistry. His work bridges Biochemistry and Chemistry Education through dual research streams: (1) Structural investigations of photosynthetic proteins like Photosystem II and [FeFe] hydrogenases, and (2) Pedagogical studies on student problem-solving in math-intensive chemistry contexts. Education: Holds a PhD and Docent (Swedish postdoctoral qualification) His research on Photosystem II includes computational modeling of water access channels and EPR spectroscopy studies of tyrosine-manganese interactions. In education, he develops frameworks for systems thinking in chemistry curricula and investigates interdisciplinary STEM reasoning , particularly graph interpretation in chemical kinetics. Recent publications (2023-2025) show a growing focus on digital learning and math-science integration . Collaborations span Royal Society of Chemistry projects and international research networks. Though no explicit awards are listed, his work appears in high-impact journals like ACS Catalysis and Journal of Biological Inorganic Chemistry .

• Public Economics
• Labor Economics
• Applied Microeconomics
• +۱ مورد دیگر

Tobias König serves as Head of the Department of Economics and Statistics and holds a tenured position as Senior Lecturer at the School of Business and Economics, Linnaeus University, where he has been instrumental in academic leadership since 2020. His scholarly profile is anchored in rigorous research spanning Public Economics, Labor Economics, Applied Microeconomics, and Behavioral Economics, with publications in top-tier journals including the European Economic Review and Economic Journal. König maintains active affiliations with the Berlin Social Science Center (WZB) as a Research Fellow and the CESifo Research Network, demonstrating significant recognition within the international economics community. His research program critically examines how social norms, image concerns, and fairness perceptions shape economic decisions in policy contexts. Key contributions include experimental investigations into welfare take-up behavior, tax compliance mechanisms, and the political economy of public goods provision. König's methodological approach uniquely blends laboratory and field experiments with behavioral insights, revealing how psychological factors like self-control and status concerns influence responses to policy interventions such as sin taxes and public provision schemes. His work consistently bridges theoretical microfoundations with practical policy applications, particularly in behavioral public finance. Analysis of his publication trajectory since 2011 shows a progressive deepening of behavioral approaches within public economics, with recent work (2023-2025) increasingly incorporating big data methodologies and experimental evidence to address equality of opportunity. Current research frontiers include sustainable consumption patterns through the Kalmar smart metering project and fairness dynamics in matching markets, reflecting his commitment to contemporary policy challenges at the intersection of individual behavior and collective welfare. His scientific contributions have been formally recognized through competitive fellowships: Research Fellow at Berlin Social Science Center (WZB) CESifo Research Network Fellow As academic leader, König directs the Linnaeus University Centre for Equality of Opportunity and Big Data Policy Analysis while spearheading the Kalmar Energi collaboration on sustainable electricity consumption. His teaching portfolio spans advanced microeconomic theory, public economics, and behavioral applications at both undergraduate and graduate levels, emphasizing methodological rigor and policy relevance. König actively participates in the Copenhagen Behavioural Economics Network, fostering cross-institutional collaborations that advance experimental economic methodology. He leads the Linnaeus University Centre for Equality of Opportunity and Big Data Policy Analysis, which pioneers data-intensive approaches to measuring opportunity structures. His Kalmar project with Kalmar Energi develops behavioral insights for energy conservation through real-time consumption data analysis, while his WZB affiliation facilitates cutting-edge experimental work on social preferences. These initiatives collectively position König at the forefront of applying behavioral microeconomics to pressing public policy questions.

• Probability
• Mathematical Physics
• Statistics

Mathias Müller is a researcher affiliated with the Royal Institute of Technology (KTH) , working in the Probability, Mathematical Physics and Statistics Unit . His research focuses on probability theory, mathematical physics, and statistical methods. He contributes to academic activities as an assistant in courses such as Computer Intensive Methods in Mathematical Statistics (SF2955) and Statistical Learning and Data Analysis (SF1930) . For contact, his email is matmul@kth.se .

• Stochastic Analysis
• Financial Mathematics
• Mathematical Biology
• +۳ مورد دیگر

Roger Pettersson is a Professor at the Department of Mathematics, Linnaeus University, with a focus on stochastic analysis, financial mathematics, and mathematical biology. He is affiliated with the Faculty of Technology and leads research in Stochastic Analysis and Stochastic Processes , Computational Mathematics for Predictive Digital Twins (PreDiTwin) , and Deterministic and Stochastic Modelling under the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA). His work bridges theoretical and applied mathematics, emphasizing numerical methods and epidemic models. Research Interests : Stochastic differential equations (SDEs/SPDEs), Lévy processes, financial modeling, biological systems, numerical approximations, and epidemic dynamics. Publications : Over 20 peer-reviewed articles (2002–2025) in journals like Stochastics , Applied Mathematics and Computation , and Chaos, Solitons & Fractals , focusing on stochastic control in epidemics, Lévy noise applications, and numerical methods for SDEs. Supervised Theses : Mentored over 20 bachelor’s and master’s students in projects spanning queueing systems, SPSS data analysis, neural networks for hydropower forecasting, and epidemic modeling. Collaborations : Active in interdisciplinary research, including forestry, wood, and building technologies, and contributes to the International Center for Mathematical Modeling (ICMM) .

• Physical activity and health
• Sports medicine
• Sports cardiology
• +۶ مورد دیگر

Mats Börjesson is a Professor at the University of Gothenburg's Sahlgrenska Academy, Department of Neuroscience and Physiology, and serves as Director of the Center for Health and Performance Development (CHP) at the University of Gothenburg and Östra Sjukhuset. With a medical degree from Gothenburg and a 1999 thesis on 'Chest Pain,' he combines clinical practice (Chief Physician at Sahlgrenska University Hospital) with academic research spanning physical activity, sports cardiology, and cardiovascular disease prevention. M.D. in Medicine, University of Gothenburg Ph.D. thesis on chest pain (1999) His research focuses on physical activity's role in primary and secondary cardiovascular prevention, contributing to national guidelines (FYSS) and major projects like SCAPIS. He leads the CHP center, integrating exercise physiology with public health initiatives. Current work includes machine learning for cardiac arrest prediction, metabolomic/proteomic biomarker studies, and cardiac screening for athletes. Key research areas include: Sudden cardiac death prevention Sports cardiology Physical activity monitoring Lifestyle intervention design Accelerometry data analysis Public health policy development Scientific contributions: Chairman of EurKard's sports cardiology section National cardiac screening recommendations Collaborations with FIFA on youth football safety Leadership in SCAPIS cohort studies Active in clinical practice and public health initiatives, Börjesson bridges academic research with real-world healthcare applications through his roles at Sahlgrenska University Hospital and University of Gothenburg.

• Software Engineering
• Software Quality Assurance
• Data-Intensive Methods
• +۳ مورد دیگر

Morgan Ericsson is a professor at Linnaeus University's Faculty of Technology, Department of Computer Science and Media Technology. He has coordinated the Linnaeus University Centre for Data Intensive Sciences and Applications and leads the research group Data Intensive Software Technologies and Applications (DISTA). His work spans software engineering, quality assessment, and machine learning applications in system design. Research coordinator, DISA Faculty member, Linnaeus University His research focuses on defining and measuring software quality through data-driven approaches, including code metrics, documentation evaluation, and machine learning. He develops methods to balance performance, safety, and functionality in complex systems. Recent publications involve generative adversarial networks for log end identification, copula-based metrics aggregation, and graph convolution networks for code-architecture mapping. Key themes include automated classification, density analysis, and educational software tools. Ericsson has led projects like "HPC for SME" and "In-line Visual Inspection Using Unsupervised Learning," while contributing to software infrastructure for quality assessment and mobile learning frameworks.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Dag Björnberg is a researcher at Linnaeus University, affiliated with the graduate school Data Intensive Applications (DIA) and the research groups Data Intensive Software Technologies and Applications (DISTA) and Linnaeus University Centre for Data Intensive Sciences and Applications. His research focuses on applying artificial intelligence and machine learning techniques to solve challenges in the forest industry. Key projects include developing algorithms for log tracking using end-grain fingerprints, generating photorealistic log end images via conditional Generative Adversarial Networks (cGANs), and improving forest inventory efficiency through AI-driven methodologies. Doctoral project: Advanced identification methods for the forest industry via computer vision and AI ForestMap: Global forest mapping methodology Tree volume measurement by AI: Sweden-Brazil collaboration His recent publications highlight the integration of GANs, image translation, and data-intensive approaches in forest resource management and AI development.

• Data Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Rakhshanda Jabeen is a researcher affiliated with Linnaeus University, actively contributing to data-intensive applications and software technologies. She is associated with the graduate school Data Intensive Applications (DIA) and the research groups Data Intensive Software Technologies and Applications (DISTA) and Linnaeus University Centre for Data Intensive Sciences and Applications (DISA). Focuses on applied research in data-driven methods Involved in industrial doctoral projects leveraging Big Data Her recent work explores Graph Convolution Networks for mapping source code entities to architectural modules, aligning with interdisciplinary applications in software engineering and data science. Keywords: Machine Learning, Big Data, Software Architecture Sub-fields: Graph Neural Networks, Data-Driven Software Development

Krzysztof Podgórskiمشاهده پروفایل

استاد

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۷ مورد دیگر

Krzysztof Podgórski is a Professor and Head of Department at the Department of Statistics, Lund University. He maintains an active research profile with ongoing projects and recent publications extending into 2025. His research focuses on three main areas: theory and applications of multivariate non-Gaussian stochastic models, statistical analysis of spatial random fields evolving in time with non-trivial dynamics, and statistical distributions at random crossing events. He has made significant contributions to ergodic theory of stochastic processes, bootstrap methods for regression analysis, theory of heavy-tailed distributions, and extreme value theory. Analysis of his recent publications reveals a strong emphasis on spline theory applications to functional data analysis alongside continued development of non-Gaussian distributions for engineering applications. His work consistently bridges theoretical statistical developments with practical implementations across diverse fields. Professor Podgórski supervises multiple PhD students and research projects, including a Swedish Research Council-funded project running through 2025. His research has practical applications in mechanical engineering, ocean engineering, spatial econometrics, actuarial sciences, and mathematical finance. He maintains an active presence in the academic community through conference organization, invited talks, and journal peer review activities, with recent engagements documented through late 2024.

Attila Frigyesiمشاهده پروفایل

دانشیار

• Clinical Aspects of Intensive Care
• Data Mining
• Big Data Analysis
• +۶ مورد دیگر

Attila Frigyesi is an Associate Professor at Lund University's Faculty of Medicine, Department of Anesthesiology and Intensive Care. He serves as a Project Manager, Consultant, Supervisor, and Principal Investigator specializing in Intensive Care Epidemiology. His research focuses on clinical aspects of intensive care, utilizing data mining, big data analysis, machine learning, and advanced statistical methods. Key research areas include biomarkers in sepsis, ARDS, and cardiac arrest. His work contributes to UN Sustainable Development Goals related to health and wellbeing. Analysis of his recent publications reveals a strong focus on biomarker research in critical care settings, particularly examining renal function, sepsis diagnostics, and COVID-19 complications. His work frequently employs observational study designs and cohort analyses, with a growing emphasis on machine learning applications in intensive care prognostication. Dr. Frigyesi actively supervises PhD students, with current supervision of five intensivists in their doctoral studies. His research is supported by grants including the Crafoord Foundation project 'Precision critical care for sepsis and ARDS using proteomics' where he serves as Principal Investigator. He leads multiple active research projects including 'Biomarkers for Acute Kidney Injury in Critical Care' and 'Machine learning in health care with a focus on breast cancer staging and intensive care prognostication.' His collaborative network spans multiple institutions with recent external collaborations across various countries.

Inger Perssonمشاهده پروفایل

مدرس ارشد

• Machine Learning Applications in Healthcare
• Medical Statistics
• Clinical Research
• +۳ مورد دیگر

Inger Persson is a Senior Lecturer at Uppsala University's Department of Statistics, where she has served as Associate Professor since 2012 and Senior Lecturer since 2022. With prior pharmaceutical research experience at AstraZeneca, she co-founded AlgoDx AB - a research company developing healthcare AI solutions. Her work focuses on applying statistical methods and machine learning to critical care medicine and nutritional management. Senior Lecturer in Statistics (2022–present) Associate Professor (2012–2022) Co-founder, AlgoDx AB (2016–2023) Excellent Teacher award (2018) Her research bridges statistical innovation with clinical applications, particularly in sepsis detection, acute kidney injury prediction, and nutritional care optimization. The NAVOY® Sepsis algorithm developed by her team demonstrates how real-time data processing can significantly improve patient outcomes in intensive care units. The Hjärnäpplet Innovation Award (2023) recognizes her work that combines statistical rigor with practical healthcare impact. Her publications demonstrate expertise in algorithm development, statistical methodology, and clinical implementation across multiple domains including oncology, geriatrics, and tropical agriculture.