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Robin Augustine is an Associate Professor in Medical Engineering at Uppsala University, Sweden, affiliated with the Department of Electrical Engineering within the Faculty of Science and Technology. He serves as the Head of the Microwaves at the Medical Engineering (MMG) group at the FTE Division and holds the position of Senior Lecturer/Associate Professor in Solid-State Electronics. His work bridges biomedical engineering and microwave technology, focusing on developing innovative medical devices and diagnostic systems. Dr. Augustine holds a Ph.D. in Electronics and Optic Systems from Université Paris-Est Marne-la-Vallée, France, a Master's in Electronics with Robotics Specialization from Cochin University of Science and Technology, India, and a Bachelor's in Electronics Science from Mahatma Gandhi University, India. His doctoral thesis focused on electromagnetic modeling of human tissues and antenna-human body interaction in the Body Area Network (BAN) context. Prior to his current position, he served as a Postdoctoral Researcher at the University of Rennes 1, France, and has been a Senior Researcher at Uppsala University since 2011. Dr. Augustine's research spans multiple cutting-edge areas in biomedical engineering, with particular emphasis on medical microwave sensors , intra-body communication systems , and non-invasive diagnostic technologies . He pioneered the concept of human body-centric communication known as Fat-IBC (Fat-Intra Body Communication), which utilizes fat tissue as a communication channel for medical devices. His work in burn injury diagnosis using microwave technology has led to significant clinical applications, while his research in wireless power transfer systems for implantable biomedical devices addresses critical challenges in medical device longevity. Additional research interests include electromagnetic modeling of human tissues, anthropomorphic phantoms for medical testing, and AI-integrated diagnostic systems for conditions like ARDS and diabetes. Analysis of Dr. Augustine's recent publications reveals a strong focus on advancing intra-body communication through fat tissue , with multiple papers exploring Fat-IBC system design, performance optimization, and security. His research group is actively developing non-invasive diagnostic systems for various medical conditions including burn injuries, diabetes, and respiratory distress. The integration of machine learning with microwave sensing technologies represents another significant trend in his recent work, enabling more accurate medical diagnostics through advanced signal processing. Dr. Augustine serves as Principal Investigator (PI) for several major research projects including VR projects Osteodiagnosis (2017) and MuSkeldiagnosis (2024), as well as Vinnova projects BDAS, Fat-IBPT, and AI-DARDS. He is also PI of the H2020 FET OPEN project B-CRATOS, and serves as Work Package Leader (WPL) for H2020 RIA SINTEC (2019) and PERSIMMON (2024). As Co-PI, he contributes to SSF Framework Grants LifeSec, Zero-IoT, and BOS, along with the VR Back Scatter project (2023). Additionally, he leads R&D efforts for multiple Eurostars projects including SenseBurn, MAS, COMFORT, and DIAMPS. Dr. Augustine founded, chairs, and serves as CTO of Probingon AB, a Swedish Medtech company. He leads the Microwaves at the Medical Engineering Group (MMG), which comprises 4 PhD students, 6 senior researchers and a project manager. His research group collaborates extensively with clinical partners to translate engineering innovations into practical medical applications, with particular focus on developing technologies that improve diagnostic accuracy and patient outcomes while reducing healthcare costs.

• Biomedical Engineering
• Microwave Technology
• Non-invasive Physiological Sensing
• +7 more

Bertil Lindahl is a Professor in the Department of Medical Sciences, specializing in Cardiology at Uppsala University, with additional affiliation at the UCR-Uppsala Clinical Research Center. His research focuses on myocardial infarction, cardiac biomarkers, non-obstructive coronary artery disease, and cardiovascular outcomes research. He maintains active clinical and research responsibilities with contact information listed for both institutional locations. His research interests span several critical areas of modern cardiology including the diagnosis and classification of myocardial infarction, cardiac biomarker applications (particularly troponin), non-obstructive coronary artery disease (MINOCA), secondary prevention strategies, and cardiovascular outcomes research. His work heavily utilizes the SWEDEHEART registry, a Swedish national quality registry for coronary artery disease that enables large-scale epidemiological and clinical studies. Analysis of his recent publications reveals a strong focus on refining diagnostic criteria for myocardial infarction, understanding prognostic implications of various cardiac conditions, evaluating treatment effectiveness in real-world settings, and investigating the relationship between biomarkers and clinical outcomes. His research has significant implications for clinical practice guidelines and patient management strategies in cardiology. Lindahl has received recognition through numerous publications in high-impact journals including the New England Journal of Medicine, European Heart Journal, Journal of the American College of Cardiology, and Circulation. His work demonstrates strong national and international collaborations with researchers across multiple disciplines within cardiovascular medicine. His research portfolio includes clinical trials, registry-based studies, diagnostic evaluations, and methodological research. Current projects appear to focus on refining myocardial infarction classification systems, evaluating secondary prevention strategies, investigating biomarker applications, and studying special populations such as women with cardiovascular disease and patients with non-obstructive coronary artery disease.

• Cardiology
• Myocardial Infarction
• Cardiac Biomarkers
• +5 more

Johan Westerbergh serves as a Statistician at the Uppsala Clinical Research Center (UCR), part of Uppsala University. His work focuses on biostatistical analysis within large-scale clinical studies, particularly cardiovascular and epidemiological research. His research spans cardiovascular epidemiology , clinical trial methodology , and medical statistics , with emphasis on atrial fibrillation, myocardial infarction, and anticoagulation therapy outcomes. Recent publications demonstrate expertise in registry-based analyses (SWEDEHEART), biomarker validation, and risk stratification models across cardiology, nephrology, and oncology domains. Analysis of his publication trends reveals consistent contributions to high-impact cardiology journals, with increasing focus on machine learning applications in medical imaging (2023) and sex-specific outcome analyses in anticoagulation therapy. His collaborative work frequently involves multinational clinical trials including ARISTOTLE and PLATO. Professional collaborations span Uppsala University's medical faculty, SWEDEHEART registry investigators, and international research consortia. Current projects include pediatric acute kidney injury meta-analyses and deep learning applications in coronary plaque characterization.

• Biostatistics
• Cardiovascular Research
• Clinical Epidemiology
• +2 more

Professor Karl Andersson serves as Dean for the Faculty of Science and Technology and Head of Subject for Cyber Security at Luleå University of Technology in Skellefteå, Sweden. He holds positions in both the Department of Computer Science and the Department of Systems and Space Engineering, with research focus primarily in cybersecurity and next-generation network technologies. Dr. Andersson earned his Master's degree in Computer Science and Technology from the Royal Institute of Technology in Stockholm before working in industry with Capgemini Group as a consultant, project manager, business developer, and branch manager. He returned to academia to complete his PhD with a dissertation titled "On Access Network Selection Models and Mobility Support in Heterogeneous Wireless Networks." His postdoctoral work included research appointments at Columbia University in New York and as a JSPS Fellow at the National Institute of Information and Communications Technology in Tokyo. His research interests span cybersecurity, 5G/6G networks, Internet of Things, wireless network mobility, and explainable artificial intelligence. From 2017-2023, he led the Center for Distance-spanning Technology at LTU, focusing on fifth and sixth generation mobile networks, IoT, and data centers. His recent publications demonstrate a growing interest in applying machine learning to cybersecurity challenges and medical diagnostics. Professor Andersson's publication record shows a strategic evolution from wireless networking fundamentals to applied cybersecurity and machine learning solutions. His recent 2025 publications indicate active research in explainable AI frameworks, malicious URL detection, and medical applications of machine learning, suggesting an interdisciplinary approach that bridges cybersecurity with healthcare applications. Senior Member of IEEE Senior Member of ACM General Chair for IEEE Conference on Local Computer Networks (LCN) 2020-2021 Reviewer for numerous prestigious journals including IEEE Transactions on Mobile Computing and IEEE Transactions on Computers Member of editorial boards for Journal of Internet Services and Information Security and Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications As Dean for the Faculty of Science and Technology since January 2022, Professor Andersson oversees significant research funding and academic programs. His service as a reviewer for major conferences (ICC, Globecom, VTC, WCNC) and journals demonstrates his standing in the academic community. The Center for Distance-spanning Technology that he led from 2017-2023 likely secured substantial research grants in 5G/6G and IoT domains. Based in Campus Skellefteå at Forskargatan 1, Professor Andersson's work through the Department of Computer Science and Department of Systems and Space Engineering connects theoretical research with practical applications in next-generation networking and security. His leadership of the Center for Distance-spanning Technology positioned LTU as a significant player in 5G/6G research, while his recent publications suggest expanding collaborations with medical researchers applying AI to healthcare challenges.

• Cybersecurity
• 5G/6G Networks
• Internet of Things
• +3 more

Björn Nordlund is an Adjunct Senior Lecturer and Docent at Karolinska Institutet, affiliated with the Department of Women's and Children's Health, Division of Clinical Paediatrics. He serves as the Research Group Leader for the Prevention of asthma and allergy (PANDA) research group, focusing on early-life and digital intervention strategies for asthma and allergy prevention. His primary research interests encompass pediatric allergology, asthma prevention strategies, digital health applications for respiratory diseases, and early intervention approaches. Nordlund has developed several innovative projects including PreventADALL (a Nordic birth cohort study investigating early food introduction and skin care to prevent allergies), ADVERT (using home spirometry for asthma diagnosis), AI-assisted asthma prevention systems, and the APPETITE study (an app-based medical device for inhalation technique training). His recent publications demonstrate expertise in AI applications for asthma prediction, early intervention strategies for allergy prevention, and digital health tools for respiratory disease management. His research often involves large-scale multicenter studies across Scandinavia with significant clinical implications for childhood respiratory health. Grants from the Asthma and Allergy Association's Research Fund Project funding from ALF Medicine and HMT, Region Stockholm Swedish Heart-Lung Foundation support Vinnova – Medtech4Health funding Collaboration with The Jerring Foundation Nordlund actively supervises graduate students at master's and PhD levels while teaching clinical immunology and digital healthcare of respiratory diseases. His work bridges clinical practice with technological innovation, particularly through his founding of the Home Spirometry System 'AsthmaTuner,' which was ranked as the best digital technology for asthma in Europe in 2023 by the Federation of Allergy and Airway diseases patient organisations.

• Pediatrics
• Pediatric Allergology
• Asthma Research
• +5 more

György Kovács is a Senior Lecturer at Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, working within the Embedded Intelligent Systems LAB. His primary research focus is on Machine Learning applications, particularly in speech and language technology. His research interests span multiple areas of speech and language processing, including: Automatic Speech Recognition across diverse conditions and languages Paralinguistic Speech Processing for emotion detection and speaker state analysis Audio event classification, including medical applications like cough sound analysis Sentiment analysis in written text, with particular interest in hateful language detection Bot detection in social media analysis Kovács has supervised numerous Master's theses since 2021, with topics ranging from accent classification and sentiment analysis to AI-generated code quality assessment. Many of these supervisions have led to co-authored publications with students. His recent publications demonstrate a strong focus on applying machine learning to diverse domains including remote sensing, healthcare technology, and natural language processing. The research shows a clear pattern of interdisciplinary work connecting machine learning with practical applications across environmental monitoring, healthcare, and social media analysis. His academic contributions include co-supervising PhD students such as Sana Al-Azzawi and Nosheen Abid, whose dissertation work on Unsupervised Curriculum Learning for Earth Observation represents significant contributions to the field. Notable publications include work on cloud detection using synthetic datasets, seagrass classification, and emotion classification using EEG in healthcare settings. Kovács also teaches courses including Introduction to Artificial Intelligence (D0032E) and Machine Learning and Pattern Recognition (D0033E), demonstrating his commitment to both research and education in the field of artificial intelligence.

• Speech and Language Technology
• Automatic Speech Recognition
• Natural Language Processing
• +5 more

Richard Sawatzky is an Affiliated Professor at the University of Gothenburg's Department of Nursing Sciences and Health within the Faculty of Medicine. His academic work bridges nursing science, health services research, and clinical practice with a strong emphasis on patient perspectives. His research centers on patient-reported outcomes (PROs), quality of life measurement, and person-centered care methodologies across multiple clinical contexts. Sawatzky has developed innovative approaches to analyzing PRO data, particularly in response shift phenomena and symptom pattern identification using latent class analysis. His work spans cancer care, palliative settings, dialysis treatment, and mental health in chronic illness. Analysis of his recent publications (2023-2025) reveals a strong focus on methodological advancements in PRO research alongside practical applications in clinical settings. He has contributed significantly to understanding how to implement PROs effectively in diverse healthcare contexts while maintaining patient-centered perspectives. His work frequently addresses vulnerable populations including older adults with cancer, dialysis patients with mental health concerns, and migrants navigating healthcare systems. Sawatzky maintains extensive international collaborations, particularly between Swedish and Canadian institutions, reflecting the global relevance of his methodological contributions to patient-centered outcomes research.

• Patient-Reported Outcomes
• Quality of Life Research
• Person-Centered Care
• +7 more

Dr. Haodong Qi is an Associate Professor and Project Researcher at Malmö University, Faculty of Culture and Society, Department of Global Political Studies. He is an economic demographer with specialized expertise in statistics, machine learning, and data science applications for social research. Dr. Qi obtained his Ph.D. from Lund University in 2016 and is actively affiliated with the Malmö Institute for Migration Studies (MIM), contributing to this international research center's mission of developing knowledge on international migration and ethnic diversity. His academic profile bridges traditional demographic analysis with cutting-edge computational methods. His primary research focus centers on climate-induced migration through leadership of the CLIMB (Climate-Induced Migration in Africa and Beyond) project, which leverages big data and machine learning to predict human mobility patterns. Recent publications demonstrate his innovative interdisciplinary approach spanning migration studies, public health, and data analytics, including work on using internet search data to predict migration intentions and analyzing refugee integration in Sweden. Dr. Qi's research has secured significant international funding from multiple national research councils across Europe and North America through the Belmont Forum partnership, reflecting the global relevance of his work on climate-migration linkages. Climate-Induced Migration Prediction Big Data Analytics for Social Research Digital Footprints in Migration Studies Economic Demography Applications Refugee Integration Processes Machine Learning for Social Phenomena His methodological innovation represents a frontier in migration studies, combining traditional demographic approaches with advanced computational techniques to address complex questions at the intersection of environmental change, human mobility, and social integration. The CLIMB project specifically exemplifies this approach by developing predictive models for one of the most pressing global challenges of our time.

• Climate-Induced Migration
• Big Data Analytics
• Machine Learning Applications
• +3 more