جست‌وجوی استاد و پژوهشگر

Harri Oinas-Kukkonenمشاهده پروفایل

استاد

• Persuasive Design
• Behavior Change
• Digital Interventions
• +۷ مورد دیگر

Harri Oinas-Kukkonen is a Professor of Information Systems Science and Dean of the Graduate School at the University of Oulu, Finland. He is affiliated with the Faculty of Information Technology and Electrical Engineering and leads the Oulu Advanced Research on Service and Information Systems group. His research focuses on persuasive design, behavior change interventions, and humanized ICT systems, with applications in healthcare, sustainability, and workplace well-being. He co-authored Humanizing the Web: Change and Social Innovation and has published extensively in journals spanning computer science, healthcare informatics, and behavioral science. Research Interests: Persuasive Systems Design (PSD) Digital Behavior Change Support Systems (BCSS) Health Informatics and mHealth Sustainable Technology Adoption Workplace Health Promotion Ethical AI in Persuasive Technologies Recent Work Trends: His articles (2024-2025) highlight emerging technologies in behavior change, including LLM-based mental health tools, CHD management interventions, and compliance mechanisms in digital health systems. He emphasizes integrating psychological theories like Self-Determination Theory into system design while addressing ethical challenges in data privacy (GDPR) and technology overreliance. Professional Contributions: Serves on conference organizing committees (e.g., Persuasive Technology Conferences) and has advised over 20 doctoral students through workshops and thematic analyses. Active in interdisciplinary collaboration involving healthcare providers, tech developers, and policymakers to advance human-centered health innovations. Labs/Tech: Leads development of sensor-based care solutions for aging populations and eco-friendly ICT systems, with prototypes validated through formative evaluations and RCTs.

• Internet of Things
• Machine Learning
• Data Security
• +۳ مورد دیگر

Avleen Malhi is a Visiting Professor in the Department of Industrial Engineering and Management , affiliated with the Främling Kary group . Holding a Doctoral degree from Thapar University (2016), their research focuses on interdisciplinary applications of machine learning, IoT security, and explainable AI. Key areas include smart building energy optimization, healthcare informatics, and cybersecurity for IoT ecosystems. Education: Doctoral degree, Other, Thapar University (Award Date: 9 Nov 2016) Research Interests: Development of ensemble learning frameworks for medical genomics and neurological disorders AI-driven solutions for energy-efficient smart infrastructure Explainability in black-box predictive models for healthcare diagnostics Security architectures for IoT lifecycle management Recent Publications: Over 37 outputs since 2019, with a focus on machine learning applications in healthcare, IoT security, and smart systems. Notable contributions include a Python library for image classification explainability and comprehensive IoT security frameworks. Labs/Teams: Active member of the Främling Kary group , collaborating internationally on AI and multi-agent systems. Supervised 2 theses, though specific student names are not listed.

Xiaozhi Gaoمشاهده پروفایل

استاد

• Machine Learning
• Optimization Algorithms
• Computational Intelligence
• +۳ مورد دیگر

Xiaozhi Gao is a Professor at the School of Computing, University of Eastern Finland (UEF), within the Faculty of Science, Forestry and Technology. His research focuses on computational intelligence, optimization algorithms, neural networks, and their applications in domains such as energy forecasting, cybersecurity, and healthcare. He has authored and edited numerous international journal articles and conference proceedings. Research Interests: Dr. Gao's work spans machine learning, evolutionary algorithms, deep learning architectures, and their implementation in real-world problems like trajectory prediction, resource allocation, and medical diagnostics. His recent studies emphasize lightweight neural networks for railway safety and multi-objective optimization in dynamic environments. Publications: His 2025 articles include advancements in audio-visual deep networks for climate forecasting, constraint-based optimization models, and edge computing security frameworks. He has also co-edited conference volumes on advanced informatics and computing research. Awards & Grants: No specific awards or grants mentioned in the provided text. Labs/Teams: Active in interdisciplinary collaborations on AI-driven solutions for energy systems, robotics, and smart infrastructure. His research contributes to UEF's strategic focus on digital innovation and sustainability.

Antti Vehkaojaمشاهده پروفایل

استاد

• Biomedical Engineering
• Wearable Sensors
• Photoplethysmography
• +۵ مورد دیگر

Antti Vehkaoja is a Professor in Biomedical Technology at Tampere University. He holds a Doctor of Science (Technology) in Automation Engineering (2015) and a Master of Science in Electrical Engineering (2004). His research focuses on physiological monitoring methods, wearable sensor systems, and biomedical signal processing. He has led projects on photoplethysmography, differential mobility spectrometry, and gait analysis. Key contributions include novel methodologies for early detection of medical conditions like abdominal aortic aneurysm and Parkinson’s disease. He served as a Docent in Measurement Methods for Physiological Monitoring at TUT-BMT (2017–2022). His work spans 161 research outputs across journals/conferences, emphasizing interdisciplinary applications such as animal behavior analysis via biologgers and real-time surgical tissue classification. He received the Northern Europe NI Engineering Impact Award (2016) for contributions to biomedical engineering. Vehkaoja actively reviews for top journals like IEEE Transactions on Biomedical Engineering and collaborates internationally. His datasets, such as the Bio-logger Ethogram Benchmark and Movement Sensor Dataset for Dog Behavior Classification, are widely used in computational biology and veterinary medicine. His research aligns with UN SDGs related to health and innovation. Current projects explore thermal simulation in vascular medicine, non-invasive blood pressure monitoring, and digital healthcare platforms for chronic disease management.

• Computer Science
• Quantum Computing
• Medical Imaging
• +۳ مورد دیگر

Lauri Kettunen is a Professor and Vice Dean at the Faculty of Information Technology, University of Jyväskylä. His research spans quantum computing, medical imaging AI, and IT education innovations. He leads the Quantum Information and Computation Team, exploring quantum computational boundaries and practical applications. Recent work focuses on deep learning frameworks for medical image analysis, predictive learning analytics in computer science education, and quantum cellular automata gate design. Research interests include: Quantum Computing: Developing scalable quantum logic gates and QCA systems Medical AI: Annotation-efficient deep learning for medical imaging challenges Education Technology: Addressing first-year IT student challenges and predictive learning analytics Industry Collaboration: Frameworks for successful academia-industry partnerships in engineering Publications reflect interdisciplinary focus with 2024 highlights in European Journal of Engineering Education (education analytics), International Journal of Computer Assisted Radiology and Surgery (medical imaging), and IEEE Transactions on Instrumentation and Measurement (sleep analysis). Active in organizing events like CM3 Transport 2023 conference.

• Neurological and Sensory Diseases
• Oncology
• Surgical Sciences
• +۶ مورد دیگر

Niku Oksala is a Professor at the Centre for Vascular Surgery and Interventional Radiology , specializing in Neurological and Sensory Diseases , Oncology , and Surgical Sciences . His research focuses on Brain Ischemia , Atherosclerotic Plaque , Surgical Smoke , Electronic Nose Technology , and Ischemic Stroke . Key Collaborations : Active in multidisciplinary research with institutions in Finland and international partners. SDG Contributions : Work aligns with UN Sustainable Development Goals for health and well-being (SDG 3) through innovative medical diagnostics and vascular health research. His recent publications highlight advancements in non-invasive vascular diagnostics (e.g., photoplethysmography for abdominal aortic aneurysm detection), AI-driven emergency department forecasting , and biomarker discovery in surgical smoke . Methodologies span machine learning (LightGBM), medical imaging (MRI), and electrocardiogram-based monitoring . Supervision & Mentorship : Nikus has supervised 61 licentiate candidates , 31 doctoral candidates , and 5 master's students throughout his career, with ongoing mentorship activities as of 2025. Labs & Networks : Collaborates with the Puolustusvoimat (Finnish Defence Forces), Puolustusministeriö/MATINE (Ministry of Defence), and Instrumentariumin Tiedesäätiö (Instrumentarium Science Foundation) on medical technology and clinical decision-making projects.

Ella Peltonenمشاهده پروفایل

استادیار

• Ubiquitous Computing
• Large-scale Data Analysis
• Applied Machine Learning
• +۵ مورد دیگر

Ella Peltonen is an Assistant Professor at the M3S research unit, University of Oulu, Finland. She joined the Ubicomp Oulu research centre and 6Genesis research programme in November 2018. Prior to this position, she was a postdoctoral researcher at the Insight Centre for Data Analytics in Cork, Ireland. She completed her PhD in the Nodes group at the University of Helsinki, Finland, working on the Carat project of collaborative energy diagnostics for mobile devices. Her educational background includes: PhD in Computer Science, University of Helsinki, Finland (Carat project on collaborative energy diagnostics) Ella Peltonen's research focuses on ubiquitous computing, large-scale data analysis, and applied machine learning. Her work particularly emphasizes everyday sensing and mobile and wearable devices. She aims to apply machine learning algorithms to large, complex data in real-time systems, with a focus on distributed machine learning and data analysis of smart devices. Her research spans various applications including energy consumption monitoring of mobile devices, wearable technology for measuring physiological signals, and exploring future sensing technologies. Peltonen has expressed interest in how future devices might sense human states, become smarter, and provide greater benefits, potentially through innovations like augmented reality glasses or subcutaneous chips. Analysis of her recent publications shows a strong focus on edge computing, vehicular networks, and sustainable computing systems. Her work bridges the gap between theoretical machine learning approaches and practical applications in transportation, healthcare, and environmental monitoring. Many of her papers address challenges in distributed systems, real-time data processing, and privacy-preserving techniques for edge intelligence. Her notable scientific awards include: Nominated to the list of 10 Rising Stars in Networking and Communications by N2 Women 2017 Selected as one of 50 Finnish Researchers by the Finnish Union of University Researchers and Teachers Nokia Scholarship 2015 and 2016 Jorma Ollila Grant 2018 Young Teacher of the Year 2012 Young Researcher of the Year 2015 Peltonen is actively involved in teaching and mentoring, with a teaching philosophy focused on supporting students' independent learning rather than lecturing from above. She enjoys guiding small groups where she can discuss topics together with students and get to know them personally. As a researcher, she describes herself as precise, detail-oriented, and committed to verifying the correctness of her work carefully. She values the combination of mathematical work with experimental work and creativity in technology, noting that research tasks are diverse and can apply different types of methodology. She is part of international research collaborations with several major universities worldwide, as required by Finnish Academy funding. Peltonen is also an advocate for diversity in technology fields, noting that technology is used by all kinds of people from various backgrounds, yet the producers of technology lack diversity. She has highlighted the importance of encouraging more women to pursue technology careers from an early age.

Julia Kemppinenمشاهده پروفایل

استادیار

• Ecology
• Climate Change Biology
• Arctic Ecosystems
• +۵ مورد دیگر

Dr. Julia Kemppinen serves as Assistant Professor in the Botany and Mycology Unit at the University of Helsinki, where she leads cutting-edge research on Arctic ecosystem responses to climate change. Her work bridges geodiversity and biodiversity science through innovative microclimate studies in polar regions. Her research focuses on: Microclimate drivers of Arctic plant community assembly Marine-derived nutrient effects on High Arctic vegetation Geodiversity-biodiversity relationships under climate change Development of standardized microclimate research methodologies Intraspecific trait variation in ecological modeling Dr. Kemppinen's recent publications reveal strong growth in output and impact, with significant contributions to Functional Ecology and Methods in Ecology and Evolution. Her work demonstrates increasing leadership in developing field protocols and collaborative frameworks for polar research. Academic service highlights: Editor for npj Biodiversity special issues Organizing committee for Microclimate Workshop (2024) Public outreach at Kilpisjärvi Biological Station Active participation in doctoral education as committee member and opponent She currently supervises multiple doctoral candidates while leading two Academy of Finland projects that provide substantial resources for field research and student training in Arctic ecology. Her research group operates primarily through the University of Helsinki's field stations in Lapland, utilizing advanced sensor networks and collaborative frameworks across Scandinavian and Arctic research networks.