جست‌وجوی استاد و پژوهشگر

Mariusz Topolskiمشاهده پروفایل

استاد

• machine learning
• pattern classification
• feature extraction and selection
• +۱۲ مورد دیگر

Mariusz Topolski is a Professor at the Department of Computer Systems and Networks, Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology. He received his habilitated doctor degree in Computer Science in 2024 and has been actively contributing to research and teaching in machine learning, data analysis, and optimization. He is a member of multiple research teams including the Machine Learning Team (ZUM), Advanced Data Analysis Methods Team, and Metaheuristics Team. Education: Habilitated Doctor in Computer Science, Wrocław University of Science and Technology, 2024 PhD in Technical Sciences (Computer Science), Wrocław University of Science and Technology, 2008 Master's and Engineering Degree in Technical Physics (Biomedical Engineering), Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 2003 His research interests include machine learning, pattern classification, feature extraction, explainable AI, multi-objective optimization, and data mining. His work applies advanced computational techniques to real-world problems in medicine, construction, pharmaceuticals, and logistics. He has developed novel methods in PCA modification, feature selection, and signal analysis, particularly for diagnostic applications in neurology and oncology. The recent publications reflect a strong trend in applying machine learning and optimization to medical diagnostics and industrial risk modeling. Key areas include PCA-based classification, multi-objective feature selection, and hybrid signal processing techniques. His interdisciplinary work bridges computer science with healthcare and civil engineering, demonstrating the versatility of AI in solving complex, multi-domain challenges. Scientific Awards: Degree of Habilitated Doctor in Computer Science (awarded February 28, 2024) Mariusz Topolski has led and participated in numerous national and international research projects, including collaborations with Jagiellonian University Collegium Medicum, Medical University of Warsaw, Gdańsk Medical University, and foreign institutions such as the National Mining University of Ukraine and Fundación ICIL in Spain. He supervises diploma theses and contributes to academic training through workshops and lectures. He is also engaged in industrial applications, developing optimization and machine learning methods for supply chain logistics. Research Teams: Machine Learning Team (ZUM) Advanced Data Analysis Methods Team Metaheuristics Team Computer Networks Team Teaching Team

• Databases
• Big Data
• Data Storage
• +۱۲ مورد دیگر

Alberto Abello Gamazo is a full professor at the Facultat d'Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Services and Information Systems Engineering. He coordinates the Erasmus Mundus Doctoral Program in Information Technologies for Business Intelligence and leads research within the inSSIDE and inLab FIB groups. His work bridges computer science and healthcare, focusing on data-intensive systems and their real-world applications. Research Interests: His expertise spans databases, big data, NoSQL, OLAP, data storage, and data science. He investigates automated data pipelines, federated data management, feature selection, and AI-driven analytics. His work increasingly integrates healthcare applications, particularly in automated diagnosis using AI and digital microscopy for diseases such as malaria and schistosomiasis. Publication Trends: His recent articles (2023–2025) emphasize automated data science pipelines (e.g., Hyppo), fairness and interpretability in machine learning, efficient data management in document stores, and the application of AI to medical diagnostics. A strong trend is the development of low-cost, open-source robotic microscopy systems for global health challenges. Scientific Awards: Premiada (recognized activity) XXV Congreso Nacional de la Sociedad Española de Enfermedades Infecciosas y Microbiologia Clínica Advising and Grants: He has advised numerous PhD students and leads multiple competitive R&D+i projects funded by national and European programs (e.g., HORIZON 2020, Plan Nacional). His projects include 'Graph-driven federated data management,' 'EXPeriment driven and user eXPerience oriented analytics,' and health-focused initiatives like 'For an improvement of Health through rapid and economic diagnosis (iH-red).' He collaborates extensively with institutions like the Hospital de la Vall d'Hebron and Fundació PROBITAS. Labs and Teams: He is a core member of the inSSIDE (integrated Software, Services, Information and Data Engineering) and inLab FIB research groups, both affiliated with CIT-UPC. These groups focus on cutting-edge data engineering, information systems, and their societal applications.

• Microwave Imaging
• Electromagnetic Scattering
• Inverse Problems
• +۵ مورد دیگر

Ann Franchois is a Full Professor at Ghent University in the Faculty of Engineering and Architecture, specifically within the Department of Information Technology (EA05). She is affiliated with the Internet Technology and Data Science Lab, where she leads research in electromagnetic imaging and inverse problems. Her academic career spans several decades with continuous contributions to the field of microwave and millimeter wave imaging. Professor Franchois specializes in microwave imaging , electromagnetic scattering , and inverse problems , with significant applications in biomedical imaging (particularly breast cancer detection) and non-destructive testing (for concrete structures and steel fiber analysis). Her research integrates advanced mathematical techniques including regularization methods, numerical optimization, and computational electromagnetics to solve complex imaging problems. She has developed innovative approaches such as value picking regularization, piecewise smoothing techniques, and Huber regularization for improving reconstruction quality in quantitative imaging. Her recent publications (2013-2023) demonstrate a consistent focus on advancing 3D and 2.5D electromagnetic modeling techniques, with particular emphasis on medical applications and non-destructive testing. The research shows an evolution from fundamental electromagnetic theory to practical applications, especially in breast cancer detection through microwave tomography. Her work often involves collaborations with experts in image processing and signal reconstruction. Professor Franchois has supervised several PhD students to completion, including Funing Bai (2010-2014), Sara Van den Bulcke (2010), and Jürgen De Zaeytijd (2009). She has secured research funding from prestigious organizations, notably the Research Foundation - Flanders (FWO) for projects such as "Scattering-type scanning near-field mm-wave microscope" (2008-2013). Her laboratory work centers around the Internet Technology and Data Science Lab at Ghent University, where she develops and applies advanced computational methods for electromagnetic imaging. Current research directions include improving the accuracy and efficiency of microwave imaging systems, particularly for medical diagnostics and structural assessment applications.

Les Norfordمشاهده پروفایل

استاد

• Building Energy Efficiency
• Urban Climate
• HVAC Systems
• +۶ مورد دیگر

Les Norford is a Professor at MIT’s Department of Architecture, specializing in sustainable building design, energy efficiency, and urban climate studies. He holds academic appointments since 1988 and served as Associate Head of the Department of Architecture from 2006. His research integrates building system diagnostics, HVAC optimization, and urban heat island mitigation, with notable contributions to non-intrusive load monitoring (NILM) and thermal comfort modeling. Education: BS in Engineering Science (Cornell University, 1973); PhD in Mechanical & Aerospace Engineering (Princeton University, 1984) Professional Experience: Former nuclear engineer (US Navy/DOE), lecturer at Princeton, consultant with Tabors Caramanis & Associates Labs/Initiatives: Director of Singapore-MIT Alliance for Research and Technology (SMART)’s Center for Environmental Sensing and Modeling (CENSAM) Research interests include energy-efficient building operations, climate-responsive architecture, and smart grid technologies. His work addresses challenges in tropical urban environments, cooling system innovation, and decarbonized energy infrastructure. Collaborations with institutions like Lawrence Berkeley National Lab and Montana State University highlight his interdisciplinary approach. Notable projects include developing NILM systems for real-time energy monitoring and creating urban microclimate models (e.g., WRF-based simulations for Singapore). He advocates for data-driven approaches to improve building controls and reduce operational carbon footprints.

• Biosensing Technology
• Nanomaterials
• Medical Diagnostics
• +۵ مورد دیگر

Dr. Jing Pan is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Florida. He leads the Biodesign Lab, focusing on developing cutting-edge biosensing technologies for real-time monitoring of immune system dynamics and biomolecules in bodily fluids. His NSF CAREER Award-winning project aims to create continuous monitoring systems for immune status assessment, addressing critical gaps in clinical diagnostics. His research integrates nanomaterials, nanostructured surfaces, and biorecognition elements to enable rapid, sensitive detection of biomarkers. Applications span healthcare (e.g., ICU monitoring during pandemics), agriculture, and engineering education innovation. Collaborations include work with Dr. Idalis Villanueva Alarcón to objectively measure student anxiety via saliva-based biosensors, bridging clinical and educational challenges. Pan’s work emphasizes adaptive, personalized medical interventions through technologies akin to 'thermostats for the body.' His lab’s advancements in DNA walkers, molecular switches, and nanoparticle-based sensors reflect a multidisciplinary approach to biomedical engineering challenges. Key scientific contributions include pioneering real-time spatiotemporal monitoring systems and antibody-based molecular switches. Key Awards: NSF CAREER Award (2023) Lab Focus: Biodesign Lab (nanotechnology, biosensors, biomedical applications) Education Initiative: Biosensor-driven anxiety measurement in engineering students

Bart De Moorمشاهده پروفایل

استاد

• Mathematical Engineering
• System Theory and Control
• Numerical Optimization
• +۴ مورد دیگر

Professor Bart De Moor is a full professor at the KU Leuven Faculty of Engineering Sciences, affiliated with the Department of Electrical Engineering (ESAT) and the STADIUS Center for Dynamic Systems, Signal Processing, and Data Analysis. His research spans mathematical engineering, system theory, and biomedical data science, with significant contributions to Machine Learning Medical AI Time Series Analysis High-Dimensional Data Optimization underpinned by an ERC Advanced Grant and the Order of the Crown (2025). His recent work includes AI-driven clinical decision support systems ( BJOG , 2025), spatial omics pipelines for pancreatic cancer ( Cancer Research , 2025), and energy demand forecasting ( Applied Energy , 2025). He has supervised over 93 PhD students and founded 9 spin-offs, including Health House and Athumi .

Jörn Kiselevمشاهده پروفایل

استاد

• Physiotherapy
• Geriatrics
• Frailty in Older Adults
• +۷ مورد دیگر

Jörn Kiselev is a Professor of Physiotherapy at Hochschule Fulda, Faculty of Health Sciences, where he has been serving since April 2023. Prior to this, he was a post-doctoral researcher and project manager at the Department of Anesthesiology with a focus on surgical intensive care at Charité – Universitätsmedizin Berlin, working on the PRÄP-GO prehabilitation project. He holds a Dr. rer. medic. from Charité and academic degrees in Physiotherapy from Philipps-Universität Marburg and Hochschule Fulda. Research Interests: His work centers on improving health outcomes in older adults, particularly focusing on frailty, fall prevention, prehabilitation before surgery, functional mobility, and stair-climbing ability. He also explores innovative diagnostic tools such as acoustic emission analysis for osteoarthritis and digital health applications for mobility assessment. Recent Research Trends: His recent publications (2023–2019) reflect a strong emphasis on clinical and translational research in geriatric physiotherapy. Many studies involve interdisciplinary collaboration and use mixed-methods or randomized trial designs. Key themes include validating assessment tools (e.g., mHealth apps, gait analysis devices), understanding mobility limitations, and developing interventions for frail elderly patients, especially in perioperative contexts. Scientific Engagement: Member of the S3 guideline group for perioperative care of frail patients Delegate of the German Association of Physiotherapy (ZVK) Member of the German Society for Physiotherapy Sciences (DGPTW) Advising and Grants: While no direct students are listed, his leadership in the PRÄP-GO project indicates experience in mentoring junior researchers and managing funded research. His affiliations with Charité and BASE-II suggest involvement in large-scale, multi-institutional studies, likely supported by national or institutional grants. Labs and Teams: He has collaborated with research groups at Charité, including the Geriatrics Research Group led by Prof. Steinhagen-Thiessen, and has worked on technology validation projects involving sensor-based assessment chairs (aTUG) and mobile health apps. His work reflects strong ties to both clinical and engineering research teams focused on aging and mobility.

• Network Management
• Cognitive Radio Networks
• Internet of Things (IoT) Security
• +۷ مورد دیگر

Francine Krief is a Professor with extensive research in network management , QoS optimization , and context-aware security across cognitive radio networks, IoT, and vehicular systems. Her work integrates blockchain and fuzzy logic for secure, efficient resource allocation. Key Research Areas : Network Security, Cognitive Radio, IoT, Edge Computing Collaborations : Active partnerships with researchers like Mohamed Aymen Chalouf, Badr Benmammar, and Tidiane Sylla Publication Trends : Over the past decade, Krief has focused on blockchain applications in IoT security (2021), energy-efficient cognitive radio (2019), and SDN-based vehicular networks (2019). Recent work (2025) addresses user-centric IoT service placement in edge infrastructures.

• Cultural Algorithms
• Evolutionary Computation
• Artificial Intelligence
• +۵ مورد دیگر

Robert G. Reynolds is a Professor of Computer Science at Wayne State University's College of Engineering, where he leads pioneering research in Cultural Algorithms and evolutionary computation. With over 197 publications spanning from 1982 to 2024, he has established himself as a leading authority in computational intelligence and socially-inspired optimization techniques. His work bridges computer science, social modeling, and practical applications across diverse domains including education, healthcare, and archaeology. Reynolds' research interests center around Cultural Algorithms, which he developed as a framework for modeling how cultures solve problems through social learning and knowledge transmission. His work extends to evolutionary computation, swarm intelligence, and agent-based modeling, with applications ranging from virtual reality educational systems to medical imaging analysis. He has published the influential book 'Culture on the Edge of Chaos - Cultural Algorithms and the Foundations of Social Intelligence' (2018) that formalized his theoretical contributions. His recent publications (2022-2024) demonstrate continued innovation in applying Cultural Algorithms to diverse challenges including STEM education through virtual reality environments, medical image analysis, economic modeling, and climate change impact assessment. These works consistently show his approach of integrating social dynamics into computational problem-solving frameworks. Among his notable scientific contributions is the development of the Cultural Algorithms framework as a comprehensive approach to optimization and problem-solving that incorporates social dimensions often missing in traditional evolutionary algorithms. His work has influenced numerous researchers in the field of computational intelligence. Professor Reynolds has mentored numerous students who have become frequent collaborators, including Mostafa Z. Ali, Thomas Palazzolo, Sarah Saad, and Chencheng Zhang. His research has been supported by various grants that have enabled the development of projects like the Deep Dive Virtual Reality system for exploring ancient submerged civilizations and educational applications of AI. His laboratory work focuses on developing the Cultural Algorithms Toolkit (CAT) and applying it to real-world problems through virtual environments that simulate social and cultural dynamics. Current projects include extending Cultural Algorithms for trustable AI systems and developing more sophisticated models of cultural knowledge transmission.

• Physical Therapy
• Rehabilitation
• Chronic Low Back Pain
• +۷ مورد دیگر

Assoc. Prof. Dr. Anıl ÖZÜDOĞRU is an Associate Professor at Kırşehir Ahi Evran University's School of Physical Therapy and Rehabilitation, Department of Physiotherapy and Rehabilitation. With over 15 years of academic experience, he has progressed from Research Assistant to his current position as Associate Professor since 2024. His academic journey began with a Bachelor's degree, followed by Master's and Doctoral studies at Dokuz Eylül University, all in Physical Therapy and Rehabilitation. Bachelor's Degree (2003-2007): Dokuz Eylül University, School of Physical Therapy and Rehabilitation Master's Degree (2007-2010): Dokuz Eylül University, Institute of Health Sciences, Neurological Physiotherapy-Rehabilitation PhD (2010-2018): Dokuz Eylül University, Institute of Health Sciences, Physical Therapy and Rehabilitation Dr. ÖZÜDOĞRU's research focuses on chronic low back pain, knee osteoarthritis, cerebral palsy, voice rehabilitation, balance and proprioception, and sports physiotherapy. His work explores the relationship between physical activity, pain management, and functional outcomes, with particular attention to innovative rehabilitation approaches for diverse patient populations. He has made significant contributions to understanding the biomechanics of movement and the application of evidence-based practices in clinical settings. His recent publications demonstrate a strong emphasis on evidence-based rehabilitation approaches, with increasing attention to innovative techniques like AI applications in personalized rehabilitation programs, advanced measurement tools for assessing functional outcomes, and the integration of psychological factors in physical rehabilitation. His research spans from geriatric care to pediatric rehabilitation, showing a comprehensive approach to physical therapy across the lifespan. Dr. ÖZÜDOĞRU has served as main advisor for numerous PhD and Master's students, guiding research on topics including chronic pain management, cerebral palsy, and carpal tunnel syndrome. He has been involved in multiple research projects funded by higher education institutions, including studies on geriatric rehabilitation, cerebral palsy, and musculoskeletal disorders. His teaching portfolio includes doctoral, master's, and undergraduate courses in specialized areas of physical therapy and rehabilitation.

• Microbiology
• Virology
• Immunology
• +۳ مورد دیگر

Catharina Hultgren serves as an Assistant Senior Lecturer and Docent (Associate Professor) at Karolinska Institutet's Department of Laboratory Medicine, Division of Clinical Microbiology. Her dual expertise bridges cutting-edge virology research and innovative medical education practices. Her academic qualifications include: Doctor of Philosophy, Department of Dental Medicine, Karolinska Institutet (2000) Licentiate Degree, Department of Dental Medicine, Karolinska Institutet (1997) Docent title awarded in 2024 Hultgren's research trajectory reveals a significant evolution from fundamental virology toward educational scholarship. Her early career focused on hepatitis virus pathogenesis, particularly examining immune responses to hepatitis B and C, vaccine development, and viral immune evasion mechanisms. More recently, she has pioneered research in microbiology education, investigating assessment methodologies including take-home examinations, viva voce assessments, and the integration of AI tools like ChatGPT in microbial pathogenesis instruction. This transition demonstrates her commitment to evidence-based educational practices in biomedical training. Analysis of her publication history shows a clear shift from laboratory-based virology research toward educational methodology. While her 2004-2016 work centered on hepatitis virus mechanisms and immune responses, her 2023-2025 publications focus exclusively on assessment techniques and technology integration in microbiology education. This represents a deliberate scholarly pivot toward improving how biomedical laboratory science students learn essential microbiological concepts. Her educational research has been published in specialized journals including Journal of Microbiology & Biology Education and Journal of Educational Evaluation for Health Professions, while her earlier virology work appeared in high-impact journals such as Journal of Infectious Diseases, Gut, and Journal of Virology. As an educator at Karolinska Institutet, Hultgren contributes significantly to curriculum development and student assessment in biomedical laboratory science. Her dual expertise in microbiology content and educational methodology positions her as a valuable resource for developing effective teaching strategies in medical education. She works within the Division of Clinical Microbiology, which serves as a critical bridge between basic microbiological research and clinical diagnostic practice.

• Rheumatology
• Autoimmune Diseases
• Biomarkers
• +۲ مورد دیگر

Dr Steven Young-Min is a researcher at Newcastle University, UK, specializing in rheumatology and autoimmune diseases. His work focuses on biomarkers, clinical trials, and disease progression in conditions like rheumatoid arthritis and primary Sjögren's syndrome. Research Interests Investigation of biomarkers (e.g., serum MMP-3, cytokines) to predict radiographic progression in rheumatoid arthritis Characterization of fatigue mechanisms in Sjögren's syndrome through transcriptional profiling Clinical trial design optimization for autoimmune diseases Discrepancies between subjective and objective measures of disease symptoms Role of antiferritin antibodies in radiographic damage Genetic risk factors (DR4 shared epitope) in rheumatoid arthritis Publications 2019: Symptom-based stratification of Sjögren's syndrome patients 2018: ESSDAI and ClinESSDAI comparison for trial outcomes 2017: Subjective vs objective dryness measures in Sjögren's syndrome 2016: Fatigue and cytokine levels in Sjögren's syndrome 2015: Transcriptional signature of fatigue in Sjögren's syndrome 2005-2008: Biomarker studies in early rheumatoid arthritis

Roberto Iglesias Rodríguezمشاهده پروفایل

دانشیار

• Machine Learning
• Robotics
• Continual Learning
• +۶ مورد دیگر

Roberto Iglesias Rodríguez is an Associate Professor at the University of Santiago de Compostela, Spain, with a focus on robotics and machine learning. He holds a B.S. and Ph.D. in Physics from the same institution (1996, 2003). His work addresses lifelong robot learning, federated learning, and deployment of intelligent systems in heterogeneous environments. Current affiliation: University of Santiago de Compostela Academic rank: Associate Professor Education: B.S. and Ph.D. in Physics (1996, 2003) His research spans robotics, machine learning, and sensor fusion , emphasizing adaptive algorithms for non-IID data, concept drift, and real-time environmental interaction. Projects include service robots learning from humans, distributed control architectures, and federated learning strategies. Recent publications analyze continual learning , scene recognition , and human-robot collaboration . Key themes: robust navigation, multi-sensor systems, and explainable AI.

• Machine Learning
• Neuroscience
• Medical Informatics
• +۳ مورد دیگر

Sebastian Niehaus is a Doctoral Researcher affiliated with the Max Planck Institute for Human Cognitive and Brain Sciences and TU Dresden. His work bridges Neuroscience and Machine Learning , focusing on Medical Informatics and Statistical Computing . He develops scalable AI applications for clinical workflows through standardized data harmonization pipelines.

Frederick Johnsonمشاهده پروفایل

پژوهشگر

• Educational Technology
• Digitalization in Education
• Teacher Training
• +۳ مورد دیگر

Frederick Johnson serves as a Researcher at the University of Trier within the Faculty of Educational Sciences, Department of Educational Sciences III. He currently works as a Research Associate for the LeBi: DireKT project under Prof. Dr. Christoph Schneider at the Chair of Diagnostics, Differentiation, Integration, and Counseling, with office located in room DM 039. Johnson's research program centers on digitalization-related attitudes and skills in educational contexts, motivation in learning environments, and the relationship between meaning of life and educational outcomes. His work particularly examines how teacher training students develop digital competencies throughout their academic programs, with significant focus on longitudinal development patterns and the impact of emergency remote education during the pandemic. His methodological approach combines quantitative assessment techniques with longitudinal analysis to track developmental trajectories in educational contexts. Johnson maintains an active publication record with numerous journal articles and conference presentations through 2024. His recent work analyzes the effectiveness of interventions for developing DigCompEdu competencies, the divergent development of digitalization-related attitudes and competencies in teacher education, and technology commitment profiles among pre-service teachers. His research often employs the TRIGIKOM'MON framework for monitoring digital competencies in teacher education. Johnson frequently presents at major educational conferences including the European Conference on Educational Research (ECER) and conferences of the German Society for Empirical Educational Research. His collaborations include researchers such as J. Koßmann, C. Schneider, and L. Müller, with whom he investigates the complex relationship between self-assessed competencies and objectively measured skills in teacher candidates, particularly regarding TPACK (Technological Pedagogical Content Knowledge).