جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Medical Imaging
• Signal Processing
• +۳ مورد دیگر

João Miguel Sanches is a Full Professor at the Department of Bioengineering (DBE) at Instituto Superior Técnico (IST). He coordinates the Biomedical Systems and Biosignals scientific area and leads the Master's and Doctoral Programs in Biomedical Engineering. As Deputy President for Scientific Affairs at the Institute for Systems and Robotics (ISR-Lisboa), he focuses on advancing biomedical engineering through interdisciplinary research. His research spans biomedical imaging, signal processing, and computational methods for cancer biomarker quantification and psychiatric assessment. Current projects include developing AI-driven tools for early cancer detection and emotional/cognitive evaluation in mental health. With over 200 international publications, his work bridges engineering innovations with clinical applications. Publications highlight advancements in cell cycle analysis, medical image processing (e.g., ultrasound, OCT), and wearable health technologies. Notable contributions include computational models for cancer cell architecture and telemedicine platforms for depression management. A Senior Member of IEEE Engineering in Medicine and Biology Society (EMBS), he collaborates across academic and clinical sectors. His lab integrates robotics, AI, and biomedical systems to address unmet needs in diagnostics and patient care.

Zafer Doganمشاهده پروفایل

استادیار

• Machine Learning
• Data Analytics
• Signal Processing
• +۵ مورد دیگر

Zafer Dogan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) research group and serves as Deputy Director of the Koç University İşbank AI (KUIS AI) Research Center. He holds a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and a B.Sc. from Middle East Technical University (METU). Before joining Koç University, he worked as a postdoctoral researcher at Harvard University's John A. Paulson School of Engineering and Applied Sciences (2016–2019). His research bridges theoretical advancements in machine learning, optimization, and signal processing with practical applications in high-dimensional data mining, AI-driven imaging (e.g., biomedical imaging), and inverse problem solutions. Key areas include generative models, image super-resolution, neural network dynamics, and domain-specific interpolation techniques. His work emphasizes trustworthy AI, perceptual-quality image enhancement, and biomedical applications like neuroimaging analysis. Notable contributions include developing the MMSR framework for class-specific image super-resolution, wavelet-domain losses for artifact reduction, and leveraging vision-language models for trustworthy super-resolution. His articles span domains from theoretical ML to applied biomedical imaging solutions. Dr. Dogan’s academic journey includes postdoctoral training at Harvard and a strong focus on interdisciplinary research at the intersection of engineering and AI. He currently leads initiatives at KUIS AI, advancing applied AI research and innovation.

• Computer Vision
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Sridharan Sridha is a Professor at the University of Queensland, specializing in advanced AI and computer vision research. His work spans neural networks, robotics, medical informatics, and surveillance systems. He collaborates extensively with institutions like the University of Queensland’s School of Information Technology and Electrical Engineering. Key research focuses include adversarial machine learning, multimodal fusion, and domain adaptation. His contributions to aerial-ground person re-identification (AG-ReID), LiDAR-based place recognition, and medical signal analysis have been widely recognized. Recent projects emphasize self-supervised learning, transformer-based architectures for hyperspectral imaging, and autism severity detection using physics-augmented models. His work bridges theory and practical applications in autonomous systems, healthcare, and robotics.

• Machine Learning
• Compressed Sensing
• Privacy-Preserving Techniques
• +۴ مورد دیگر

Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .

• Machine Learning
• Cognitive Science
• Virtual Reality (VR)
• +۳ مورد دیگر

Adrian Vulpe-Grigorasi is a Junior Researcher at the Center for Digital Health and Social Innovation , affiliated with the Institute of Health Sciences at FH Steyr (FHSTP.ac.at). He holds BEng and MEng degrees and specializes in interdisciplinary research at the intersection of machine learning, cognitive science, and biomedical engineering. His research focuses on: Cognitive load assessment using VR systems and biosensors Development of multimodal machine learning frameworks for health monitoring Applications of GANs in ECG analysis and synthetic data generation Energy systems optimization through data-driven approaches Key projects include: Realistic clinical XR training systems Attention performance classification via eye tracking Smart grid forecasting with GAN data augmentation Adrian has published in conferences such as IEEE Informatics, CGI, and ACM MUM. His work bridges theoretical machine learning advancements with practical healthcare and energy applications.

Ilias Maglogiannisمشاهده پروفایل

استاد

• Artificial Intelligence
• Biomedical Informatics
• Image Processing
• +۷ مورد دیگر

Professor Ilias Maglogiannis is a leading academic in Computational Biomedicine at the University of Piraeus , directing its Computational Biomedicine Laboratory. He holds a PhD from the National Technical University of Athens and has held faculty positions at the University of the Aegean and University of Thessaly before joining the University of Piraeus in 2013. He has served as Dean and Department Chair, leading large-scale EU projects like AI4WORK and MELIORA . His research focuses on AI in Healthcare , including medical imaging, wearable devices, and telemedicine systems. He has published over 400 papers (h-index 46), three books, and serves on editorial boards of journals like IEEE JBHI and Personal and Ubiquitous Computing . Key Awards: Fellow of EAMBES, Senior IEEE Member Leadership: IFIP WG12.5 President (AI Applications) Current Projects: MedSecurance (IoMT security), E-Prevention (mental health monitoring) His teaching includes courses on Pattern Recognition , Telemedicine , and Digital Image Processing . He actively promotes AI ethics and human-centric digital twin technologies in healthcare and education sectors.

• Educational Technology
• Learning Management Systems
• Data Privacy
• +۷ مورد دیگر

Maria Alsina Claret is an Associate Lecturer in the Enterprise and Technology Department at La Salle International School of Commerce and Digital Economy, Ramon Llull University. She is actively engaged in research related to educational technology, digital competence, and data privacy in learning environments. Research Interests: Her work focuses on Learning Management Systems , data extraction and analytics , digital competence assessment , human-computer interaction , and privacy in EdTech . She is particularly interested in leveraging technology to enhance teaching and learning experiences through personalized and data-driven approaches. Recent Research Trends: Her recent publications (2020–2024) reflect a strong emphasis on using LMS data to assess teacher digital skills, improving student performance via self-directed learning, and developing privacy-preserving tools like the Personal Data Broker. The integration of AI and data literacy in education is a growing theme in her current projects. Scientific Projects: IADE: AI and Data Literacy in Education (2024–2026) DISTRESIA: Non-invasive Stress Biomarkers for Oral Presentations (2024) Sarrià Sant Gervasi Talent District (2024) Evaluación NCA: New Learning Context Indicators (2020) GRETEL: Research Group on Technology-Enhanced Learning (2017–2021) Advising and Grants: While no formal advisees are listed, she has participated in multiple research grants funded by AGAUR and internal university calls. She collaborates extensively with researchers in educational innovation, contributing to interdisciplinary teams focused on technology-enhanced learning and digital transformation in education. Laboratories and Teams: She is a core member of the GRETEL (Grup de Recerca en Technology Enhanced Learning) , a research group dedicated to advancing digital learning through innovative technologies and pedagogical models.

Peter Washingtonمشاهده پروفایل

استادیار

• Consumer Digital Health
• Human-Centered AI
• Artificial Intelligence / Machine Learning
• +۱۹ مورد دیگر

Peter Washington is an Assistant Professor in the Department of Medicine at the University of California, San Francisco (UCSF), affiliated with the School of Medicine and the Division of Clinical Informatics and Digital Transformation (DoC-IT). He leads the UCSF TECH Lab (Technologies Empowering Consumer Health), focusing on human-centered AI for digital health applications. Education: PhD in Bioengineering, Stanford University (2022) MS in Computer Science, Stanford University (2018) BA in Computer Science, Rice University (2015) His research centers on consumer digital health informatics, leveraging data from wearables, smartphones, and specialized health devices to develop digital diagnostics, therapeutics, and monitoring tools. Key areas include autism and ADHD screening, mental health, substance use, chronic disease management, and global digital health. His work emphasizes ethical AI, crowdsourcing, and personalized machine learning. The recent publications highlight a strong trend in applying AI to real-world health challenges, particularly through passive sensing, social media data, and multimodal inputs. His research spans digital phenotyping, remote monitoring, and bias mitigation in clinical AI, with a focus on implementation and equity. Scientific Awards: NIH New Innovator Award (2023–2028) Stanford Interdisciplinary Graduate Fellowship (2020–2022) Dr. Washington teaches Epi 233 – Introduction to Clinical Artificial Intelligence – and mentors UCSF students, staff, residents, and fellows in research projects. His lab is funded by the NIH and NSF, including as Principal Investigator on grants focused on personalized machine learning and crowd-powered diagnostics for neurodevelopmental conditions. He is actively involved in global health equity initiatives through the Institute for Global Health Sciences at UCSF. Labs and Teams: UCSF TECH Lab (Technologies Empowering Consumer Health) Collaborator with Wall Lab and other interdisciplinary teams

Daivaras Sokasمشاهده پروفایل

پژوهشگر

• Digital Signal Processing
• Machine Learning
• Biomedical Engineering
• +۱ مورد دیگر

Daivaras Sokas is a researcher at the Institute of Biomedical Engineering, Kaunas University of Technology, affiliated with the Biosignal Analytics Laboratory. His primary responsibilities include conducting laboratory work and grading reports for the course 'Digital Signal Processing and Machine Learning.' He holds an ORCID ID (0000-0001-5723-3655). Research Interests: His work focuses on Digital signal processing Machine learning applications in biomedical contexts Electrical and electronic engineering (T001) Laboratory Affiliation: He is actively involved with the Biosignal Analytics Laboratory at KTU.

Andrius Petrėnasمشاهده پروفایل

دانشیار

• Biosignal Processing
• Physiological Signal Modeling
• Clinical Data Analysis
• +۳ مورد دیگر

Dr. Andrius Petrėnas is a Senior Researcher and Associate Professor at the Kaunas University of Technology (KTU), affiliated with the Faculty of Electrical Engineering and the Department of Electronics Engineering. He serves as the Laboratory Manager of the Biosignal Analytics Laboratory within the KTU Institute of Biomedical Engineering, where he leads research initiatives focused on biosignal processing algorithms and physiological signal modeling. Scientific role: Senior Researcher in projects SV/115, SV3/116, and SV3240177 Primary research interests: analysis of biosignals for clinical applications, algorithm development, and physiological system modeling

• Biomedical Engineering
• Electrical Engineering
• Circuit Design
• +۷ مورد دیگر

Christofer "Chris" Toumazou is a distinguished British Cypriot electronic engineer serving as the Regius Professor of Engineering at Imperial College London, a prestigious appointment made during the Queen's Diamond Jubilee in 2013. He also holds positions as Chief Scientist of the Institute of Biomedical Engineering and Professor of Circuit Design at Imperial. Beyond academia, Toumazou is an accomplished entrepreneur, having founded Toumaz Holdings Ltd and DNA Electronics Ltd., and co-founding DNAnudge, which developed the groundbreaking CovidNudge point-of-care diagnostic system. With a research portfolio spanning over 800 publications, Toumazou's work primarily focuses on the intersection of semiconductor technology and biomedical applications. His most significant contribution is the development of ISFET (Ion-Sensitive Field-Effect Transistor) technology for DNA detection, which eliminates the need for optical systems in genetic testing and enables miniaturized, lab-free diagnostic devices. This innovation has been applied across various healthcare domains including pandemic response, cancer diagnostics, and personalized medicine. Toumazou's publication record shows consistent high-impact output across biomedical engineering, electrical engineering, and applied physics. His recent work (2020-2022) has particularly emphasized point-of-care diagnostics, personalized healthcare systems integrating genetic information with real-time monitoring, and novel approaches to DNA analysis. His research demonstrates a clear trajectory from fundamental circuit design to practical healthcare applications that address significant clinical needs. Fellow of the Royal Society (FRS) Fellow of the Royal Academy of Engineering (FREng) Fellow of the Academy of Medical Sciences (FMedSci) Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Institute of Electrical and Electronics Engineers (FIEEE) Regius Professor of Engineering at Imperial College London Professor Toumazou maintains an active research program with numerous ongoing projects bridging engineering and medicine. His work continues to focus on developing next-generation diagnostic technologies that integrate semiconductor electronics with biological systems, with particular emphasis on making sophisticated genetic testing accessible at the point of care. Through his academic position and entrepreneurial ventures, he maintains strong connections between fundamental research, commercial development, and clinical implementation of new healthcare technologies.