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Sami ÄyrämöView profile

Associate Professor

• Computer Science
• Machine Learning
• Health Science
• +11 more

Sami Äyrämö is an Associate Professor at the Faculty of Information Technology , University of Jyväskylä. His research bridges machine learning and health science , focusing on innovative applications in biomechanics , medical imaging , and exercise physiology . Specializes in automated scoring systems for medical diagnostics Pioneer in domain-specific transfer learning for healthcare data Develops synthetic data for wellbeing sector innovation His work spans colorectal cancer tissue analysis , ACL injury risk modeling , and dementia detection from speech , with recent studies applying cluster analysis and deep learning to sports biomechanics challenges. Current projects include the WellbeingDataLab initiative for synthetic exercise data, and collaborations with the Computational Data Science Research Group on spectral imaging and health analytics.

• Cultural Studies
• Media Research
• Media Education
• +5 more

Rainer Winter is a Professor at the Institute for Media and Communication Studies at Alpen-Adria-Universität Klagenfurt. He specializes in Cultural Studies, Media Research, Media Education, and Qualitative Research Sociology. His work bridges critical theory with contemporary media analysis, focusing on mediatization processes, resistance studies, and the societal implications of media narratives.

• Artificial and Augmented Intelligence
• Biomedical Informatics and Data Science
• Medical Image Analysis
• +3 more

Baosheng Yu serves as an Assistant Professor of Digital Health at the Lee Kong Chian School of Medicine, Nanyang Technological University (NTU), Singapore, with prior experience as a Research Fellow at the University of Sydney, Australia. His academic credentials include: Bachelor of Engineering (B.E.) from University of Science and Technology of China (USTC), 2014 Ph.D. from University of Sydney (USYD), 2019 Dr. Yu's research integrates cutting-edge artificial intelligence with multimodal medical data—spanning imaging, clinical text, and physiological signals—to revolutionize diagnostic precision and therapeutic efficacy. His work bridges Artificial and Augmented Intelligence , Biomedical Informatics , and Data Science , with specialized focus on medical image segmentation, clinical NLP for electronic health records, and real-time signal analysis for patient monitoring systems. He actively recruits PhD candidates and Research Associates/Fellows for digital health initiatives, indicating robust research momentum. While specific grant portfolios remain unspecified, his methodology suggests strong alignment with Singapore's national priorities in AI-driven healthcare transformation and precision medicine.

Navid FarajiView profile

Assistant Professor

• Radiology Education
• Musculoskeletal Imaging
• Medical Imaging
• +4 more

Navid Faraji, MD is an Assistant Professor in both the Department of Radiology and Department of Anatomy at Case Western Reserve University School of Medicine. He also serves as Associate Program Director for the Diagnostic Radiology Residency at UH Cleveland Medical Center, where he actively mentors medical students and residents in radiology research initiatives. University: Case Western Reserve University School: School of Medicine Departments: Radiology, Anatomy Academic Rank: Assistant Professor Email: navid.faraji@uhhospitals.org His research spans two primary domains: Radiology Education and Musculoskeletal Imaging . Recent publications demonstrate expertise in: Educational technology integration (e.g., Anki flashcards) Emergency fracture detection tool validation Bone marrow edema patterns in athletes/military trainees Gender equity initiatives in radiology pipeline AI-human collaboration in diagnostic imaging Cross-modality image registration techniques Scientific accolades include the Wei-Shen Chin Award for Medical Student Teaching (2022) . His 2023-2025 publications focus on optimizing radiology workflows, ballet dancer injuries, and incidental lesion analysis, with emerging themes in synthetic imaging and psychological safety frameworks.

A. Lynn AbbottView profile

Professor

• Computer vision
• Pattern recognition
• Artificial intelligence
• +5 more

A. Lynn Abbott is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech , specializing in computer vision, biometrics, and AI-driven sensing systems. His work bridges theoretical and applied domains, including autonomous vehicle perception, physiological signal analysis, and secure healthcare monitoring. Education: Ph.D., University of Illinois, 1990 M.S., Stanford University, 1981 B.S., Rutgers University, 1980 Research Interests focus on computer vision for autonomous systems, biometrics using physiological signals, and deep learning applications in transportation safety and healthcare. Recent projects include neural networks for intersection safety modeling and vision-based cardiovascular signal recovery. Publications highlight advancements in graph neural networks for traffic analysis, spatiotemporal filtering for 3D object detection, and privacy-preserving biometric authentication. His work spans disciplines like transportation safety, biomedical signal processing, and computer architecture. Labs & Teams: Affiliated with the Center for Embedded Systems for Critical Applications , contributing to real-time vision systems and hardware-software co-design for safety-critical domains.

Ting-Chung PoonView profile

Professor

• Optical Scanning Holography
• Digital Holography
• 3D Imaging
• +3 more

Ting-Chung Poon is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on optical scanning holography (OSH), digital holography, and 3D imaging applications. He leads the Optical Scanning-Holographic Imaging Group (OSIG), which explores OSH for 3D imaging, processing, and display, emphasizing 2D optical heterodyne scanning techniques. Education: Ph.D., University of Iowa, 1982 M.S.E.E., University of Iowa, 1979 B.A., University of Iowa, 1977 Research Interests: Optical Scanning Holography (OSH) and its applications in 3D imaging Computer-Generated Holography (CGH) Quantitative Phase Imaging Optical Cryptography Efficient Hologram Algorithms His work spans theoretical advancements and practical implementations, including encryption systems, noise reduction techniques, and high-resolution 3D reconstruction. Recent Research Trends: Focus on polygon-based CGH algorithms for faster rendering Integration of machine learning for speckle noise reduction and hologram classification Development of adaptive and compressive holography methods for industrial and biomedical applications Labs & Teams: Optical Scanning-Holographic Imaging Group (OSIG) at Virginia Tech Collaborations with institutions globally, including conferences on digital holography and photonics

Adín Ramírez RiveraView profile

Professor

• Computer Vision
• Machine Learning
• Representation Learning
• +6 more

Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.

Taran RaiView profile

Researcher

• Computational Pathology
• Deep Learning
• Digital Pathology
• +3 more

Dr. Taran Rai is a Researcher at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) and the School of Veterinary Medicine. His work focuses on computational pathology, deep learning for medical imaging, and applying AI to veterinary medicine. Rai holds a PhD and has contributed to advancements in necrosis and mitosis detection in canine tumors, leveraging CNNs and digital pathology. His research interests include AI-driven diagnostic tools, social media listening for health insights, and optimizing neural networks for medical applications. He has published extensively on topics like synthetic histopathology data evaluation, diffusion models, and adaptive thresholding methods in pathology. Rai's recent work explores the integration of large language models with medical imaging (e.g., the IPATH dataset) and addresses challenges in veterinary healthcare through social media data. His studies often bridge computational methods with real-world clinical needs, aiming to improve diagnostic accuracy and patient care.

• Biomedical Signal Processing
• Fetal Monitoring
• Maternal-Fetal Medicine
• +3 more

Prof. SG (Guid) Oei is a full-time faculty member at the Eindhoven University of Technology (TU/e) in the Biomedical Diagnostics Lab under the Department of Electrical Engineering. He serves as a leading academic in the Eindhoven MedTech Innovation Center and the Center for Care & Cure Technology Eindhoven , focusing on advanced signal processing systems for maternal-fetal health diagnostics. Primary Affiliation: Professor , Electrical Engineering, TU/e Research Centers: Eindhoven MedTech Innovation Center, Biomedical Diagnostics Lab, Center for Care & Cure Technology Eindhoven Research Focus : Developing non-invasive fetal monitoring technologies, including electrohysterography and speckle tracking echocardiography , to improve detection of fetal distress, preterm birth prediction, and maternal-fetal health outcomes. His work bridges biomedical diagnostics with machine learning, emphasizing real-time clinical applications. Scientific Contributions : Over 288 research outputs with 4568 citations, including pioneering studies on: Fetal myocardial deformation analysis AI-enhanced cardiotocogram interpretation Extra-uterine life support system design Impact of maternal mental health on labor outcomes Optimization of uterine monitoring techniques Collaborative Networks : Works with key researchers like Jan WM Bergmans (NeuroPlatform), Massimo Mischi (Biomedical Diagnostics), and Judith OEH van Laar on projects spanning prenatal diagnostics, maternal-fetal coupling, and simulation-based obstetric training.

Jeyeon JoView profile

Assistant Professor

• Wearable Sensors
• Assistive Technology
• Textile Engineering
• +7 more

Jeyeon Jo is an Assistant Professor in the Department of Textiles, Merchandising and Interiors at the University of Georgia's College of Family and Consumer Sciences. Her research focuses on wearable sensors, assistive technology, and ergonomic design, with an emphasis on sustainable materials and healthcare applications. She holds a Ph.D. in Human Centered Design from Cornell University (2023), an M.S. in Fashion Design (2017), and a B.S. in Clothing and Textiles (2014), both from Seoul National University. **Education:** Ph.D. Human Centered Design, Cornell University (2023) M.S. Fashion Design, Seoul National University (2017) B.S. Clothing and Textiles, Seoul National University (2014) **Research Interests:** Jeyeon develops wearable healthcare solutions using stretchable fiber optics and passive RFID, with a focus on optimizing user comfort and performance. Her work spans assistive technologies, firefighting gear ergonomics, and cross-cultural studies of clothing's sociocultural role. Recent projects include sustainable textile prototyping (LeatherBoard) and gait-monitoring systems (RFInsole). **Awards:** Human Service Studies Graduate Fellowship (Cornell, 2022) Best Brief at ISWC 2021 (2021) Roberta G. & John B. De Vries Graduate Award (Cornell, 2020) **Grants & Advising:** Currently no grants explicitly listed; actively advising students in wearable tech and textile engineering. Editorial roles include Fashion and Textiles (2024–present) and Korean Fashion & Textile Research Journal (2025–2026).

Thomas HeldtView profile

Associate Professor

• Computational Physiology
• Neuromonitoring
• Critical Care Informatics
• +2 more

Thomas Heldt is Associate Professor of Electrical and Biomedical Engineering in the Department of Electrical Engineering and Computer Science at MIT, and a Principal Investigator at MIT's Research Laboratory of Electronics. He leads the Integrative Neuromonitoring and Critical Care Informatics Group and serves as Associate Director of the Institute for Medical Engineering and Science. Research focuses on: Noninvasive intracranial pressure monitoring Computational models of cerebrovascular dynamics Sepsis detection algorithms Wearable physiological monitoring Clinical decision support systems Recent publications (2022-2024) demonstrate advances in hemodynamic modeling, diagnostic algorithms for critical care, and AI applications for physiological monitoring. Key innovations include open cranium models for intracranial hypertension studies, deep learning frameworks for fatigue assessment, and mobile-based neurocognitive tracking. Professor Heldt collaborates with Boston Children's Hospital, Beth Israel Deaconess Medical Center, and Boston Medical Center to translate research into clinical practice. His work has been recognized through the W.M. Keck Career Development Professorship and IEEE EMBS Distinguished Lectureship.

Dina KatabiView profile

Professor

• Digital Health
• Wireless Sensors
• Machine Learning
• +3 more

Dina Katabi is the Thuan and Nicole Pham Professor of Electrical Engineering and Computer Science at MIT, leading the Katabi Lab and directing the MIT Center for Wireless Networks and Mobile Computing. Her research bridges AI, wireless systems, and digital health, focusing on non-invasive health monitoring via wireless signals and machine learning. She is a MacArthur Fellow and holds the Andrew & Erna Viterbi Professorship. Key research areas include emotion recognition (EQ-Radio), sleep posture monitoring (BodyCompass), and through-wall human pose estimation. Her lab develops AI systems for biosensors, leveraging RF signals to detect diseases like Parkinson's and Alzheimer's. Notable awards include the ACM Prize in Computing and SIGCOMM's Lifetime Achievement Award. Publications span wireless networks, computer vision, and health tech, with impactful work in CVPR, ECCV, and Nature Medicine. She advises over 20 students/postdocs and collaborates on technologies like in-body backscatter communication and AI-driven drug development monitoring. Labs: Katabi Lab (MIT CSAIL) and the MIT Wireless Center. Ongoing work explores digital biomarkers, self-supervised learning, and scalable health monitoring systems for chronic diseases.

• Networking
• Cybersecurity
• Machine Learning

Dr. Ahmad Azab is a Lecturer at the University of Sydney’s School of Computer Science, specializing in Networking, Cybersecurity, and Machine Learning. He holds a PhD and industrial certifications including CISSP, CCSP, and CCNA. His teaching spans networking, cybersecurity, ethical hacking, and machine learning applications. Research interests focus on network traffic classification, IoT integration in cognitive radio networks, malware analysis, and cybersecurity countermeasures. His work bridges academic research with industrial applications, emphasizing practical solutions for emerging threats. Publications highlight innovations in botnet detection, malware classification, and cybersecurity frameworks. Supervision of bachelor and master’s projects underscores his commitment to mentoring future technologists. Professional affiliations include IEEE and ISC².

• Biomedical Engineering
• Electrical Impedance Tomography (EIT)
• Medical Device Design
• +6 more

Professor Alistair McEwan, affiliated with the University of Sydney's School of Biomedical Engineering, specializes in biomedical devices and instrumentation for neurological and cardiovascular diagnostics. As the Cerebral Palsy Chair of Technology and Engineering and Associate Head (External Engagement), he focuses on low-cost medical solutions for resource-limited settings, including neonatal care and stroke detection. His research bridges biophysics and clinical translation, with applications in electrical impedance tomography, wearable sensors, and neuroprosthetics. PhD from University of Oxford Member of the Sydney Nano Institute, Brain and Mind Centre, and Charles Perkins Centre His work explores how tissue electrical properties can enhance understanding of neural signaling and implant design. Clinical collaborators include the Bill & Melinda Gates Foundation for newborn nutrition monitoring. Recent projects involve AI-assisted critical care modeling, bionic devices for stress measurement, and adaptive control systems for neonatal resuscitation equipment.

• Fourier Analysis
• Digital Signal/Image Processing
• Phase Recovery Problem
• +2 more

Comlan de Souza is a Professor of Mathematics at Fresno State, affiliated with the College of Science and Mathematics. He holds a Ph.D. in Mathematics from Southern Illinois University at Carbondale. His research focuses on Fourier Analysis, Digital Signal/Image Processing (particularly the phase recovery problem), Numerical Linear Algebra, and B-splines interpolation. Dr. de Souza teaches a wide range of mathematics courses at both undergraduate and graduate levels. His academic contributions include advancing theoretical and applied aspects of his research areas. While no specific articles or awards are listed, his teaching encompasses courses such as Math 6 through Math 271, reflecting his broad expertise in foundational and advanced mathematical topics.