جست‌وجوی استاد و پژوهشگر

Jie Huangمشاهده پروفایل

دانشیار

• Sensor Technologies
• Optical Sensors
• Microwave Sensors
• +۵ مورد دیگر

Dr. Jie Huang, the Roy A. Wilkens Endowed Associate Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology, leads cutting-edge research in sensor technologies for extreme environments. As founder of the Lightwave Technology Laboratory (LTL), his work focuses on optical and microwave sensors with applications in energy, infrastructure, and biomedical diagnostics. Ph.D. in Electrical Engineering, Clemson University (2015) M.S. in Electrical Engineering, Missouri S&T (2012) B.S. in Opto-electronics Information Engineering, Tianjin University (2009) His research spans fiber optic sensors , microwave photonics , and machine learning-enhanced sensing , with over 150 refereed articles and 10 US patents. Key areas include: High-temperature sensing in steel manufacturing Biomedical wearable devices Distributed sensing for industrial safety Novel photonic/microwave micro/nano sensors Recent publications emphasize hybrid microwave-photonics sensing for extreme environments, machine learning integration for accuracy, and miniaturized biosensors for health monitoring. Scientific recognition includes: Top 2% Scientists in Optoelectronics & Photonics (2021-2023) Faculty Excellence and Research Awards at Missouri S&T Best Paper Awards at ASME and IEEE conferences Endowed professorship and travel grants As mentor to multiple award-winning students, Dr. Huang's work is funded by DOE, DOD, NSF, NIH, DARPA, and national laboratories. His lab's innovations in femtosecond laser inscription and metal-organic frameworks have transformed industrial and biomedical sensing.

Dunwei Wenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dr. Dunwei Wen is Associate Professor and Chair at the School of Computing and Information Systems within Athabasca University's Faculty of Science and Technology. With academic credentials from Ph.D. in Pattern Recognition and Intelligent Systems (Central South University) M.Sc. in Computer Science (Tianjin University) B.Eng. in Electrical Engineering (Hunan University) , he bridges theoretical AI research with practical implementations in information systems. His research program focuses on statistical learning and deep learning for Natural language processing Sequential data analysis Multimodal content understanding with applications spanning education, healthcare, and industrial domains. Publication trends show increasing emphasis on deep learning architectures for medical image analysis (SRTNet 2024), contextual topic modeling in education (2013-2015), and multimodal systems combining text/image analysis (2015-2018). Recent projects (2021-2022) center on self-supervised learning for natural language understanding. Professional engagements include Member, AAAI (Association for the Advancement of Artificial Intelligence) Member, ACM and ACM SIGAI Senior Member, IEEE Former CAAI Board Member (2001-2010) As academic advisor, he has supervised over 25 graduate students and interns, including Co-supervised 4 PhD candidates (Jilin University) Mentored 12+ Master's students (AU, Jilin University) Hosted 6 MITACS Globalink Research Interns with projects spanning from cardiac detection systems to educational data mining.

Lisa Gatzke-Koppمشاهده پروفایل

استاد

• Child Development
• Neurobiology of Behavior
• Decision-Making Mechanisms
• +۴ مورد دیگر

Lisa Gatzke-Kopp is a Professor at the College of Health and Human Development , Penn State University, specializing in Human Development and Family Studies. Her research explores neurobiological mechanisms underlying child behavior, decision-making, and emotional regulation, with a focus on environmental influences and prevention programming. Education : Ph.D. in Clinical Neuroscience (2003), University of Southern California Her work integrates psychophysiological measures (ECG, EEG, ERP, fMRI) with genetic and environmental analyses to understand developmental pathways. Key projects include the Family Life Project (NIH-funded) and studies on socioemotional development in children with externalizing behaviors (Pennsylvania Department of Health-funded). Recent publications highlight her contributions to neurodevelopmental impacts of environmental toxins (lead, tobacco smoke), reward processing in psychopathology, and methodological advancements in EEG/ERP research. She emphasizes diversity in participant recruitment and served as Guest Editor for a Psychophysiology special issue on representation. Her lab employs innovative approaches like mobile research units to assess physiological markers in underrepresented populations, addressing disparities in developmental science generalizability.

Lakshman Tamilمشاهده پروفایل

استاد

• Quantum Machine Learning
• AI & Machine Learning in Healthcare
• Internet of Things (IoT)
• +۳ مورد دیگر

Dr. Lakshman Tamil is a Full Professor in the Department of Electrical and Computer Engineering at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. in Electrical Engineering from the University of Rhode Island (1989), an M.S. in Mathematics (1989), an M.Tech. in Microwave and Optical Communications Engineering from IIT Kharagpur (1983), and a B.E. in Electronics and Communications Engineering from Madurai Kamaraj University (1981). His research focuses on AI-driven healthcare technologies, including quantum machine learning , telemedicine platforms , wearable health monitoring systems , and IoT applications . Notable projects include automated breast cancer detection via AI mammogram screening , arrhythmia assessment through wearable ECG patches, and low-cost sleep apnea monitoring tools like Apnea MedAssist . His work has been commercialized through ventures like MedCognetics and Cardiocognetics . Research Highlights : - Developed racially unbiased AI systems for mammogram analysis ( RAMS ). - Designed real-time ECG-based heart attack prediction algorithms. - Advanced blockchain solutions for secure medical data sharing. - Authored over 200 publications and holds multiple patents, including U.S. patents for arrhythmia detection and sleep quality assessment. Awards : - Best Teacher Award (2022), UTD. - Fellow, National Academy of Inventors (2019). - Fellow, OPTICA (2009) and The Electromagnetics Academy (2000). Grants & Funding : - Led multi-terabit optical switching projects funded by Yotta Networks ($40M+). - Secured NIH grants for AI-powered healthcare data repositories ( AIM-AHEAD ). - Managed NASA and ONR grants for optical communication and inverse scattering research. Labs & Teams : - Director of the Quality of Life Technology Lab (QoLT) , which pioneers AI-driven healthcare solutions. - Collaborates with industry partners like Spike Technologies and Alcatel on applied research.

Moataz Kilanyمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Machine Learning
• Geographic Information Systems (GIS)
• +۳ مورد دیگر

Moataz Kilany is a Researcher, GIS Developer, and Data Scientist at the Connecticut Transportation Safety Research Center (CTSRC) at the University of Connecticut. He holds a Master's degree in Computer Science from Minia University, Egypt (2018) and a Ph.D. in Geography from the University of Connecticut (2023). His work focuses on remote sensing for urban land cover analysis, machine learning optimization, and the application of advanced algorithms for transportation infrastructure mapping. Since 2019, he has developed GIS applications for the Connecticut Department of Transportation (CTDOT), contributing to integrated roadway asset management systems. **Research Interests:** His research emphasizes optimizing machine learning classifiers using swarm algorithms such as the Elephant Herding Optimization and Lion Optimization Algorithm. Applications span remote sensing (urban land cover classification), healthcare (ECG classification, cardiac arrhythmia detection), and human behavior modeling. His work bridges geospatial data analysis with machine learning for practical solutions in transportation planning and environmental monitoring. **Publications:** His articles reflect a focus on algorithm optimization for classification tasks across domains like environmental science, healthcare, and human behavior. Key themes include hybrid optimization methods (e.g., water wave optimization paired with SVM), and applications in biomedical signal processing (e.g., ECG, EMG analysis). **Grants & Advising:** No formal grant information or advisees are mentioned in the provided text. **Affiliations:** Part of the CTSRC team at UConn, contributing to interdisciplinary projects at the intersection of geospatial technologies and transportation safety research.

• Nonlinear Partial Differential Equations
• Fluid Mechanics
• Mathematical Biology
• +۳ مورد دیگر

Yao Yao is an Associate Professor at the Department of Mathematics, National University of Singapore (NUS), and the Vice Dean of the NUS Graduate School. He holds a Ph.D. in Mathematics from UCLA (2012), with postdoctoral work at the University of Wisconsin-Madison under mentors Alexander Kiselev and Andrej Zlatoš. His research focuses on nonlinear partial differential equations (PDEs) in fluid mechanics and mathematical biology, complemented by recent work in AI applications for healthcare, including disease prediction and medical data analysis. Before joining NUS, he was an Assistant Professor at Georgia Tech's School of Mathematics. Yao Yao serves as an associate editor for several prestigious journals: Analysis and Applications , Kinetics and Related Models , Nonlinear Analysis , Nonlinearity , and SIAM Journal on Mathematical Analysis . His academic contributions span theoretical mathematics and applied AI, with a strong emphasis on interdisciplinary research bridging PDE analysis and healthcare informatics. His work in AI includes explainable deep learning models for Alzheimer’s disease prediction, graph-based patient trajectory modeling, and cybersecurity for medical survival models. He has also explored ontology-driven frameworks for personalized medicine and EHR data analysis. While no awards are explicitly listed, his editorial roles and prolific publication record highlight his influence in mathematical and computational sciences. Yao Yao’s lab focuses on mathematical modeling of biological systems and AI-driven healthcare solutions. His research has applications in cardiology, oncology, and chronic pain management, leveraging both classical PDE theory and modern machine learning techniques.

Evan Museمشاهده پروفایل

استادیار

• Cardiovascular Medicine
• Genomics
• Artificial Intelligence in Healthcare
• +۵ مورد دیگر

Evan Muse, MD, PhD is an Assistant Professor in the Department of Translational Medicine. He holds advanced degrees including a BS in Chemistry from University of North Carolina at Chapel Hill (2000), PhD and MD from Albert Einstein College of Medicine (2008), and M.S. in Clinical and Translational Investigation from The Scripps Research Institute. His research focuses on cardiovascular medicine, genomic diagnostics, and AI-driven healthcare solutions. Key areas include predictive analytics for coronary artery disease, wearable medical technologies, and translational applications of genomics in heart failure and cardiometabolic disorders. Dr. Muse has pioneered work in ambulatory hemodynamic monitoring systems, molecular autopsies for sudden cardiac death, and AI-enhanced diagnostic tools. He leads the HF2 initiative, a multicenter registry advancing heart failure research. His lab develops smartphone-based physiological measurements (e.g., VO2max) and genomic risk assessment platforms like MyGenerank for coronary artery disease. He has received over 12 major awards including the 2018 Department of Medicine Excellence in Research Award and multiple accolades from the American College of Cardiology. His work has been supported by grants focused on digital health innovation, precision medicine, and clinical trial methodologies. Labs/Teams: Director of the Translational Cardiovascular Genomics Lab and co-leader of the Digital Health Innovation Core at [Institution]. Collaborates with global networks in molecular autopsy research and AI-driven diagnostics.

André Ngمشاهده پروفایل

استاد

• Cardiac Electrophysiology
• Atrial Fibrillation
• Cardiac Arrhythmias
• +۳ مورد دیگر

Professor André Ng is the Head of the Department of Cardiovascular Sciences at the University of Leicester and a Consultant Cardiologist/Electrophysiologist at University Hospitals of Leicester. His expertise spans cardiac arrhythmias management, catheter ablation, and innovative medical devices like the LifeMap system. He pioneered robotic AF ablation techniques and has led cross-disciplinary research teams since 2002. Notable awards include the Medical Futures Award 2011 and European Heart Rhythm Association Inventors Award 2016, alongside two international patents. Research focuses on cardiac electrophysiology mechanisms, translating preclinical findings into clinical applications. He collaborates with industry partners on pharmaceutical and device trials as national chief investigator. Professional leadership includes roles in the Royal Colleges, European Society of Cardiology, British Cardiovascular Society (Vice President for Research/Education), and Programme Committee Chair. Key contributions include advancing AF ablation technologies, AI-driven arrhythmia detection, and telemedicine innovations like the AF Virtual Ward. His work addresses disparities in cardiovascular care, particularly during humanitarian crises, and emphasizes gender equity in clinical research participation.

Nikesh Bajajمشاهده پروفایل

مدرس

• Signal Processing
• Machine Learning
• Biomedical Engineering
• +۵ مورد دیگر

Nikesh Bajaj is a Lecturer in Data Science and Director of Education at Queen Mary University of London (QMUL), affiliated with the School of Physical and Chemical Sciences. He holds a PhD from QMUL and the University of Genova, focusing on auditory attention prediction from physiological signals. Previously, he worked as a Research Associate at Imperial College London, exploring cardiac electrophysiology via ECG imaging, and as a Research Fellow at the University of East London on deception detection in financial conversations (resulting in a U.S. patent). His research spans signal processing, machine learning, and biomedical applications, including projects like PhyAAt (auditory attention modeling), ATAR (EEG artifact removal), and computational models for cardiac arrhythmia. He also collaborates with industry on gamification, VR/AR, and forensic interaction analysis. Key Research Areas: ECGI inverse problems, EEG artifact correction, deception detection, and data-driven healthcare. Teaching: Leads courses on data science programming, exploratory data analysis, and machine learning principles at QMUL. Software: Developed Python libraries spkit , phyaat , and pylfsr for signal processing and ML. Publications: Over 20 peer-reviewed papers across biomedical signal processing, machine learning applications, and game design. Recent work emphasizes AI-driven cardiac diagnostics and mixed-reality user experience metrics.

• 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.

Jusak Jusakمشاهده پروفایل

مدرس ارشد

• Internet of Things (IoT)
• Telehealthcare
• Blockchain Technology
• +۳ مورد دیگر

Dr. Jusak Jusak is a Senior Lecturer in Internet of Things (IoT) at the School of Science and Technology, James Cook University Singapore. He holds a PhD in Electrical Engineering from RMIT University and a BEng in Electrical Engineering from Brawijaya University, Indonesia. His research focuses on IoT applications in telehealthcare, blockchain for healthcare IoT, and lightweight machine learning for constrained devices. Dr. Jusak has held academic positions at Dinamika University (Surabaya, Indonesia) and Massey University (New Zealand). He has secured grants including a Cross-Campus Collaboration Grant for blockchain development and a Teaching Innovation Grant for the VetheRBlocks project. He is a Guest Editor for the Electronics journal's special issue on wireless communication security. His research interests include IoT device development for medical signal analysis, secure cloud-based healthcare systems, and hybrid-channel communication for rural healthcare. He has published over 20 peer-reviewed articles and received awards such as the ICITAS 2018 Best Paper Award and a 2022 Futuristic Technology Award for IoT-based water quality monitoring. Dr. Jusak's work emphasizes practical IoT solutions for healthcare challenges, integrating machine learning and blockchain to enhance data security and accessibility in distributed networks.

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.

• Neuroscience
• Mild Cognitive Impairment
• Electrocardiogram
• +۳ مورد دیگر

Ville Mäntynen is a Visiting Professor at the Department of Neuroscience and Biomedical Engineering at Aalto University. His research spans interdisciplinary domains including neuroscience, cardiology, and biomedical signal processing, focusing on advanced non-invasive imaging techniques like magnetoencephalography (MEG) and magnetocardiography (MCG). Research Interests : Neuroscience, Mild Cognitive Impairment, Atrial Fibrillation, Magnetic Field Analysis, Electrocardiogram (ECG), Cardiac Dysrhythmia. Activities : Organized the AI-Mind clinical study (2024), presented at Biomag 2012 (International Conference on Biomagnetism), and contributed to MEG Nord collaborative events (2023). His work integrates computational modeling with clinical applications, particularly in mapping brain and cardiac activity using magnetic fields. Key projects include AI-driven dementia risk estimation and characterization of ECG/MCG sensitivity in realistic anatomical models. Scientific contributions include 17 publications, cited 128 times (Scopus), with a focus on: Machine learning for brain connectivity analysis Cardiac arrhythmia characterization via magnetic fields Biomedical signal processing algorithms He has collaborated internationally, including visiting researcher positions at foreign institutions in 2002 and chairing conferences in 2023.

Raphael Twerenboldمشاهده پروفایل

استاد

• Cardiology
• Oncology
• Neuroscience
• +۴ مورد دیگر

Raphael Twerenbold is a Professor at the Clinic and Polyclinic for Cardiology, University Medical Center Hamburg-Eppendorf. His research spans Cardiology , Oncology , and Neuroscience , with a focus on cardiovascular biomarkers , myocardial infarction , and brain aging . He leads studies in population-based cohorts like the Hamburg City Health Study and collaborates on international trials (GMMG-CONCEPT, DEDICATE-DZHK6). University: University Medical Center Hamburg-Eppendorf School: Medical Faculty Department: Cardiology Academic Rank: Professor Research trends in his 15+ recent articles (2023–2025) include biomarker development for myocardial infarction, brain aging in vascular disease, and occupational risk factors for coronary heart disease. His work integrates epidemiological data with multimodal imaging and machine learning . Notable projects involve the GRACE scores for coronary angiography timing, transcatheter aortic valve trials, and neuroimmune interactions in post-COVID conditions. He contributes to Clinical Research Cardiology and JAMA literature.

• Cardiology
• Biomarkers
• Heart Failure
• +۳ مورد دیگر

Dirk Westermann is a Researcher at the Universitätsklinikum Hamburg-Eppendorf , specifically within the Clinic and Polyclinic for Cardiology . His work focuses on cardiology , with key interests in biomarkers , heart failure , myocardial infarction , and anticoagulation therapy . Research Interests Cardiovascular risk stratification using novel biomarkers Sex-specific differences in cardiac disease Optimization of diagnostic algorithms for acute coronary syndromes Role of immune and inflammatory pathways in myocarditis Outcomes analysis for transcatheter and surgical cardiac interventions Recent Article Trends (2022-2025): His publications examine cardiac biomarkers , anticoagulation strategies post-TAVR, sex-specific diagnostics , and myocarditis pathophysiology , often employing clinical trials , meta-analyses , and inflammatory models . Collaborative Networks include institutions like the German Cancer Research Center (Heidelberg), European Heart Journal editorial teams, and international cardiology consortia.