جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Deep Learning
• Signal and Image Processing
• +۵ مورد دیگر

BOHI Amine is a Lecturer-Researcher at CESI , affiliated with the Engineering and Numerical Tools research team. He holds a PhD in Computer Science from the University of Toulon (2013–2017), focusing on Fourier descriptors inspired by the human visual cortex for maritime surveillance. His academic background includes a Research Master in Computer Science (University of Fez, Morocco, 2009–2011) and a Bachelor's in Mathematics and Computer Science (University of Fez, 2006–2009). Research Interests: Machine Learning, Deep Learning, Signal and Image Processing, 3D Shape Analysis, and applications in Computer Vision and Digital Health. Current projects include developing emotional interaction solutions for neurodegenerative patients (collaboration with VyV3 Bourgogne) and multimodal emotion recognition systems. Education Activities: Teaches Computer Science and Engineering Sciences at various levels, including Integrated Preparatory Cycle, Engineering Cycle, and Specialized Bachelor's Degree. Specializes in Algorithms, Programming, Operations Research, and Web Development. Advising & Collaborations: Supervised over 10 research internships across institutions like the University of Limoges, Marrakech, and Aix-Marseille University. Notable projects include AI-driven facial emotion recognition, multimodal pain assessment, and EEG/ECG monitoring systems on Raspberry Pi. Collaborates with institutions such as the Timone Neuroscience Institute and Institut Fresnel. Publications: Authored 5 peer-reviewed journal articles and 9 conference papers (2017–2024). Key contributions include deep learning approaches for emotion recognition in elderly patients, medical imaging segmentation, and biomechanical modeling of cortical folding patterns.

• Epilepsy
• Neuroimaging
• Neurophysiology
• +۳ مورد دیگر

Armin Nikpour is a Clinical Associate Professor at the University of Sydney's Central Clinical School, Department of Medicine. He is a member of the Brain and Mind Centre, focusing on Neurosciences and Mental Health. His research emphasizes Epilepsy, Neuroimaging, Neurophysiology, and Positron Emission Tomography. Key areas include seizure detection using AI, EEG advancements, and clinical applications of neurotechnology. Publications span 2008–2024, addressing topics like seizure forecasting, EEG electrode improvements, and neuromodulation. He collaborates on studies involving machine learning for cardiac abnormality detection and clinical trial designs for epilepsy treatments. His work integrates neuroimaging techniques with clinical outcomes to improve diagnostic accuracy and patient care. Research also explores psychological aspects of epilepsy, including anxiety management and sleep impacts. He contributes to translational studies, such as developing low-energy biomedical devices and optimizing epilepsy surgery pathways. His interdisciplinary approach bridges clinical practice, engineering, and computational methods.

• Cardiovascular Imaging
• Coronary Artery Disease
• Perivascular Adipose Tissue
• +۳ مورد دیگر

Dr. Evangelos K. Oikonomou is a Researcher at the University of Oxford, affiliated with The Queen's College and the Antoniades Group (The Oxford Translational Cardiovascular Research Group). He holds an MD and DPhil (Oxon) in cardiovascular medicine. His research focuses on developing novel imaging modalities for early detection of coronary inflammation and cardiovascular disease through computational methods, including machine learning and radiotranscriptomics. Key research interests include perivascular adipose tissue (PVAT) interactions with coronary vessels, FAI (Fat Attenuation Index) metrics for risk prediction, and AI-driven analysis of echocardiograms and electrocardiograms. Notable achievements include the ESC Congress Young Investigator Award 2018 for work on FAI in acute coronary syndromes. Publications span high-impact journals like Science Translational Medicine , The Lancet , and European Heart Journal , with contributions to understanding adipose tissue roles in cardiovascular health and AI applications in cardiology. Affiliations: University of Oxford, The Queen's College, Antoniades Group Education: MD, DPhil (Oxford) Awards: ESC Congress Young Investigator Award 2018

• Cardiac Arrhythmias
• Neural Stimulation
• Bioinformatics
• +۴ مورد دیگر

Alain Vinet is an Associate Professor at the Université de Montréal, affiliated with the Faculty of Medicine's Department of Pharmacology and Physiology. His research focuses on cardiac arrhythmias, neural stimulation, and bioinformatics, with expertise in signal modeling and statistical analysis. He is part of the CIUSSS Nord-de-l’Île-de-Montréal and the Centre de recherches mathématiques (CRM). Key research projects include mathematical modeling of cardiac automaticity and collaborative studies on stress and noise effects in tinnitus. He has advised numerous graduate students, including Feng Xiong, Sylvain Bouchard, and Brigitte Maillet. Major awards include the Prix Armand-Frappier (2009) and membership in the Ordre du Canada (2021). Vinet leads projects funded by FRQNT, FRQS, and CRSNG, investigating cardiac electrophysiology and biomedical data analysis. His work bridges clinical cardiology with computational modeling, contributing to arrhythmia prevention and treatment strategies.

Jean-Marie Marionمشاهده پروفایل

مدرس

• Statistics
• Medical Imaging
• Environmental Science
• +۳ مورد دیگر

Jean-Marie Marion is a Lecturer at the University of Angers (UCO), affiliated with the LARIS laboratory (Angevin Research Laboratory in Systems Engineering). He holds a Doctorate in Statistics from University of Paris 6 (1985) and is a member of the French Statistical Society. His research spans statistical methodology applications in biomedicine, environmental science, and education, with a focus on functional data analysis, survival models, and educational statistics. Key projects include developing preprocessing techniques for medical imaging diagnostics and analyzing transcriptomic data for cancer prognosis. Research interests include: (1) Biostatistics and medical imaging analytics, (2) Environmental biomarker analysis in estuaries, (3) Pedagogical approaches to statistical education in social sciences, and (4) Functional data analysis methods. Recent work combines deep learning with medical imaging for disease classification (e.g., lung cancer, head/neck cancers) and explores explainable AI in survival modeling. He has advised PhD students Wissam Abdallah (meteorological forecasting) and Hassan Serhal (ECG signal modeling). His collaborative projects involve跨 disciplinary teams from engineering, oncology, and environmental science. Research outputs include over 30 peer-reviewed articles in journals like Computer Methods and Programs in Biomedicine and Marine Pollution Bulletin . LARIS laboratory membership enables multidisciplinary systems engineering research, integrating statistical methods into healthcare and environmental applications.

• Computer Sciences
• Biomedical Engineering
• Electrical and Electronics Engineering
• +۷ مورد دیگر

Lecturer Furkan Balci is affiliated with the Faculty of Technology at Gazi University, specifically within the Electrical and Electronics Engineering Department. His academic positions include membership in the Department's Research and Development Commission, Infrastructure and Planning Commission, and Internship Committee. He holds a Doctorate from Ankara University (in progress), a Postgraduate degree from Nuh Naci Yazgan University (2023-2025), and multiple other qualifications including an undergraduate degree in Electrical and Electronics Engineering from Erciyes University (2015-2018). His research focuses on interdisciplinary areas such as biomedical engineering, machine learning applications in signal processing, and deep learning for medical diagnostics. Specific interests include EEG-based biometric authentication systems, tumor classification using neural networks, and computer vision for infrastructure monitoring. He has contributed to over a dozen peer-reviewed publications, including SCI-Expanded indexed articles on topics like melanoma detection and hybrid learning models for atrial fibrillation detection. Dr. Balci has led or collaborated on six funded projects with TUBITAK, including initiatives on AI-driven target detection systems, optimization algorithms for electromagnetic launchers, and biometric identification via vein pattern analysis. His teaching activities include Python programming courses for certificate programs since 2021. His work bridges theoretical advancements with practical applications in healthcare technology and engineering systems.

• Biomedical Data Analysis
• Signal Processing
• Machine Learning
• +۸ مورد دیگر

Trygve Christian Eftestøl is a Professor of Information Technology at the Department of Electrical Engineering and Computer Science, University of Stavanger. His academic background includes a PhD in signal processing from NTNU and an M.Sc. in Electrical and Computer Engineering from HiS, Stavanger. He is a senior member of IEEE and serves on the board of the Cognitive Lab at UiS since 2025. Educations: PhD in Signal Processing (NTNU/HiS, 2000) M.Sc. in Electrical and Computer Engineering (HiS, Stavanger) Research Interests: His work focuses on biomedical data analysis, including resuscitation, cardiac science, waveform analysis (ECG, thorax impedance), and MRI for myocardial injury. He is involved in multidisciplinary projects such as digital pathology, newborn resuscitation, sports medicine, neurogenerative diseases, and prostate cancer imaging. He co-founded the Biomedical Data Analysis Laboratory (BMDLab) and serves as its deputy leader since 2020. Articles Trends: Recent publications emphasize machine learning applications in healthcare (e.g., EEG-based neurodegenerative disorder classification, MRI segmentation for myocardial injury), predictive models for cardiac arrest outcomes, and AI-driven solutions in oncology and pathology. His work bridges signal/image processing with clinical needs, addressing challenges in resuscitation, cardiology, and diagnostic accuracy. Awards/Grants: No specific awards listed, but his leadership roles and research contributions highlight sustained academic and clinical impact. Advising & Labs: Supervises/co-supervises PhD projects in areas like human activity recognition and prostate cancer detection. Active in BMDLab, collaborating nationally and internationally on biomedical data analysis.

Terry Lyonsمشاهده پروفایل

استاد

• Stochastic Analysis
• Machine Learning
• Financial Mathematics
• +۴ مورد دیگر

Prof. Terry Lyons is the Wallis Professor of Mathematics at the University of Oxford's Mathematical Institute. Previously, he held professorships at Imperial College London and the University of Edinburgh. He has directed major institutions like the Oxford Man Institute of Quantitative Finance and the Wales Institute of Mathematical and Computational Sciences. His research focuses on stochastic analysis, rough paths theory, and their applications in finance, data science, and environmental systems. He actively bridges pure mathematics with applied domains, emphasizing rigorous proofs and algorithmic innovation. Education & Positions: Colin Maclaurin Chair at Edinburgh (1985–93), Professor at Imperial College (1993–2000), Wallis Professor at Oxford since 2000. Awards: Fellowships from the Royal Society (FRS), Royal Society of Edinburgh (FRSE), and Learned Society of Wales (FLSW). His research explores stochastic processes and their mathematical foundations, with recent work applying rough paths to machine learning, environmental monitoring (e.g., NO₂ sensing via CCTV), and complex data analysis. Key contributions include signature methods for data representation and algorithms for high-dimensional systems. His work often intersects geometric measure theory and numerical methods, addressing challenges in randomness and approximation. Publications span theoretical advances (e.g., Lipschitz theorems, path signatures) and applied solutions (e.g., medical signal reconstruction, environmental modeling). His lab’s tools like RoughPy and Sig-networks exemplify software implementations of his mathematical innovations.

Dhaval Solankiمشاهده پروفایل

استادیار

• Wearable Devices
• Biomedical Signal Processing
• Embedded Systems
• +۴ مورد دیگر

Dhaval Solanki serves as an Assistant Professor in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island, where he co-directs the Wearable Biosensing Lab. His work bridges engineering innovation with healthcare applications, focusing on real-world implementation of accessible medical technologies. His academic foundation includes: Ph.D. in Electrical Engineering from Indian Institute of Technology Gandhinagar (2020) M.Tech. in Electrical Engineering from Indian Institute of Technology Gandhinagar (2015) B.E. in Electronics and Communication Engineering from Gujarat Technological University (2013) Solanki's research centers on wearable health technologies that transform medical electronics into practical solutions. He develops smart textiles, biosignal processing systems, and embedded devices for continuous health monitoring, with emphasis on shifting healthcare from reactive to proactive personalized care. His human-centered design approach integrates engineering precision with clinical needs across neurological rehabilitation and chronic disease management. Analysis of his 15 most recent publications reveals dominant trends in wearable biosensing for neurological disorders (Parkinson's, stroke, ADHD, epilepsy), with significant focus on textile-based physiological monitoring systems and virtual reality rehabilitation platforms. His work consistently addresses real-world challenges like motion artifacts in clinical settings and usability for diverse patient populations. As an educator, Solanki mentors students in the Wearable Biosensing Lab while teaching electrical and biomedical engineering courses. His research is supported by federal and foundation grants targeting healthcare technology innovation, with recent projects spanning neonatal monitoring, geriatric care, and digital interventions for substance use disorders.

Ioan Clejuمشاهده پروفایل

مدرس

• Coding Theory
• Digital Systems Design
• Computer-Aided Error-Correcting Code Design
• +۷ مورد دیگر

Ioan Cleju is a Lecturer at the Technical University of Iasi , affiliated with the Faculty of Electronics and Telecommunications and the Department of Logic Circuits . His research spans coding theory, digital systems design, and error-correcting codes with computer-aided methods, while later works focus on AI-driven signal processing for ECG biometrics and smart traffic systems. Email: icleju@etc.tuiasi.ro Research Interests include: Computer-Aided Design of Error-Correcting Codes Multi-Distance and Cyclic Code Design PLD Circuit Applications in Coding Theory AI-Driven Sparse Signal Recovery ECG Biometric Authentication Smart Urban Mobility Systems Recent Publications highlight trends in: Machine learning for cybersecurity and traffic management ECG signal analysis using convolutional networks Dictionary learning for anomaly detection Historical document digitization with symbolic AI Multi-lane vehicle flocking algorithms

Joshua Barriosمشاهده پروفایل

استادیار

• Machine Learning in Healthcare
• Cardiology
• Clinical Medicine
• +۳ مورد دیگر

Dr. Joshua Barrios is an Assistant Professor in the Department of Medicine at the University of California, San Francisco (UCSF), School of Medicine. He holds a Ph.D. in Neuroscience from the University of Utah (2019), focusing on dopaminergic modulation of sensorimotor behavior. His research integrates computational neuroscience, systems neuroscience, and advanced machine learning to develop clinical tools for disease detection and diagnostic enhancement. He collaborates closely with Dr. Geoff Tison in the Tison Lab, focusing on deep learning systems for cardiovascular applications, such as automated disease detection from ECGs, echocardiograms, and other clinical data. Key research interests include improving early disease detection for conditions like mitral valve prolapse, hypertrophic cardiomyopathy, and pulmonary hypertension. Dr. Barrios mentors students and fellows in UCSF’s collaborative environment, leveraging access to clinical data and computational resources. His work emphasizes enhancing healthcare sensitivity and preventative care efficacy through AI-driven innovations. Recent publications highlight advancements in automated ECG analysis, cardiac imaging interpretation, and AI applications for arrhythmia monitoring. Despite no listed awards, his contributions to AI in cardiology have been widely discussed in medical and tech communities, with collaborations across institutions like UCSF, University of Utah, and international teams. His lab’s work aims to bridge engineering and clinical practice, addressing unmet needs in diagnostics and patient care.

Ersoy Subasiمشاهده پروفایل

دانشیار

• Stochastic Programming
• Financial Mathematics
• Data Mining
• +۵ مورد دیگر

Ersoy Subasi is an Associate Professor and Doctoral Program Coordinator at the College of Aeronautics, Florida Institute of Technology. His research focuses on stochastic programming, financial mathematics, and data mining, with interdisciplinary applications in biomedical engineering, genomics, and quantum computing. He emphasizes collaborative research and industry partnerships to solve complex real-world challenges. Subasi has published extensively in journals and conference proceedings, addressing topics like blood pressure monitoring, chronic kidney disease analysis, and machine learning-driven risk estimation. His work integrates mathematical rigor with computational methods, spanning optimization theory, genomic data interpretation, and quantum technologies. Despite his prolific output, there are no listed academic awards or formal advisees in the provided profile. Subasi’s contributions highlight the intersection of operations research with healthcare technology and financial modeling. Key areas of exploration include: Non-invasive biomedical monitoring systems High-dimensional genomic data analysis Quantum-based algorithms for random number generation Discrete moment problems with shape constraints He is accessible at esubasi@fit.edu and maintains active profiles on ORCID and Google Scholar.

Francis Haasمشاهده پروفایل

دانشیار

• Additive Manufacturing
• Thermal-Fluid Sciences
• Sustainable Materials
• +۳ مورد دیگر

Francis (Mac) Haas is an Associate Professor in Mechanical Engineering at Henry M. Rowan College of Engineering, Rowan University, with affiliated appointments in Chemical Engineering. His research focuses on thermal-fluid sciences applied to advanced manufacturing and biomedical devices, particularly cold spray additive manufacturing of high-performance polymers and composites. Research expertise spans: Thermal-Fluid Sciences Chemically Reacting Flows Gas Phase Fuel Chemistry Additive Manufacturing Process Safety Impactful areas include Health, Environment, Sustainability, and Renewable Energy. Professional memberships include ACS, ASEE, ASME, and The Combustion Institute.

Hassan Chizariمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Randomness Extraction
• Cryptography
• +۵ مورد دیگر

Hassan Chizari is a Senior Lecturer and Academic Course Leader for the MSc Cyber Security program at the University of Gloucestershire. He leads the Cisco Network Academy and manages the Cisco Lab, teaching networking and advanced cybersecurity topics. His research focuses on randomness extraction in physiological signals for cryptographic applications, funded by InnovateUK. He has published over 40 peer-reviewed articles and supervised 20+ postgraduate students. Chizari holds a BSc in Computer Hardware (Shiraz University, 1999), MSc in Artificial Intelligence and Robotics (Shiraz University, 2003), and PhD in Networking (Universiti Teknologi Malaysia, 2011). His work has earned awards including Gold Medals at Malaysia Technology Expo (2016) and Malaysian Research Conference (2015), and Bronze Medals for innovations like Indoor Air Quality Monitoring and Smart Environment Monitoring. His research bridges cybersecurity, bio-inspired algorithms, and IoT infrastructure. Recent work includes cryptographic key extraction from ECG signals and energy-efficient cloud resource allocation using nature-inspired techniques. He collaborates with Crossword Company to commercialize physiological signal-based security solutions. His teaching spans undergraduate to postgraduate levels, emphasizing practical lab work and industry-relevant skills. Awards and recognitions include multiple medals for innovations in environmental monitoring and sensor networks. His grants include InnovateUK funding for medical device security. Chizari’s advising focuses on cybersecurity, networking, and bio-inspired systems, contributing to over 20 completed theses. He actively explores green computing, wireless sensor networks, and resilient critical infrastructure frameworks. Chizari’s research group develops secure IoT applications and trustworthy smart systems, addressing challenges in data privacy, anomaly detection, and energy efficiency. Current projects aim to integrate bio-signals into cryptographic protocols and optimize cloud/fog computing infrastructure for sustainability.

• Affective Neurosciences
• Brain-Heart Interactions
• Virtual Reality
• +۳ مورد دیگر

Antonin Fourcade is a Doctoral Researcher affiliated with Charité Universitätsmedizin Berlin and the Max Planck Institute for Human Cognitive and Brain Sciences (MPI-CBS) in Leipzig. His research focuses on multimodal affective computing within naturalistic settings, particularly leveraging virtual reality (VR), EEG/ECG technologies, and computational tools like Python and MATLAB. He is part of the Neurology/Mind-Body-Emotion group and the Max-Planck School of Cognition. Education: 2007-2010: Biomedical Engineering diploma (PHELMA, France) 2016-2019: Master in Social Affective and Cognitive Neuroscience (Freie Universität Berlin) 2019-present: Doctoral program at the Max-Planck School of Cognition His research interests span affective neuroscience, brain-heart interactions, and the application of immersive VR for studying emotional experiences. Recent work emphasizes real-time affect tracking in VR environments and interoceptive alterations in psychiatric disorders like schizophrenia. Advisors: Dr. Michael Gaebler and Prof. Arno Villringer. Collaborations include MPI-CBS (Leipzig) and the Max Planck Institute for Dynamics and Self-Organization (MPDCC, Berlin). Labs/Teams: Active in the Mind-Body-Emotion research group at MPI-CBS, focusing on translational neuroscience and neurotechnology integration.