جست‌وجوی استاد و پژوهشگر

• Signal Processing
• Image Processing
• Smart Environments
• +۶ مورد دیگر

Manuel Reis is an Associate Professor with Agregação at the University of Trás-os-Montes e Alto Douro (UTAD), Portugal, in the Electrical Engineering Department. His research focuses on signal/image processing, smart environment systems, and multimedia education technologies. He is affiliated with the Institute of Electronics and Telematics Engineering of Aveiro (IEETA) and collaborates with Brazil's IFPA-Santarém in computing education research. Education: Bachelor's in Electrical Engineering (University of Trás-os-Montes e Alto Douro, 1991) Master's in Electronics and Telecommunications (University of Aveiro, 1996) PhD in Electrical Engineering (University of Aveiro, 2001) Research Interests: Signal & Image Processing Cybersecurity for IoT and Smart Environments Education Technology (e-learning, multimedia tools) 5G and Edge Computing Applications Artificial Intelligence in Agriculture and Healthcare Recent articles highlight his work on cybersecurity in connected vehicles, federated learning for IoT security, and IoT-based systems for drowsiness detection. He has contributed to projects involving smart city infrastructure, sustainable waste management, and low-cost biomedical devices. His educational research explores e-learning frameworks, student engagement metrics, and innovative teaching methodologies in engineering education. Lab/Affiliations: Institute of Electronics and Telematics Engineering of Aveiro (IEETA) Multidisciplinary Research Group IFPA-Santarém-Brazil (Computing in Education)

Yichuang Sunمشاهده پروفایل

استاد

• Wireless Communications
• 5G/6G Technologies
• RF Circuits and Systems
• +۷ مورد دیگر

Yichuang Sun is a Professor of Communications and Electronics at the University of Hertfordshire, leading the Communications and Intelligent Systems Research Group and the Electrical and Electronic Engineering Department. He holds a PhD from the University of York (1996) and has over 450 peer-reviewed publications, 5 authored books, and 40+ supervised PhD students. His research focuses on wireless communications (5G/6G, RIS-aided systems), RF/microwave circuits, neuromorphic computing, and machine learning applications. He serves as Editor or Guest Editor for 12+ IEEE/IET journals and chairs technical committees for IEEE conferences. Notably ranked in the World's Top 2% Scientists since 2019, he also advises global institutions like Oxford and the Royal Academy of Engineering. Education: PhD in Electronics, University of York, UK (1996) Research Interests: Wireless/Mobile Communications and Networks RF and Microelectronic Circuits Neuromorphic Computing and Machine Learning Memristor-based Systems Secure and Energy-Efficient Communications Grants & Projects (selected): Advanced mmWave MIMO 4D Radar for Traffic Detection (2024–2027) Wireless Power Transfer for Implantable Medical Devices (2023–2025) RFID System for Sample Management (2021–present) Labs/Teams: Leads the Communications and Intelligent Systems Research Group, collaborating on 6G, neuromorphic hardware, and IoT systems.

• Biomedical Engineering
• Nanotechnology
• Microelectronics
• +۵ مورد دیگر

Professor Omid Kavehei is a Professor of Intelligent Microsystems in the Faculty of Engineering at the University of Sydney, serving as Deputy Head of the School of Biomedical Engineering. Previously, he held roles as a Research Fellow at the University of Melbourne and Lecturer at RMIT University. His research focuses on biomedical microsystems, nanotechnology, and brain-inspired hardware for healthcare applications, particularly epilepsy monitoring and seizure prediction. Education: PhD (specific institution not explicitly stated) His work bridges nanoelectronics and healthcare, aiming to develop low-power, brain-inspired devices for sensory perception and medical diagnostics. Key interests include neuromorphic engineering, wearable sensors, and AI-driven medical systems. Awards include the Ramaciotti Biomedical Research Award (2021), Microsoft AI for Accessibility Grant (2019), and multiple teaching/research excellence awards from the University of Sydney. His recent work explores neuromorphic cytometry for cell analysis, edge AI for ECG/EEG diagnostics, and closed-loop neurostimulation systems. Collaborations include the Sydney Nano Institute, Brain and Mind Centre, and industry partners like Microsoft. Current research students are advancing topics like bio-inspired algorithms for seizure detection, hardware-friendly machine learning models, and flexible sensor systems for aquatic environments. Grants include funding for neurophysiology platforms, quantum sensors, and semiconductor design. Labs/teams: Involved in the Centre for Drug Discovery Innovation and collaborations across engineering, neuroscience, and clinical domains.

Francesca Dalia Faraciمشاهده پروفایل

مدرس ارشد

• Biomedical Signal Processing
• Medical Data Analysis
• Wearable Technology
• +۵ مورد دیگر

Francesca Dalia Faraci is a Senior Lecturer and Researcher at the Department of Innovative Technologies (DTI) of SUPSI. She holds a PhD in Electronics from the University of York (UK) and a Laurea in Physics from the University of Genoa (Italy). Her research focuses on biomedical signal processing, statistical data analysis, and wearable technology applications in healthcare. She leads projects such as Cardio MIPA (arrhythmia monitoring) and AutoPlay_DD (child development analysis via smart toys). Key contributions include developing algorithms for sleep staging, arrhythmia detection, and synthetic ECG generation. Awards include the GOSPEL and DoE School Certificates. She coordinates research projects and teaches courses in Physics and Applied Statistics. Education: PhD in Electronics (University of York, 2006), Laurea in Physics (University of Genoa, 2002) Roles: Senior Lecturer, Researcher, Project Coordinator Key Projects: Tackling teleWorking Impacts, my Doctor's Lifestyle (lifestyle medicine), Telemonitoring Breast Cancer Her research bridges AI and clinical practice, emphasizing ethical frameworks and complementarity between clinicians and AI systems. Recent work includes causal inference for sleep dynamics and bias quantification in healthcare algorithms.

Zhiyi Huangمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithmic Game Theory
• Online Algorithms
• +۳ مورد دیگر

Zhiyi Huang is an Associate Professor of Computer Science at the University of Hong Kong, leading the Computer Science Division within the School of Computing and Data Science. He holds a PhD from the University of Pennsylvania (2013) and completed a postdoctoral fellowship at Stanford University (2013–2014). His research focuses on Theoretical Computer Science, Algorithmic Game Theory, Online Algorithms, and Differential Privacy, with notable contributions to Machine Learning and Computer Networks. Education: PhD in Computer and Information Science, University of Pennsylvania (2013) Postdoctoral Researcher, Stanford University (2013–2014) Bachelor's Degree from the Yao Class at Tsinghua University (2008) Research interests span foundational areas including algorithmic game theory, online optimization, and privacy-preserving mechanisms. He has pioneered work on revenue maximization in single-parameter settings and developed novel frameworks for analyzing price of anarchy in game theory. Key Awards: Early Career Award (Research Grant Council of Hong Kong, 2014) Best Paper Award at ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2015) Morris and Dorothy Rubinoff Dissertation Award (2013) Simons Graduate Fellowship in Theoretical Computer Science (2012–2013) Recent grants include studies on algorithmic foundations of Bayesian mechanism design (HK$675,647), online primal dual techniques (HK$496,028), and privacy-preserving mechanisms (HK$931,737). His work bridges theoretical advancements with practical applications in healthcare, autonomous systems, and cybersecurity. Notable Projects: Medical predictive systems for acute cardiopulmonary events AI-driven maritime navigation using AIS data Secure federated learning frameworks with blockchain

Kelly Nashمشاهده پروفایل

استاد

• Biophysics
• Nanomaterials
• Biophotonics
• +۷ مورد دیگر

Kelly Nash is a Professor in the Department of Physics and Astronomy at the University of Texas at San Antonio (UTSA), within the College of Sciences. She also serves as the Vice Provost for Faculty Success, a leadership role reflecting her commitment to academic excellence and faculty development. Previously, she was Associate Dean for Faculty Success (2019–2023) and Director of the Kleberg Advanced Microscopy Center (2018–2020). Education: Ph.D. in Physics, University of Texas at San Antonio M.S. in Applied Physics, University of Michigan B.S. in Physics, Dillard University Prof. Nash's research lies at the intersection of physics, materials science, and biology. Her work focuses on functional nanomaterials , particularly their synthesis, optical properties, and interactions with biological systems. Key areas include biophotonics , nanoparticle synthesis via laser ablation , photoacoustic sensing , and nanotransducers for biomedical applications . Her lab develops nanoparticles for photodynamic therapy, on-demand drug release, antimicrobial treatments, and environmental sensing. Her recent publications reveal a strong trend in leveraging light-matter interactions for biomedical diagnostics and therapy. She pioneers all-optical photoacoustic techniques that eliminate bulky transducers, enabling high-resolution, high-frequency detection. Her work spans fundamental biophysics to translational applications, with materials including gold, selenium, and dichalcogenide nanoparticles. Scientific Awards and Recognition: Air Force Office of Scientific Research Young Investigator Program Award APS Women Physicist of the Month (July 2016) Dillard University Alumni 40–Under-40 San Antonio Business Journal 40-Under-40 UTSA President’s Distinguished Diversity Award UTSA COS Excellence in Community Service Award Prof. Nash is a dedicated mentor and advocate for diversity in STEM. She has served as UTSA-APS Bridge Program Coordinator and chair of the APS Conferences for Undergraduate Women in Physics National Organizing Committee. Her lab, the Functional Nanomaterials Laboratory , receives funding from AFOSR, NIH, NSF, DOE, and NNSA. She is actively involved in grants focused on biophysics, antimicrobial nanomaterials, and STEM education. She welcomes graduate and undergraduate students interested in nano-bio interfaces and translational research. The lab collaborates extensively with institutions such as the University of Texas Health Science Center, Southwest Research Institute, AFRL, and international partners, forming a robust interdisciplinary research network.

• artificial intelligence
• image analysis
• deep learning
• +۸ مورد دیگر

Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.

Martin Brookeمشاهده پروفایل

دانشیار

• Analog Circuits
• RF Circuits
• Optoelectronic Circuits
• +۴ مورد دیگر

Martin Brooke is an Associate Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. He earned his B.E. in Electrical Engineering (First Class Honors) from Auckland University, New Zealand (1981), followed by M.S. (1984) and Ph.D. (1988) degrees from the University of Southern California. His career includes positions at Georgia Institute of Technology (1988-2003) before joining Duke. Dr. Brooke's research spans analog/RF/optoelectronic circuits, sensor interfaces, and deployable sensor systems with applications in ocean engineering and biomedical imaging. He leads innovative projects including ocean pH monitoring sensors and X Prize seafloor mapping initiatives, focusing on solving 'open-ended problems' through interdisciplinary approaches combining engineering with marine science. His extensive publication record (160+ articles) demonstrates consistent focus on sensor technologies, integrated circuits, and engineering education. Recent works emphasize biomedical applications (cancer margin assessment), environmental monitoring (ocean sensors), and educational innovations (remote microelectronics labs), showing a trend toward multidisciplinary solutions for real-world challenges. Awards and Honors: Capers and Marion McDonald Award for Teaching/Research Excellence (2022) Georgia Tech Outstanding Thesis Advisor Award (2003) IEEE Midwest Symposium Best Paper Award (1992) NSF Research Initiation Award (1990) Analog Devices Career Development Award (1988-1993) He has graduated 23 PhD students and mentors teams for major challenges like the X Prize ocean robotics competition. His research group develops deployable sensor systems with funding from NSF, X Prize Foundation, and industry partners. Current projects include drone-based ocean floor mapping systems and advanced pH sensors for marine ecosystem monitoring. Dr. Brooke leads the Brooke Research Group focusing on analog/RF systems and sensor integration. The team collaborates with Duke Marine Lab on ocean engineering initiatives and maintains eight U.S. patents. Future work emphasizes scalable sensor networks for environmental monitoring and biomedical diagnostics.

Thomas C. Richمشاهده پروفایل

استاد

• Cell Signaling
• cAMP Signaling
• Biomedical Imaging
• +۵ مورد دیگر

Thomas C. Rich serves as Professor of Pharmacology and Director of the Bioimaging Core Facility at the University of South Alabama's Frederick P. Whiddon College of Medicine, where he leads innovative research in cellular signaling dynamics and advanced imaging technologies. His educational background includes: Baccalaureate with Honors in Engineering from Georgia Institute of Technology Masters in Aerospace Engineering from Georgia Institute of Technology Ph.D. in Biomedical Engineering from Vanderbilt University Dr. Rich's research focuses on cellular signaling specificity , particularly cAMP pathways and phosphodiesterase regulation . His laboratory pioneered single-cell cAMP sensors and excitation-scanning hyperspectral imaging (HSI) techniques enabling 100-fold signal-to-noise improvements over traditional FRET. Key contributions include mapping cAMP gradients in pulmonary endothelial cells and airway smooth muscle, revealing previously undetectable signaling microdomains through collaborations with Dr. Silas Leavesley and Dr. Michael Francis. Analysis of his 14 publications (2015-2024) shows consistent innovation in quantitative imaging and signal transduction , with increasing emphasis on multi-parametric measurement (cAMP, Ca2+, NO, cGMP) and real-time dynamic tracking . His work spans fundamental enzymology to clinical applications, particularly in respiratory physiology and endoscopic technology development. No scientific awards were mentioned in the provided documentation. While specific advisees and grant details are not documented, Dr. Rich's research program demonstrates extensive collaboration through co-authorship patterns and facility leadership. His Bioimaging Core Facility serves as a hub for interdisciplinary projects requiring advanced fluorescence measurement capabilities. Dr. Rich leads the Bioimaging Core Facility in a collaborative research ecosystem centered around hyperspectral imaging development. His team works closely with Dr. Leavesley on optical system engineering and Dr. Francis on dynamic region-of-interest algorithms, creating an integrated approach to overcome limitations in cellular signal measurement within the 'turbulent maelstrom of the cellular environment'.

Avery Bermanمشاهده پروفایل

استادیار

• Medical Physics
• Functional Neuroimaging
• Biomedical Engineering
• +۵ مورد دیگر

Avery Berman is an Assistant Professor in the Department of Physics at Carleton University and a Scientist at the University of Ottawa Institute of Mental Health Research (IMHR) at The Royal. His work focuses on advancing functional MRI (fMRI) techniques for high-resolution imaging of brain activity and physiology, with applications in neuroscience and mental health disorders. PhD in Biomedical Engineering and MSc in Medical Radiation Physics from McGill University Postdoctoral research at Harvard Medical School and the Martinos Center for Biomedical Imaging Research Interests include: High-resolution fMRI at 7 Tesla Biophysical modeling of vascular networks Quantitative biomarkers for oxygen metabolism PET-MRI hybrid imaging systems Neurovascular coupling in mental illness Scientific Awards include: NSERC Canada Graduate Scholarship (Master's) CIHR Canada Graduate Scholarship (Doctoral) CIHR Postdoctoral Fellowship (top 10/600 applicants) NSERC Postdoctoral Fellowship (top Physics section recipient) Research Funding from NSERC, CFI, and institutional support from Carleton University and IMHR. His lab develops the open-source BOLDsωimsuite software for fMRI signal modeling and collaborates with Canada-wide vascular training programs.

• Metabolic Health
• Skeletal Muscle Physiology
• Alcohol Misuse
• +۵ مورد دیگر

Danielle Levitt, PhD, CSCS*D, serves as an Assistant Professor in the Department of Kinesiology & Sport Management at Texas Tech University and is actively affiliated with the Institute for One Health Innovation. Her research program focuses on metabolic health, investigating how lifestyle factors contribute to dysfunction in health and disease while developing targeted intervention strategies to improve clinical outcomes. Dr. Levitt's core research interests include: Skeletal muscle physiology and bioenergetics Alcohol misuse and its metabolic consequences Glycemic control mechanisms Inter-organ crosstalk in disease states Cardiometabolic risk in vulnerable populations (e.g., people living with HIV, first responders) Exercise physiology interventions Analysis of her recent publications reveals dominant themes in alcohol-pathogen interactions, skeletal muscle regeneration under metabolic stress, and cardiometabolic risk modulation. Her work frequently employs SIV-infected macaque models to study HIV-alcohol comorbidities, explores mTOR signaling pathways in muscle adaptation, and investigates dietary impacts on viral pathogenesis including SARS-CoV-2 susceptibility. Methodologically, she integrates molecular biology, exercise physiology, and epidemiological approaches across diverse populations. No scientific awards were mentioned in the provided documentation. Information regarding Dr. Levitt's graduate student advising, grant funding history, or specific laboratory resources was not included in the source materials. Her research appears to operate through collaborative frameworks within the Institute for One Health Innovation. Dr. Levitt maintains active research operations at Texas Tech University's Institute for One Health Innovation, where she contributes to interdisciplinary initiatives addressing complex health challenges at the human-animal-environment interface through metabolic and physiological research.

Dawen Caiمشاهده پروفایل

دانشیار

• Neuroscience
• Developmental Biology
• Molecular Biology
• +۳ مورد دیگر

Dawen Cai, Ph.D., is an Associate Professor at the University of Michigan Medical School in the Department of Cell and Developmental Biology , with a secondary affiliation in the Biophysics Department under the College of Literature, Science, and the Arts (LS&A). He is also affiliated with the Neuroscience Graduate Program at the Medical School. His research focuses on integrating computational and experimental approaches to study neuronal subtype determination using scRNA-seq and in situ analysis. His research explores the intersection of RNA biology, neuroscience, and bioinformatics. He develops tools for multispectral imaging and lineage tracing to decode neural development and connectivity in Drosophila and mammalian models. His work combines single-cell transcriptomics with advanced microscopy to identify marker genes and model neuronal architecture. The articles reflect a strong interdisciplinary focus on neuroscience and biomedical imaging. Recent publications highlight innovations in 3D imaging technologies, image compression algorithms, and machine learning applications for medical image segmentation. These works emphasize scalable solutions for high-resolution data analysis, advancing tools for neurophysiology, and leveraging RNA sequencing to map neural development. No scientific awards were explicitly mentioned in the text. Dawen Cai actively recruits PhD students and postdoctoral fellows for the Cai Lab, prioritizing candidates with wet-lab skills, bioinformatics expertise, and experience in quantitative image processing. His lab emphasizes training in interdisciplinary research, paper/grant writing, and critical thinking.

Joy Hirschمشاهده پروفایل

استاد

• Neuroscience
• Social Neuroscience
• Neuroimaging
• +۶ مورد دیگر

Joy Hirsch is a Professor of Psychiatry, Comparative Medicine, and Neuroscience at the Yale School of Medicine. She directs the Brain Function Laboratory, which focuses on the neural mechanisms of social interactions using functional near-infrared spectroscopy (fNIRS). Her work bridges single-brain and multi-brain neuroimaging to explore dynamic social behaviors under natural conditions. Education: PhD, Columbia University (1977) MA, Portland State University (1970) BS, University of Oregon (1967) Research Interests: Dr. Hirsch investigates the 'neuroscience of two,' emphasizing cross-brain neural synchrony and the 'interactive brain hypothesis.' Her lab develops multimodal technologies to study face-to-face interactions, focusing on conflict, cooperation, nonverbal cues, and social anxiety. Key themes include autism research, emotional contagion, and the impact of virtual communication on brain activity. Scientific Trends: Her recent articles highlight applications of fNIRS in psychiatric disorders, machine learning for neural diagnostics, and comparative studies of in-person vs. virtual interactions. Collaborations span interdisciplinary domains, including cognitive modeling, neuroimaging, and behavioral analysis. Scientific Awards: George Gamow Science Award (2009) Leah M Lowenstein Teaching Award (1990) Advising and Grants: Dr. Hirsch mentors students in social neuroscience and neuroimaging. She leads clinical trials, including Neural Mechanisms for Social Interactions and Eye Contact in ASD (HIC ID 1512016895), with primary completion in 2026. Lab and Teams: The Brain Function Laboratory, established at Yale in 2013, continues her legacy from Columbia's fMRI Research Center. The lab explores ecological validity in social neuroscience using head-mounted cameras, physiological sensors, and multimodal data synchronization.

• Terahertz Science
• Biomedical Engineering
• Medical Imaging
• +۳ مورد دیگر

Zachary Taylor is an Associate Professor at Aalto University's School of Electrical Engineering, specializing in terahertz science and biomedical engineering. His research focuses on advanced imaging and spectroscopy techniques for medical diagnostics. Specialized in terahertz and microwave-optical systems Recipient of Aalto University Doctoral Thesis Award Active in IEEE Transactions and IRMMW-THz conferences His work spans innovative technologies like Gaussian beam analysis for corneal sensing, quasioptical calibration methods, and frequency diversity applications in holography. Recent publications highlight his contributions to radiation oncology predictive modeling and millimeter-wave measurement systems. Key research domains include: Terahertz biomedical imaging Microwave-optical component integration Computational electromagnetic modeling MRI-guided radiotherapy prediction Reflective optical system design Precision calibration techniques Scientific Recognition: Aalto University School of Electrical Engineering Doctoral Thesis Award Prof. Taylor leads the Zachary Taylor Group, advising multidisciplinary researchers across terahertz science, medical physics, and computational modeling. His team's recent work demonstrates cross-domain applications from corneal diagnostics to cancer treatment prediction.

Hala Lamdouarمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Hala Lamdouar is a Research Fellow at the University of Oxford , specifically affiliated with the Institute of Biomedical Engineering (IBME). She works under the supervision of Professor Alison Noble and completed her DPhil (PhD) at Oxford's Visual Geometry Group (VGG), advised by Professor Andrew Zisserman and Professor Weidi Xie . Her academic journey began with an Engineering degree in signal and image processing from ENSEIRB-MATMECA , Bordeaux, France, followed by a Master's in applied mathematics focusing on machine learning and computer vision at Ecole Normale Superieure , Paris, where she also worked on autonomous driving perception solutions for Valeo . Education Engineering degree, ENSEIRB-MATMECA, Bordeaux MSc in Applied Mathematics (MVA), Ecole Normale Superieure DPhil (PhD), Visual Geometry Group, University of Oxford Lamdouar's research centers on Video Understanding through single and multi-modal learning, particularly in clinical settings and challenging datasets. Key contributions include motion segmentation techniques for detecting camouflaged objects, the creation of the MoCA dataset (Moving Camouflaged Animals), and scalable synthetic data pipelines for motion-based object segmentation. Her work combines ConvNets, Transformers, and optical flow analysis to address partial occlusion and motion absence in videos. Notable achievements include the Best Paper Award at the CVPR Workshop on Robust Video Scene Understanding (2021). She has published in top conferences like BMVC (2021), ICCV (2021), and ACCV (2020), with applications in biomedical imaging, autonomous systems, and unsupervised learning. Additional affiliations include the Centre for Doctoral Training in Autonomous Intelligent Machines & Systems (AIMS).