جست‌وجوی استاد و پژوهشگر

• Biomedical imaging
• Lasers and optical physics
• Optical microscopy
• +۳ مورد دیگر

Haohua Tu is a Research Assistant Professor at the University of Illinois, affiliated with the Department of Electrical and Computer Engineering within the College of Engineering. His research focuses on biomedical imaging and lasers/optical physics, particularly advancing multiphoton microscopy and nonlinear optical technologies for medical diagnostics and biomarker discovery. He develops innovative imaging systems like SCIFI (Supercontinuum Intrinsic Fluorescence Imaging) and SLAM (Simultaneous Label-Free Autofluorescence Multiharmonic) microscopy, aiming to commercialize label-free imaging solutions. Dr. Tu's work integrates optical engineering with biomedical applications, including real-time histopathology, cancer microenvironment analysis, and photodamage monitoring. His contributions span supercontinuum generation in fiber optics, computational photon counting techniques, and deep learning-based image correction. His research bridges fundamental photonics with clinical needs, addressing challenges in early cancer detection, treatment response assessment, and intraoperative diagnostics. Key technical areas include femtosecond laser systems, adaptive optics, and multimodal imaging approaches. His studies often involve collaborations in biomedicine and oncology, targeting applications such as breast cancer prognosis, pancreatic tumor monitoring, and urinary extracellular vesicle detection. While no specific grants or awards are mentioned, his prolific publication record reflects sustained innovation in optical imaging technologies.

Michalis Zervasمشاهده پروفایل

استاد

• Optical Communications
• Photonics
• Fibre Lasers
• +۳ مورد دیگر

Professor Michalis Zervas serves as Professor of Optical Communications at the University of Southampton's Optoelectronics Research Centre (ORC), leading pioneering research in photonics and laser technologies. His work integrates advanced optical systems with artificial intelligence to solve complex challenges in telecommunications, manufacturing, and medical diagnostics through major collaborations with industry and international research bodies. His primary research spans Optical Communications, Photonics, and Fibre Lasers, with specialized focus on deep learning applications for laser control optimization, coherent beam combination, and optical fibre sensor development. Current investigations include high-power photonics systems for industrial manufacturing and novel laser-based biomedical diagnostic platforms that bridge physics with healthcare innovation. Recent publications (2025) reveal a decisive trend toward AI-photonic integration, where deep learning algorithms enhance precision in laser-material interactions across diverse applications—from microbead cleaning and paint analysis to psoriasis treatment simulation and diatom imaging. This interdisciplinary approach demonstrates consistent methodological innovation in merging computational intelligence with fundamental laser physics. Supervises 6 PhD students including Rosemary Catriona Clark and Fedor Chernikov in ORC's photonics programs Secures major funding from EPSRC (Smart Fibre-Optic High Power Photonics, Hearing Light) and US Air Force Office of Scientific Research Leads collaborative projects with Professor Sir David Payne and Professor Johan Nilsson across national manufacturing hubs As co-leader of the Smart Lasers and Special Fibres research group within the Advanced Laser Laboratory, Zervas drives experimental photonics innovation through state-of-the-art fibre laser systems and optical resonator technologies. His team maintains strategic partnerships with global industry leaders in photonics manufacturing and medical device development.

Vahab Khoshdelمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Vahab Khoshdel is an Assistant Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba. His research bridges machine learning, deep learning, computer vision, robotics, and medical imaging, with applications in rehabilitation robotics, microwave/ultrasound imaging, stored grain monitoring, and precision agriculture. Education: 2021, Ph.D. Biomedical Engineering, University of Manitoba 2017, Ph.D. Mechanical Engineering, Ferdowsi University of Mashhad 2013, M.Sc. Mechatronic Engineering, University of Shahrood 2011, B.Sc. Robotics Engineering, University of Shahrood Research Interests: Khoshdel specializes in applying generative AI, neural networks, and optimization techniques to medical imaging and robotics. His work includes microwave/ultrasound breast imaging, impedance control for rehabilitation robots, and AI-driven agricultural monitoring systems. Publication Trends: His recent articles emphasize machine learning workflows for medical diagnostics, deep learning in multimodal imaging, and neural networks in rehabilitation robotics. Key subfields include 3D imaging, inverse scattering, tissue classification, and sEMG signal analysis. Contact: Vahab.Khoshdel@umanitoba.ca

Tamer Ölmezمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Brain-Computer Interfaces
• Deep Learning
• +۷ مورد دیگر

Tamer Ölmez is a Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU), College of Engineering, where he conducts cutting-edge research in biomedical signal processing, brain-computer interfaces (BCI), and deep learning applications in medical systems. His work bridges engineering and neuroscience, with a strong focus on EEG-based motor imagery classification, medical image analysis, and embedded deep learning systems. His research interests include motor imagery EEG signal processing , brain-computer interfaces , feature extraction , deep neural networks , classification algorithms , and medical image analysis . He applies machine learning and signal processing techniques to improve diagnostic accuracy and system performance in neuroengineering and healthcare technologies. The recent publications highlight a consistent trend in leveraging divergence-based deep neural networks , convolutional neural networks , and small-sized models for efficient and accurate classification in BCI and medical imaging. His work emphasizes performance improvement with reduced channel counts, noise elimination, and real-time applicability in embedded systems. Scientific Awards: Excellent Oral Presentation Certificate, June 1, 2015 Advising and Grants: He is actively supervising 26 theses in progress, indicating a strong mentoring role. He has led multiple funded research projects, including those funded by ITU’s Technology Transfer Office (TTO) and Scientific Research Projects (BAP), such as 'Classification of Medical Images with Deep Learning Method in Embedded Systems' and 'New Approaches to Finding Optimal Protein Folding'. Labs and Research Teams: While specific lab names are not mentioned, his collaborative fingerprint and project leadership suggest he leads or is a key member of a research group focused on biomedical signal processing, neural networks, and intelligent systems at ITU.

• Digital Circuits and Systems
• Energy-Efficient Circuits and Systems
• Embedded Systems
• +۷ مورد دیگر

Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)

• Cardiac Electrophysiology
• Biomedical Instrumentation
• Mechanoelectric Feedback
• +۷ مورد دیگر

Irene del Canto Serrano is a Researcher in the Department of Electronic Engineering at the School of Engineering, Universitat de València. Her work integrates biomedical engineering with cardiac electrophysiology, focusing on the interaction between mechanical forces and electrical activity in the heart. She is actively involved in two key research groups: GRELCA (Cardiac Electrophysiology group) and i2N (Electronic Instrumentation in Medical and Nuclear Physics), reflecting her dual expertise in physiology and instrumentation. Education: PhD in Biomedical Engineering, Universitat Politècnica de València (2015). Thesis: Estudio de las modificaciones farmacológicas de los efectos electrofisiológicos producidos por el estiramiento local miocárdico a partir de técnicas dinámicas de cartografía eléctrica, en un modelo experimental de corazón aislado de conejo , supervised by Dr. David Moratal Pérez and Dr. Francisco Javier Chorro Gascó. Her research interests center on cardiac electrophysiology , particularly mechanoelectric feedback , myocardial stretch , arrhythmia mechanisms , and pharmacological modulation using experimental models. She also explores cardiac imaging , especially cardiac MRI for strain and deformation analysis, and applies machine learning to improve detection and classification in myocardial infarction. Her recent publications highlight a strong trend toward integrating biomarkers (e.g., ferritin), iron therapy , and cardiac function recovery in heart failure, showing translational relevance. Her 15 most recent publications reflect a consistent focus on experimental cardiology using isolated heart models, pharmacological interventions (ranolazine, GS967, eleclazine), and advanced imaging techniques. She investigates how drugs affect stretch-induced arrhythmias, evaluates MRI-based strain changes post-iron therapy, and develops AI tools for cardiac image analysis. These works span basic science (e.g., CaMKII inhibition) to clinical applications (e.g., Myocardial-IRON trial analysis). Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: While no formal students or grants are listed, her role as a postdoctoral researcher and active publication record suggest involvement in mentoring junior researchers and contributing to funded projects, particularly within the GRELCA and i2N groups. She has co-authored numerous experimental studies, indicating strong collaborative and project-based research activity. Labs and Teams: Irene is affiliated with two prominent research groups at Universitat de València: GRELCA (Cardiac Electrophysiology group) , which studies arrhythmia mechanisms and therapeutic interventions, and i2N (Electronic Instrumentation in Medical and Nuclear Physics) , which develops advanced tools for medical diagnostics. These affiliations underscore her interdisciplinary approach, combining physiology, engineering, and data science.

Kalle Åströmمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +۵ مورد دیگر

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.

• Biomedical Engineering
• Voice Analysis
• Artificial Intelligence
• +۵ مورد دیگر

Daria Hemmerling , PhD, Eng., is a Lecturer at the Department of Metrology and Electronics under the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków. Her research focuses on the intersection of biomedical engineering, voice analysis, and artificial intelligence, particularly for neurological and cardiovascular disease diagnostics. Research Interests : Applying mixed reality and AI for Parkinson’s disease assessment Voice/speech biomarkers for heart failure and neurodegenerative disorders Deep learning techniques in medical imaging (e.g., skull segmentation/reconstruction) Haptic feedback systems for multisensory interaction Simulation training in electrophysiology education Unsupervised learning and modality translation in biomedical signal processing Scientific Trends : Her recent work emphasizes multimodal diagnostic systems integrating voice analysis, VR/MR visualization, and deep learning. She explores explainable AI for medical classification tasks, data augmentation strategies, and innovative haptic/gamification interfaces.

Lutz Frankensteinمشاهده پروفایل

استاد

• Clinical Cardiology
• Chronic Heart Failure
• Risk Prediction
• +۳ مورد دیگر

Lutz Frankenstein is a Professor and Senior Physician ( Oberarzt ) at the Department of Cardiology, Angiology, and Pulmonology at Heidelberg University Hospital. He also serves as Medical Director of the Heart Failure Outpatient Clinic and leads the Working Group on Chronic Heart Failure/Health Economics. Education: Executive MSc in Health Economics (London School of Economics), Habilitation at Heidelberg University Clinical Specialties: Chronic Heart Failure, Performance Physiology, Submarine Medicine, Health Economics His research focuses on clinical cardiology , particularly chronic heart failure , with emphasis on new diagnostic techniques , risk prediction models , and health economics . He has published extensively on beta-blockers, SGLT2 inhibitors, and multidisciplinary care for heart failure patients. Recent article trends span cardiovascular outcomes in diabetes , sex-based treatment disparities , and health policy reforms for transcatheter valve interventions. His work integrates clinical trials , network meta-analysis , and health services research . Frankenstein is actively involved in clinical training (e.g., interventional cardiology certification) and healthcare policy , including roles in DRG evaluation and ESC guidelines. He leads research on resilience in chronic heart failure and periodontal-heart disease links .

Alin Achimمشاهده پروفایل

استاد

• Statistical Signal Processing
• Inverse Problems
• Earth Observation
• +۲ مورد دیگر

Alin Achim serves as Professor of Computational Imaging within the School of Computer Science at the University of Bristol, specializing in advanced signal and image analysis methodologies with cross-disciplinary applications. His academic foundation includes: B.Sc. from Bucharest M.Sc. from Bucharest Ph.D. from Patras Research focuses critically on statistical signal processing frameworks for solving inverse problems, with significant contributions to Earth observation systems through synthetic aperture radar analysis and biomedical image processing innovations. His work bridges theoretical computational models with practical remote sensing and medical diagnostics applications. Professor Achim maintains active media engagement as a subject-matter expert, operating from the Merchant Venturers Building (Woodland Road, Clifton, Bristol BS8 1UB) with direct contact via Alin.Achim@bristol.ac.uk or +44 117 456 1359.

• Neuroscience
• Epilepsy Research
• Molecular Mechanisms of Epileptogenesis
• +۳ مورد دیگر

James O'Connell McNamara is a Distinguished Professor in Neuroscience at Duke University School of Medicine, holding professorships in Neurobiology, Neurology, and Pharmacology and Cancer Biology. He serves as Director of the Center for Translational Neuroscience and is a Faculty Network Member of the Duke Institute for Brain Sciences. His research focuses on elucidating the cellular and molecular mechanisms underlying epileptogenesis, particularly temporal lobe epilepsy (TLE). McNamara's research interests center on understanding how episodes of status epilepticus lead to the development of temporal lobe epilepsy. His laboratory discovered that excessive activation of the BDNF receptor tyrosine kinase TrkB is required for epileptogenesis, and they developed a peptide (pY816) that uncouples TrkB from phospholipase Cγ1, which can prevent and potentially reverse epilepsy. His work has established a novel strategy targeting receptor tyrosine kinase signaling for epilepsy prevention and treatment. His recent publications demonstrate continued active research in TrkB signaling pathways, seizure detection methods, and therapeutic approaches for epilepsy. His work spans from basic molecular mechanisms to translational applications, with a clear focus on developing disease-modifying treatments for epilepsy rather than just symptom management. Dr. McNamara has received numerous prestigious awards including membership in the National Academy of Medicine (2005), the Freedom to Discover Award from Bristol-Myers Squibb Foundation (2001), and multiple Epilepsy Research Recognition Awards from professional societies. His sustained contributions to epilepsy research have been recognized with two Javits Neuroscience Investigator Awards from the National Institute of Neurological Disease and Stroke. McNamara earned his M.D. from the University of Michigan, Ann Arbor in 1968 and has maintained an active research program for decades, with publications spanning from the 1980s to the present day. His work has significantly advanced our understanding of the molecular mechanisms underlying epilepsy and has opened new therapeutic avenues for preventing and treating this neurological disorder.

Zhuhao Wuمشاهده پروفایل

استادیار

• Neuroscience
• Neurovascular Coupling
• Neural Circuit Mapping
• +۴ مورد دیگر

Zhuhao Wu serves as Assistant Professor of Neuroscience at the Brain and Mind Research Institute, Weill Cornell Medical College since 2022. His research integrates neurovascular biology, neural circuit mapping, and neurodegenerative mechanisms to understand brain organization and disease processes. Education: Ph.D. in Neuroscience, The Johns Hopkins University School of Medicine (2011) B.S. in Biological Sciences, Tsinghua University, China (2003) Research Focus: Dr. Wu pioneers multi-scale investigations of neurovascular coupling , brain-wide circuit organization , and neurodegenerative pathways . His lab employs whole-brain imaging , single-cell transcriptomics , and genetic engineering in murine models to dissect mechanisms of stroke recovery, tau pathology, and developmental disorders. Current work emphasizes regional blood-brain barrier heterogeneity and axon degeneration pathways with therapeutic implications. Publication Trends: Recent work (2023-2025) reveals three convergent themes: (1) neurovascular dynamics in health/disease, (2) high-resolution brain atlasing techniques, and (3) molecular mechanisms of neurodegeneration. Publications in Cell , Nature , and Neuron demonstrate methodological innovation in circuit mapping and translational relevance to stroke, Alzheimer's, and autism spectrum disorders. Grant Portfolio: Principal Investigator Subaward: NINDS R01 Investigating Neurobiology of Early Cognitive Impairment (2024-2029) Principal Investigator Subaward: NINDS R01 Mechanisms of anosmia in COVID-19 (2023-2028) Principal Investigator Subaward: NINDS BRAIN CONNECTS Center for Large-scale Imaging (2023-2028) Principal Investigator Subaward: NINDS Global mapping of DDX3X mutation circuits (2023-2028) Principal Investigator Subaward: NIAID single-cell encephalitis pathogenesis (2023-2026) Dr. Wu leads a multidisciplinary team within the Brain and Mind Research Institute focused on developing HOLiS (whole-brain staining/clearing pipeline) and TrailMap for neural circuit analysis. His lab collaborates extensively on NIH BRAIN Initiative projects advancing large-scale connectome mapping.

Ørjan Grøttem Martinsenمشاهده پروفایل

استاد

• Bioimpedance
• Biomedical Physics
• Electronics and Instrumentation
• +۳ مورد دیگر

Ørjan Grøttem Martinsen is a Professor of Electronics at the Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo. He also holds a temporary research position at the Medical Technology Business Area of Oslo University Hospital. With over three decades of experience, he has established himself as a leading expert in bioimpedance research and applications. Education: High-voltage engineer degree (1983) Cand. scient. in electronics/measurement technology (1990) Dr. scient. with thesis on skin's electrical properties (1995) Professor Martinsen's research centers on bioimpedance—the passive electrical properties of biological tissues that vary with anatomy and physiology. His work spans diverse applications including medical diagnostics (skin cancer detection), food quality assessment (fresh vs. thawed fish), skin condition monitoring (moisture levels), and stress level evaluation. His research bridges physics, engineering, and medical applications, creating practical diagnostic tools from fundamental electrical principles. He has pioneered methods to characterize tissue properties through impedance measurements, with particular focus on electrodermal activity and skin impedance. His recent publications (2022-2025) demonstrate a strong interdisciplinary approach combining bioimpedance with machine learning, robotics, and advanced signal processing. The work spans from fundamental biophysics (GABA detection, tissue characterization) to practical applications (dental anxiety assessment, ADHD treatment evaluation). Key trends include integration of AI with bioimpedance measurements, development of novel sensor systems, and expansion into new application areas like optogenetics and micro-robotics. Awards and Recognition: IEEE Senior Member (2006) CLABIO Award (2012) Fellow at Institute of Physics (FInstP) (2015) Dr. Honoris Causa, Tallinn University of Technology (2018) UiO Innovation Award (2019) Member of Norwegian Academy of Technical Sciences (2021) Professor Martinsen has served as Editor-in-Chief of the Journal of Electrical Bioimpedance since 2010 and was President of the International Society for Electrical Bioimpedance (2010-2016). His research has attracted significant funding, enabling collaborations across engineering, medical, and biological disciplines. He has supervised numerous students and researchers in the Bioimpedance Group at UiO, fostering a strong research environment that bridges theoretical and applied work. His work is conducted primarily through the Oslo Bioimpedance Group and Sensorama SmartSense research teams, which focus on developing innovative measurement techniques and applications of bioimpedance technology. These groups maintain strong collaborations with medical institutions and industry partners to translate research findings into practical healthcare solutions.

• Artificial Intelligence
• Smart Manufacturing
• Fault Diagnosis
• +۶ مورد دیگر

Dr. M M Manjurul Islam is a Research Associate in Artificial Intelligence for Smart Manufacturing at Ulster University's School of Computing, Engineering and Intelligent Systems. His research focuses on applying advanced AI techniques to solve critical challenges in manufacturing systems, with particular expertise in fault diagnosis, predictive maintenance, and semiconductor production optimization. His research interests span Artificial Intelligence , Smart Manufacturing , Fault Diagnosis , Machine Learning , Deep Learning , Predictive Maintenance , and Semiconductor Manufacturing . He has made significant contributions to the application of convolutional neural networks, support vector machines, and generative adversarial networks in industrial settings, particularly for bearing fault diagnosis and wafer defect classification. Dr. Islam's recent publications (2023-2025) demonstrate a strong focus on practical AI applications in manufacturing, with multiple chapters in the Springer Series in Advanced Manufacturing. His work shows an evolving trajectory from traditional machine learning approaches to more sophisticated deep learning and explainable AI techniques, with increasing emphasis on semiconductor manufacturing challenges and trustworthy AI systems. His research contributes to UN Sustainable Development Goals, particularly in industrial innovation and infrastructure. He is an active member of professional organizations including IEEE and Advance HE, serving as Chair for both networks. According to Scopus data, Dr. Islam has accumulated 1,706 citations with an h-index of 16, reflecting the impact of his research in the field. His publication record shows consistent productivity, with research outputs spanning from 2015 to anticipated publications in 2025.

Andrew Jirasekمشاهده پروفایل

استاد

• Medical Physics
• Radiation Oncology Physics
• Raman Spectroscopy
• +۳ مورد دیگر

Andrew Jirasek is a Professor at the Irving K. Barber Faculty of Science , University of British Columbia Okanagan , and serves as the Associate Dean for Graduate and Postdoctoral Training . He leads the Analytics in Medical Sciences (AiMS) Institute with a focus on Medical Physics and Radiation Oncology Physics , particularly utilizing Raman spectroscopy and 3D radiation dosimetry for cancer treatment verification. PhD from University of British Columbia His research involves developing polymer gel dosimeters for 3D radiation dose verification in complex therapies like volumetric modulated arc therapy (VMAT) , collaborating across physics, oncology, and engineering . He also investigates optical technologies to monitor biological responses during radiotherapy using Raman spectroscopy with machine learning for data analysis. Recent publications highlight advancements in 3D gel dosimetry , Raman spectroscopy for metabolic profiling , and iterative image reconstruction algorithms . His work spans dosimeter technology development , radiation therapy quality assurance , and clinical applicability studies for novel treatment verification methods.