جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Medical Diagnostics
• Microfluidics
• +۷ مورد دیگر

Prof. Hayden Oliver holds the Heinz Nixdorf Chair of Biomedical Electronics at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. His research focuses on interdisciplinary biomedical engineering, combining electronics, optics, microfluidics, and materials science to develop novel in-vitro diagnostic methods. Current work emphasizes magnetic and optical methods for functional diagnostics of blood cells, with applications in point-of-care testing and quantitative phase imaging. Education: Studied biochemistry at the University of Vienna (PhD, 1999). Postdoctoral research at Harvard University, followed by habilitation in Analytical Chemistry at the University of Vienna. Prior to TUM, he worked at IBM Research Zurich (2005–2007) and Siemens Corporate Technology/Siemens Healthcare (2007–2017) on organic electronics for medical imaging and diagnostics. Appointed to TUM in 2017. Research Themes: Digital holographic microscopy, magnetic flow cytometry, label-free cell analysis, AI-driven diagnostics, and microfluidic device development. His work bridges fundamental science (e.g., cell mechanics) with clinical applications (e.g., rapid disease diagnostics). Awards: European Inventor Award (2017), AMA Innovation Award (2016), Siemens NTF Award (2013), Young Investigator Award (2002). Lab/Teams: Leads the Biomedical Electronics research group at TUM, collaborating with industry partners (e.g., Siemens) and academic institutions globally. Grants/Funding: Not explicitly listed in text, but implied through high-profile publications and awards.

• Optometry
• Ophthalmology
• Eye Care
• +۴ مورد دیگر

Jenny Roth is a Research Fellow and PhD student in Optometry at the Department of Medicine and Optometry, Linnaeus University, Kalmar campus. She is affiliated with the Faculty of Health and Life Sciences and the Research in Eyes, Vision and Optometry (REVO) group. Her research focuses on the anterior segment of the eye, with emphasis on infectious keratitis, corneal nerve imaging, and AI-driven diagnostic tools. Research Interests : Jenny’s work bridges clinical optometry and biomedical research. Her studies explore bacterial keratitis epidemiology, AI applications for pathogen detection (e.g., Acanthamoeba Keratitis), and ocular impacts of systemic conditions like mild COVID-19. She employs advanced imaging techniques such as in vivo confocal microscopy to analyze corneal nerve structures and dendritic cells. Key Projects : Current projects include automated nerve segmentation algorithms and antibiotic susceptibility analyses. Completed work includes the AIDAK study (AI for Acanthamoeba diagnosis) and a pilot on dry eye syndrome prevalence in Sweden. Publications : Her articles span clinical ophthalmology journals and AI-driven diagnostic studies. Recent work addresses bacterial infection patterns, AI decision-support systems, and post-COVID ocular nerve changes. Labs/Teams : Active member of the REVO group, collaborating on interdisciplinary eye research projects.

• Music Information Retrieval
• Machine Learning
• Audio Signal Processing
• +۳ مورد دیگر

Alicja Wieczorkowska is a Professor affiliated with the University of Gdańsk, specializing in Music Information Retrieval, Machine Learning, and Audio Signal Processing. She has edited multiple books and special issues in journals like Sensors and Springer's Studies in Computational Intelligence series. Her research focuses on audio processing applications in healthcare, music recommendation systems, and environmental monitoring. Education Background: Not explicitly stated in text. Research Interests: Development of audio and image sensing techniques for healthcare and environmental applications Machine learning models for music and speech analysis Data mining and pattern recognition in polyphonic audio Interpretable models for medical survival analysis Recent Research Trends: Her recent work emphasizes practical applications of machine learning in music recommendation systems, medical diagnostics (e.g., liver transplantation prognosis), and environmental pollen monitoring through image processing. She has pioneered methods for audio-based vehicle speed detection and pipe organ acoustics modeling. Labs/Teams: Actively collaborates with interdisciplinary teams including computer scientists, medical researchers, and acoustic engineers. Key collaborators include Z.W. Ras, E. Kubera, and M. Klec.

Lily Liangمشاهده پروفایل

استاد

• AI
• Cybersecurity
• Computer Science Education
• +۳ مورد دیگر

Dr. Lily Liang is a Professor in the Department of Computer Science and Information Technology at the University of the District of Columbia (UDC), affiliated with the School of Engineering and Applied Sciences. She holds leadership roles including Director of the Master of Science in Computer Science Program (2010-14; 2017-Present) and former Department Chair (2014-16). Education: PhD in Computer Science and Engineering from the University of Nevada, Reno. Research focuses on AI, cybersecurity, and computer science education with an emphasis on K-12 outreach, student mentorship, and broadening STEM participation. Notable projects include Minecraft-based learning initiatives and community partnerships to engage female students in robotics. Publications emphasize educational innovation and cybersecurity solutions, such as deepfake detection methods and curriculum development for underrepresented groups. Awards: 2014 Outstanding University Service Award, 2012 Myrtilla Miner Faculty Fellow Grants: Multiple NSF grants for cybersecurity education and HBCU partnerships Leadership: Led UDC's Learning and Development initiatives (2014-15) and served on NAS/NAE/IOM committees Labs/Teams: Active in interdisciplinary initiatives promoting computational literacy and inclusive tech ecosystems.

Odelia Schwartzمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Neuroscience
• Medical Informatics
• +۴ مورد دیگر

Odelia Schwartz is an Associate Professor in the Department of Computer Science at the University of Miami, College of Arts and Sciences. She also serves as Director of Undergraduate Studies for Computer Science and holds a secondary faculty appointment in Biology. Her research focuses on computational neuroscience, machine learning applications in healthcare and biology, and the intersection of artificial intelligence with visual and neural processing systems. Key research areas include machine learning analysis of medical signals (e.g., ECG for atrial fibrillation prediction), computational modeling of biological systems (e.g., endosymbiont population dynamics via microscopy image analysis), and hierarchical neural network models for visual cortex understanding. Her work integrates statistical methods with deep learning to bridge computational models and biological/neurological phenomena. Publications highlight applications in cardiology, neurotrauma recovery prediction, and visual cortex modeling. While no specific awards are listed, her contributions span interdisciplinary fields at the university and collaborative research institutions. Advising no listed students, but actively engages in graduate training through her faculty roles. No specific labs/teams are mentioned, though collaborations with medical and biological departments are evident.

Xiaotang Luمشاهده پروفایل

استادیار

• Biochemical tools for brain imaging
• Connectomics
• Spatial omics
• +۴ مورد دیگر

Xiaotang Lu is an Assistant Professor in the Department of Chemistry at the University of Illinois Urbana-Champaign, with affiliations at the Beckman Institute for Advanced Science and Technology and the Carl R. Woese Institute for Genomic Biology. She holds a PhD in Materials Science and Engineering from the University of Texas at Austin and completed postdoctoral training in neuroscience at Harvard University under Jeff Lichtman. Her research focuses on developing biochemical tools and advanced imaging techniques to study brain connectivity, particularly in connectomics and correlated light-electron microscopy. Research interests include biochemical tools for brain imaging, spatial omics, and systems neuroscience. She leads the Lu Lab, which works at the chemistry-neuroscience interface to address challenges in understanding brain circuitry and neuropsychiatric diseases. Notable achievements include the development of NATIVE (Nanobody-assisted tissue immunostaining for volumetric EM) and contributions to NIH BRAIN Initiative projects. Education: PhD in Materials Science and Engineering, University of Texas at Austin M.S. in Chemistry, Tsinghua University Postdoctoral Fellowship, Harvard University Awards include the NIH BRAIN Initiative K99/R00 Award (2022–2024), Cornell FIRST Scholar (2022), and Leading Edge Fellow (2022). She teaches Chem 588: Physical Methods for Materials Chemistry and actively recruits graduate students, postdocs, and undergraduates for interdisciplinary research projects. Key publications focus on multiplexed molecular imaging, connectomics methodologies, and advanced microscopy techniques to map brain circuitry. Her work bridges chemical innovation with neurobiological applications, aiming to clarify how brain structure enables function and disease.

Jing Yangمشاهده پروفایل

استاد

• Developmental Biology
• Cytoplasmic Structure and Dynamics
• Reproductive Biology
• +۴ مورد دیگر

Jing Yang is a Professor of Comparative Biosciences at the University of Illinois at Urbana-Champaign, affiliated with the Carl R. Woese Institute for Genomic Biology. She holds dual roles within the College of Liberal Arts & Sciences and School of Molecular & Cellular Biology. Her research focuses on cytoplasmic structure dynamics, developmental biology, reproductive systems, and RNA mechanisms, alongside interdisciplinary work in robotics and medical imaging. Research Interests: Yang’s biological research explores germ plasm dynamics in vertebrate development, RNA phase transitions, and reproductive cell biology. Her robotics work integrates vision-guided systems, surgical navigation, and human-robot interaction, with applications in precision agriculture and medical robotics. Key themes include adaptive control, SLAM algorithms, and immersive learning frameworks. Recent Trends: Articles emphasize robotics advancements (e.g., telerobotics, SLAM systems) and biological studies on Xenopus and zebrafish embryology. Cross-disciplinary projects bridge molecular mechanisms with engineering solutions, such as robotic platforms for strawberry harvesting and AI-enhanced surgical tools. Labs & Teams: The Yang lab develops technologies at the intersection of biology and robotics. Ongoing projects include automated micromanipulation systems and educational frameworks for engineering curricula. Collaborations span departments in genomic sciences, mechanical engineering, and computer vision.

James Hopgoodمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Data Science
• +۵ مورد دیگر

James Hopgood is a Professor of Statistical Signal Processing at the University of Edinburgh's School of Engineering, leading the Institute for Data, Imaging, and Communications. He holds roles as Dean of Quality and Enhancement for the College of Science and Engineering and Director of the EPSRC/MoD CDT in Sensing, Processing & AI for Defence and Security (SPADS). His research focuses on statistical signal processing applications in medical imaging, acoustic sensing, and multi-target tracking. Hopgood earned his M.A., M.Eng., and Ph.D. from the University of Cambridge, joining Edinburgh in 2004. He has developed algorithms for Bayesian signal processing, acoustic source localization, and medical imaging, with over 100 publications. As Editor-in-Chief of the IET Journal of Signal Processing since 2011, he contributes to academic leadership. Teaching includes courses on signal processing, probability, and sensor networks, emphasizing visualization and real-world applications. Current projects span adaptive optical imaging for medical diagnostics and probabilistic graphical models for multi-target tracking. His work bridges academia and industry, collaborating with companies like Leonardo and Agilent Technologies. He supervises numerous PhD students and leads multidisciplinary initiatives in defense, healthcare, and autonomous systems. **Education**: PhD in Statistical Signal Processing, University of Cambridge (2001) MEng in Electrical and Information Sciences, University of Cambridge (1997) MA, University of Cambridge (2000) **Research Interests**: His work spans model-based Bayesian methods, acoustic source localization, medical imaging (e.g., fluorescence lifetime imaging), and multi-target tracking. Recent projects include optimizing UAV trajectories in dynamic environments and developing algorithms for distributed sensor networks. **Grants & Collaborations**: Funded by EPSRC/MoD, GSK, and Leonardo, his projects address defense sensing, drug development, and clinical imaging. Collaborations include Firefinch (software/data science) and the UDRC Summer School. **Labs/Teams**: Leads the Acoustics and Audio Group and the SPADS CDT, fostering innovation in sensing and AI for defense/security applications.

Christian Gagnéمشاهده پروفایل

استاد

• Deep Learning
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Christian Gagné is a Full Professor in the Department of Electrical and Computer Engineering at Laval University and Director of the Institute for Intelligence and Data (IID). He holds a prestigious Canada CIFAR AI Chair and is an Associate Academic Member at Mila – Quebec Artificial Intelligence Institute. His research spans multiple interdisciplinary domains with significant contributions to machine learning and its applications. Dr. Gagné completed his doctorate in electrical engineering at Université Laval in 2005, followed by a postdoctoral fellowship at INRIA Saclay (France) and the University of Lausanne (Switzerland) from 2005-2006. Prior to his academic appointment, he worked as a research associate in an industrial environment between 2006 and 2008. His research interests focus on deep learning and stochastic optimization, with particular emphasis on the robustness and generalization of deep neural networks, neurosymbolic approaches to enhance interpretability, and the development of multimodal foundational models. His work has practical applications across computer vision, super-resolution microscopy, healthcare, transportation, and energy sectors. Dr. Gagné has developed important methodologies in representation learning, meta-learning, transfer learning, and optimization approaches based on probabilistic models and evolutionary algorithms. Recent publications demonstrate a strong focus on improving the robustness of machine learning models, with significant contributions to data augmentation techniques for medical imaging, adversarial attacks on visual object trackers, and test time adaptation methods. His research group has produced influential work at the intersection of theoretical machine learning and practical applications, particularly in medical imaging and computer vision domains. Canada CIFAR AI Chair As an academic advisor, Dr. Gagné has supervised numerous doctoral and master's students including Frédéric Beaupré, Catherine Bouchard, Fatemeh Nourilenjan Nokabadi, Sabyasachi Sahoo, and Adam Tupper. His research is supported through various grants, including his Canada CIFAR AI Chair position and collaborations with multiple research centers. Dr. Gagné is actively involved with several research entities including the Vision and Digital Systems Laboratory (LVSN), the Center for Research in Robotics, Vision and Machine Intelligence (CeRVIM), the Center for Research in Massive Data (CRDM), as well as strategic groups REPARTI and UNIQUE of the FRQNT, the VITAM center of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technology (OBVIA).

• Visual Cortex
• Interneuron
• Ocular Dominance
• +۷ مورد دیگر

Christiaan Levelt is an Endowed Professor in the Faculty of Science at Vrije Universiteit Amsterdam (VU Amsterdam), specializing in Molecular and Cellular Neurobiology. He is also affiliated with Amsterdam Neuroscience - Cellular & Molecular Mechanisms research institute. His primary research interests focus on: Visual Cortex development and plasticity Interneuron function and maturation Ocular dominance plasticity mechanisms Thalamocortical interactions Calcium imaging and optogenetics techniques Dr. Levelt's recent work demonstrates significant contributions to understanding neural circuit development, particularly how inhibitory neurons and astrocytes regulate plasticity in the visual system. His research employs advanced techniques including calcium imaging, optogenetics, and sophisticated computational analysis tools. A notable trend in his recent publications is the exploration of how experience shapes neural circuitry, with implications for understanding developmental disorders and brain plasticity mechanisms. Dr. Levelt has supervised 12 PhD theses, indicating a strong commitment to mentoring the next generation of neuroscientists. His work has received attention across multiple platforms, with several publications being picked up by news outlets (ranging from 5 to 16 outlets per paper) and widely discussed on social media. His laboratory appears to focus on cellular and molecular mechanisms of neural circuit development, particularly in the visual system, using mouse models to investigate fundamental principles of brain organization and plasticity.

• Acoustic Manipulation
• Microfluidics
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Peter Glynne-Jones is an Associate Professor in Mechatronics at the University of Southampton, UK. He leads the Mechatronics theme within the Mechanical Engineering undergraduate program and supervises research projects at all academic levels. His research focuses on acoustic manipulation of microscopic particles , ultrasonic wave physics , and microfluidic systems for disease detection . He develops advanced imaging technologies for human cells and ocean plankton, and explores the intersection of science/engineering with the arts . Recent publications highlight expertise in ultrasound-responsive microbubbles , acoustic-Raman platforms , and ocean sensing technologies , with applications spanning biomedical engineering , microbial biotechnology , and environmental monitoring . Scientific awards include recognition as an IET Scholar and founding the Acoustofluidics Society to foster global research collaboration. He supervises PhD student Anthony James Willis Lindley and contributes to EPSRC-funded projects such as "Ultrasonic cell handling" and "High-Throughput Optical Blood Imaging."

• Machine Learning
• Infection Biology
• Biomedical Imaging
• +۵ مورد دیگر

Dr. Artur Yakimovich leads the Machine Learning for Infection and Disease group at the Helmholtz Center Dresden-Rossendorf (HZDR) and is affiliated with the CASUS Center for Advanced Systems Understanding. His research focuses on applying artificial intelligence to infection biology and biomedical imaging, with a particular emphasis on virus-host interactions and microscopy image analysis. Research areas include deep learning , computational virology , biomedical image processing , and AI-driven diagnostics . Developed open-source tools like PyPlaque for viral plaque analysis. Active in high-content screening and physics-informed neural networks for imaging applications. Current work spans urinary tract infection diagnostics , super-resolution microscopy , and computational modeling of virus transmission .

• Soil Sciences
• Environmental Protection
• Circular Economy
• +۵ مورد دیگر

Martin Head is a Senior Teaching Fellow at the Centre for Environmental Policy, Faculty of Natural Sciences, Imperial College London. He has been active in teaching and research since 2008, specializing in environmental science, soil quality, and citizen science initiatives. Academic Rank: Senior Teaching Fellow University: Imperial College London Role: Director of Postgraduate Teaching (2022-present) Research Focus: Martin's work bridges environmental protection, soil science, and the circular economy. He has pioneered citizen science projects like OPAL Soils, engaging the public in environmental data collection. Article Trends: His publications span soil quality assessment (2014-2010), citizen science methodologies (2011-2012), and concrete microstructure analysis (2008-1998). Key keywords include environmental monitoring, biodiversity conservation, concrete durability, and waste mitigation. Scientific Awards: Fellow of the Higher Education Academy (HEA) Microsoft 365 Champion Professional Contributions: Martin leads curriculum development for the MSc Environmental Technology program, chairs departmental committees, and collaborates with institutions like the Grantham Institute. He has also served as a personal tutor and former warden in student halls (2002-2016).

• Head and Neck Surgery
• Optical Diagnostic Imaging
• Sinonasal Pathologies
• +۳ مورد دیگر

Christian Betz is a full Professor and Director of the Department of Otorhinolaryngology at the University Medical Center Hamburg-Eppendorf (UKE), Faculty of Medicine, University of Hamburg. As Deputy Director of the Head and Neurocenter, he specializes in head and neck surgery, optical diagnostic imaging, and HPV-related cancers. Medical Specialist in Otorhinolaryngology Vice-Deanery for Research representative Active in the European Platform for Photodynamic Medicine International collaborations in LASER SURG MED and radiobiology His research focuses on: Optical metabolic imaging for tumor differentiation AI-driven computer-aided diagnosis of sinus anomalies DNA repair mechanisms in HNSCC Photodynamic therapy applications Post-COVID olfactory dysfunction Key scientific contributions include: Over 30 publications in LASER SURG MED and HEAD NECK-J SCI SPEC 2025 international multicenter studies on surgical outcomes 2024 leadership in AI integration for maxillary sinus classification Awards: Video Award for 5-ALA fluorescence endoscopy Best Oral Communication Prize for OCT applications

Faik Boray Tekمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Deep Learning
• Artificial Neural Networks
• +۷ مورد دیگر

Assoc. Prof. Dr. Faik Boray Tek is a faculty member in the Department of Artificial Intelligence and Data Engineering at Istanbul Technical University (ITU), Faculty of Computer and Informatics. He joined ITU in August 2022 after serving as an Assistant Professor in Computer Engineering at Işık University from 2009 to 2022. His research bridges artificial intelligence and healthcare, with a strong focus on medical image analysis. Education: PhD in Medical Image Analysis, University of Westminster, UK (2007) MSc in Electrical and Electronics Engineering, Middle East Technical University (2003) BSc in Electronic Engineering, Başkent University (2000) His research interests include deep learning, artificial neural networks, medical image analysis, malaria diagnosis, and retinal disease detection . He leverages advanced AI models for diagnosing Alzheimer's disease and diabetic retinopathy using optical coherence tomography (OCT) and OCT angiography (OCTA). His work also extends to Text-to-SQL systems and neuron modeling. Recent publications (2023–2025) demonstrate a strong trend in medical AI , particularly in ophthalmology and neurodegenerative disease diagnosis. He employs foundational models, segmentation techniques, and hybrid architectures to analyze OCT and OCTA scans. Additional work focuses on time-series forecasting via enhanced recurrent units and systematic reviews of AI in healthcare. His publications appear in IEEE Access, Pattern Recognition, and UBMK proceedings. Scientific Awards: Above Threshold Award, TÜBİTAK (2024) Dr. Tek leads active research projects funded by TÜBİTAK and ITU-BAP, including "Optical Coherence Tomography (and Angiography) Scans and Deep Learning-Based Alzheimer's Disease Diagnosis" and "Two-Dimensional Adaptive Local Connected Neuron Model" . He has advised multiple theses and supervised research outputs, indicating active mentorship. His collaborations include Heidelberg Collaboratory for Image Processing (2015). He is affiliated with research labs and teams at ITU focused on AI and data engineering, contributing to both academic and applied innovations in medical diagnostics and neural architectures.