جست‌وجوی استاد و پژوهشگر

• Robotics
• Control Systems
• Medical Engineering
• +۳ مورد دیگر

Liangjing Yang is an Adjunct Associate Professor at the University of Illinois at Urbana-Champaign and an Assistant Professor at Zhejiang University. He holds a Doctor of Engineering from the University of Tokyo (2014), a Master of Engineering from the National University of Singapore (2011), and a Bachelor of Engineering from the same institution (2008). His academic positions include research fellowships at MIT, SUTD, and the University of Tokyo, alongside his current faculty roles. His research focuses on robotics, control systems, and medical engineering , with applications in image-guided surgery, human-robot interaction, and precision engineering. Key areas include robotic micromanipulation, adaptive control strategies, and vision-based tracking systems for surgical navigation. Yang's teaching contributions include courses such as Resident Instruction Dynamics of Mechanical Systems , Signal Processing , and Control Systems at ZJUI (Zhejiang University-UIUC Institute). He has received accolades like the First Prize in ZJU-UIUC Junior Faculty Course Design Competition (2022) and the SUTD-MIT Postdoctoral Fellowship (2016) . His research articles span topics like robotic solutions for 3D printing, medical imaging integration, and collaborative human-robot systems. Notable work includes Detect-Remove-Replace: A Robotic Solution That Enables Unmanned Continuous 3D Printing (2022) and Review on Vision-Based Tracking in Surgical Navigation (2020).

• Surface Metrology
• Additive Manufacturing
• Precision Engineering
• +۴ مورد دیگر

Professor Jane Jiang is a distinguished academic at the University of Huddersfield , affiliated with the School of Computing and Engineering and the Department of Engineering . She specializes in surface metrology, precision engineering, and advanced manufacturing technologies. Her work focuses on optimizing measurement techniques for additive manufacturing, X-ray computed tomography, and optical systems. Research Interests Her research spans surface texture analysis , 3D vision systems , and metrology for smart manufacturing . She develops novel methods for characterizing complex surfaces using techniques like phase measuring deflectometry and chromatic confocal sensors. Her contributions bridge engineering and materials science, emphasizing practical applications in aerospace, biomedical, and industrial sectors. Recent Contributions Her 2025 work includes advancements in freeform optics design, vibration-resistant microscopy, and neural network-based surface characterization. Her studies on XCT measurement for additive manufacturing parts address critical challenges in precision and data analysis. Affiliations & Projects She leads the Centre for Precision Technologies and actively collaborates on EU-funded projects. She organized the 1st International Conference on Metrology and Standard (2024) and contributes to interdisciplinary initiatives in bio-engineering and advanced materials. Grants & Teams Her team includes researchers like Shan Lou and Paul Scott , focusing on metrology for medical implants and aerospace components. She supervises PhD students exploring AI-driven surface specification and manufacturing process optimization.

Saima Hilalمشاهده پروفایل

استادیار

• Neurodegenerative diseases
• Neurovascular diseases
• Cardiovascular diseases
• +۴ مورد دیگر

Saima Hilal is an Assistant Professor at the NUS Saw Swee Hock School of Public Health, Department of Pharmacology, with affiliations at the National University Health System's Memory Aging and Cognition Center and Erasmus University Medical Center, Rotterdam. Her research focuses on neurodegenerative and vascular diseases, leveraging neuroimaging, epidemiology, and biomarkers to address underdiagnosed conditions like dementia in Asian populations. Education: PhD in Medicine (2016, NUS), MPH (2013, NUS), MD (2006, Dow Medical College, Pakistan). Research interests include Alzheimer’s disease, cerebrovascular pathologies, and cardiovascular risk factors. Notable grants include a Bright Focus Foundation grant on cerebral microinfarcts and a Start-up grant on cardiovascular disease prevention. She has pioneered methods like WMH-DualTasker for neuroimaging analysis and developed predictive models for cognitive decline using retinal and plasma biomarkers. Awards : World Heart Federation Emerging Leaders Programme, Travel grants from Alzheimer’s Association and Vascular Behavioral Disorders Society. Teaching : Advanced Epidemiology, Public Health Aging, Non-Communicable Diseases. Her work bridges clinical and population-based studies, emphasizing multi-ethnic cohorts and machine learning applications. She leads initiatives like the DEBBIE-AD study on blood-brain barrier biomarkers and the SEACURE consensus for mild cognitive impairment management in Southeast Asia.

• Optical Communication Systems
• Medical Image Segmentation
• Blockchain Technology
• +۳ مورد دیگر

Xueguang Yuan is a researcher with expertise in optical communication systems, medical image segmentation, blockchain technology, and IoT networks. Their work spans 2009–2024, focusing on advanced sensor design, federated learning algorithms, and secure communication protocols. Key Research Areas : Optical Time-Domain Reflectometry, Graphene Metasurfaces, Thyroid Nodule Segmentation, and Federated Learning for Multi-Institutional Collaboration Recent Publications (2024): Wearable strain sensors using MXene composites, SNR optimization in Φ-OTDR systems, and polarization conversion metasurfaces for radar cross-section reduction Notable collaborations include Yangan Zhang (optical systems), Xiaohong Huang (medical AI), and Zhifang Deng (federated learning). Trends in their 2021–2024 articles highlight applications of transformers , compressed sensing , and blockchain in healthcare, 5G networks, and security. They have contributed to 5G broadcast services, satellite routing, and edge computing platforms, though no formal awards or student mentorship details are publicly listed in the provided sources.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۳ مورد دیگر

João Paulo Papa is a Professor at the Department of Computing within the Institute of Biosciences, Humanities and Exact Sciences at São Paulo State University (UNESP), Brazil. His research spans machine learning, computer vision, and quantum computing with significant applications in medical diagnostics and environmental monitoring. Over the past three years, he has published extensively in top-tier journals including IEEE Access, ACM Computing Surveys, and Neural Computing and Applications. His research interests focus on developing innovative machine learning approaches for healthcare applications, particularly in Parkinson's disease detection through speech and facial analysis, medical image processing for cancer detection, and quantum-classical hybrid models. His work demonstrates strong integration of theoretical machine learning advancements with practical medical and environmental applications. Papa has established a productive research group that has produced numerous publications in computer vision conferences and medical informatics venues. Analysis of his recent publications reveals a strong emphasis on medical applications of AI, with approximately 60% of his work focused on healthcare diagnostics, 25% on fundamental machine learning advancements, and 15% on environmental and remote sensing applications. His research shows increasing collaboration with international partners while maintaining strong roots in Brazilian academic networks. Among his notable contributions is the development of specialized machine learning architectures for medical image analysis, including TransConv for esophageal cancer detection and quantum-classical hybrid models for breast cancer diagnosis. He has also made significant contributions to the Portuguese language processing community through adaptations of large language models for Brazilian Portuguese medical applications. Prof. Papa actively supervises graduate students and collaborates with medical professionals across Brazil, translating AI research into practical clinical tools. His research group maintains strong connections with hospitals and medical research centers to ensure clinical relevance of their technical developments.

• Image Processing
• Computer Vision
• Signal Processing
• +۴ مورد دیگر

Sos S. Agaian is a Professor affiliated with City University of New York (CUNY), USA, with a former position at the University of Texas at San Antonio's Department of Electrical and Computer Engineering. His research focuses on advanced image and signal processing techniques, including thermal imaging, medical imaging, neural networks, and computer vision applications. He has authored numerous publications in top-tier journals and conferences, contributing to fields like adversarial attack defenses, solar panel segmentation, and quantum-inspired algorithms. His work emphasizes practical solutions for image enhancement, deblurring, and segmentation, with applications in healthcare, energy systems, and cybersecurity. Research Interests: Image Processing and Computer Vision Signal Processing and Machine Learning Medical and Thermal Imaging Neural Networks and Deep Learning Quantum Computing Applications in Imaging Recent Trends in Articles: Recent work highlights advancements in quaternion-based neural networks for weather removal, lightweight networks for solar panel fault detection, and novel entropy models for thermal imaging. Collaborations span medical diagnostics, cybersecurity, and energy systems, reflecting interdisciplinary impact. Grants and Collaborations: While specific grants are not detailed, his extensive publication record suggests active research funding in areas like computer vision and quantum information processing. Collaborators include institutions globally, emphasizing translational research. Labs/Teams: Not explicitly mentioned, but his work implies involvement in imaging and machine learning research groups focused on practical solutions for real-world challenges.

Miloš Stojmenovićمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Image Processing
• +۶ مورد دیگر

Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.

• Biomedical Signal Processing
• Decision-Making Methodologies
• Frailty Prevention in Elderly
• +۳ مورد دیگر

Aly CHKEIR is an Associate Professor at the University of Technology of Troyes (UTT), holding the 'SilverTech' chair focused on technologies for healthy aging and elderly autonomy support. He earned his PhD in biomedical sciences from the University of Technology of Compiègne (UTC) in 2011, specializing in uterine electrical activity modeling. His research emphasizes biomedical signal processing, decision-making methodologies, and applications in frailty prevention among the elderly. Education: PhD in Biomedical Sciences, UTC, France (2011). Research interests include signal segmentation/classification, biomedical engineering, and solutions for aging populations. Notable projects include the SilverTech chair's holistic approach to aging technologies and the PiCADo initiative for in-home medical monitoring systems. Recent work focuses on AI-driven diagnostics (e.g., MRI segmentation, cough analysis), radar-based mobility assessment, and voice recognition for health monitoring. His contributions span sensor fusion, gait analysis, and non-invasive health tracking tools. His lab collaborations include Doppler radar systems for gait speed measurement, thermal imaging for health indicators, and AI-enhanced geriatric assessment tools. Projects like ARPEGE and PiCADo highlight his commitment to deploying innovative healthcare solutions for aging populations.

Krishan Ranaمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Artificial Intelligence
• Reinforcement Learning
• +۳ مورد دیگر

Dr. Krishan Rana is a Research Fellow at the QUT Centre for Robotics, specializing in robot learning at the intersection of AI and robotics. His work focuses on enabling robots to intelligently plan and interact with the world, particularly through projects like From Chat to Chores: The Future of LLM-Powered Service Robots and Robot Learning for Everyday Tasks with Large Language Models . He explores reinforcement learning, visuomotor control, and sim-to-real policy deployment. His research interests include robot navigation, policy learning, and embodied AI, with applications in service robotics and medical imaging. He leads projects involving semantic maps, affordance-centric task frames, and multi-modal perception. His work bridges theoretical advancements with practical deployments in dynamic environments. Recent contributions include developing datasets like LHManip for cluttered manipulation tasks and Robohop for visual navigation. He collaborates on open-source robotics tools through initiatives like Open X-Embodiment. His research emphasizes scalability, sample efficiency, and integration of large language models into robotic systems.

Andrew Razjigaevمشاهده پروفایل

پژوهشگر

• Medical Robotics
• Surgical Robotics
• Robot Kinematics
• +۳ مورد دیگر

Dr Andrew Razjigaev is a PhD Researcher at the Queensland University of Technology (QUT), affiliated with the Medical Robotics Group within the QUT Centre for Robotics. He holds a Mechatronics Engineering degree from QUT (2017) and has been involved in the Medical Robotics Group since 2016, contributing to projects like teleoperating snake robots via hand gestures and visual servoing systems. His research focuses on surgical robotics, particularly developing continuum robots for vision-based teleoperated minimally invasive surgeries. Education: Bachelor of Mechatronics Engineering, Queensland University of Technology (2017) Research interests include robot arm kinematics, robotic vision, and teleoperation systems. His work integrates computational design algorithms with patient-specific surgical needs, advancing the field of minimally invasive surgery through snake-like manipulators and robotic collaboration (e.g., RAVEN II integration). He has also contributed to projects like CARTMAN, a low-cost Cartesian manipulator that won the Amazon Robotics Challenge, and semantic segmentation techniques for limited training data. He served as President of the QUT Robotics Club in 2018, developing robot arm workshops focused on control systems and computer vision for students. His contributions span surgical robotics, competition robotics, and medical automation, with a strong emphasis on practical applications and interdisciplinary collaboration.

• Medical Imaging
• Radiology
• Oncology
• +۳ مورد دیگر

Erik Baubeta Fridh is a Researcher affiliated with Lund University, based at the Lund University Cancer Centre (LUCC) and the Department of Radiology Diagnostics in Malmö. His academic roles include PhD Researcher and Postdoc positions. He focuses on medical imaging applications in oncology, particularly prostate cancer and liver tumor treatments. His research integrates radiomics, clinical prediction models, and artificial intelligence to improve diagnostic and surgical outcomes. His work contributes to Sustainable Development Goals related to health and well-being. Key projects include 'Improving minimal invasive techniques for liver tumors' (2024–2032) and 'Breast cancer and liver metastases imaging perspectives' (2024–2028). He collaborates internationally, with recent studies published in journals like Annals of Surgical Oncology and Journal of Medical Imaging . Research interests span radiology diagnostics, surgical oncology, and AI-driven imaging solutions. He has co-authored 17 peer-reviewed articles and actively participates in clinical trials and technology validation studies.

Yuchen Qiuمشاهده پروفایل

دانشیار

• Quantitative Image Analysis
• Medical Image Analysis
• Artificial Intelligence in Medicine
• +۶ مورد دیگر

Dr. Yuchen Qiu is an Associate Professor at the Gallogly College of Engineering, University of Oklahoma, specializing in quantitative medical imaging and artificial intelligence applications in cancer diagnosis. His research bridges electrical engineering and biomedical analysis, focusing on early detection of breast and ovarian cancers. Education: B.E. and M.E. in Electrical Engineering and Biomedical Engineering, Xi'an Jiaotong University Ph.D. in Electrical and Computer Engineering, University of Oklahoma Dr. Qiu’s work emphasizes medical image analysis , leveraging radiomics and deep learning for tumor response prediction and biomarker development. Recent publications highlight applications of Fourier ptychography microscopy and transformer models in cancer diagnostics. His research trends include multimodal data fusion (CT/pathology), comparative analysis of 2D/3D imaging features, and optimization of computer-aided diagnosis schemes. Professional Affiliations: International Society for Optics and Photonics (SPIE) Institute of Electrical and Electronics Engineers (IEEE)

• Cardiology
• Medical Imaging
• Nuclear Medicine
• +۳ مورد دیگر

Dr. Yi-Hwa Liu is a Senior Research Scientist in Medicine (Cardiology) at Yale School of Medicine, serving as Associate Director of the Cardiovascular Nuclear Imaging Core Laboratory. He holds a PhD in Electrical and Computer Engineering from Rensselaer Polytechnic Institute (1991), with prior faculty roles as Assistant Professor (1998–2004) and Associate Professor (2004–2014) at Yale. His research focuses on advanced medical imaging techniques, including nuclear cardiac image reconstruction, myocardial perfusion imaging, and non-coherent image restoration. He co-invented the Wackers-Liu CQ® quantitative analytic method for myocardial perfusion tomographic imaging and authored influential textbooks in cardiovascular imaging. Dr. Liu is an IEEE Senior Member and Sigma Xi member. His work integrates machine learning, AI, and engineering principles to improve cardiovascular diagnostics. Education: PhD, Electrical and Computer Engineering, Rensselaer Polytechnic Institute (1991) MS, Electrical and Computer Engineering, University of Missouri (1987) BS, Biomedical Engineering, Chung Yuan Christian University (1981) Research Interests: Medical image reconstruction and quantification Nuclear cardiology imaging Myocardial perfusion SPECT/PET AI-driven medical imaging solutions Cardiovascular risk prediction models Key Contributions: Co-developer of Wackers-Liu CQ® quantitative method Pioneer in fluorescence microscopy image restoration Authored major textbooks in cardiovascular imaging Leader in integrating machine learning with cardiac imaging Publications: Over 80 peer-reviewed articles, emphasizing innovations in cardiac imaging techniques and quantitative analysis methodologies. Recent work explores GAN applications for motion correction and deep learning-based SPECT/PET reconstruction.

• Meta-learning
• Deep Learning
• Bayesian Deep Learning
• +۳ مورد دیگر

Boyan Gao is a Postdoctoral Researcher at the University of Oxford's Institute of Biomedical Engineering, part of the Engineering Science faculty. His research focuses on meta-learning, deep optimisation, and Bayesian deep learning, with applications in clinical machine learning. He is affiliated with the Computational Health Informatics (CHI) Lab under Prof. David Clifton, exploring optimiser and loss function learning for novel tasks. Education: PhD in Deep Learning and Machine Learning from the University of Edinburgh (supervised by Prof. Timothy Hospedales) Research Interests: Boyan's work bridges theoretical advancements in meta-learning with practical clinical applications. Key areas include gradient-based meta-learning, optimisation algorithm adaptation for unseen tasks, and Bayesian approaches in deep learning. His recent projects address challenges in medical image segmentation, physiological signal analysis, and scalable edge-based language models. Labs & Collaborations: He collaborates within the CHI Lab, focusing on clinical machine learning applications. His work intersects with digital health initiatives, leveraging AI for healthcare diagnostics and patient monitoring.

• Biochemistry and cell biology
• Clinical sciences
• Genetics
• +۳ مورد دیگر

Ismail Moghul is a Research Fellow at University College London (UCL), affiliated with the Institute of Ophthalmology and the Research Department of Cancer Bio. He holds a PhD in Data Science (UCL, 2016–2021), an MSc in Bioinformatics (Queen Mary University of London, 2015–2016), and a BSc in Biomedical Sciences (Queen Mary University of London, 2011–2015). His research focuses on biochemistry, clinical sciences, genetics, ophthalmology, immunology, and computational biology, with a particular emphasis on AI-driven diagnostics, genetic disease mechanisms, and epigenetic analysis. He collaborates internationally on projects such as the Eye2Gene initiative, leveraging deep learning for inherited retinal disease diagnosis. His work spans AI applications in ophthalmic imaging, including fundus autofluorescence analysis and retinal biomarker quantification. He has contributed to tools like Retinograd-AI and aIRDetect, which automate image grading for inherited retinal dystrophies. His research also explores genetic determinants of conditions like Fuchs endothelial corneal dystrophy and age-related macular degeneration, integrating multi-omics data and clinical cohorts. Moghul’s publications highlight advancements in epigenetic profiling, long-read sequencing pipelines, and the development of open-source bioinformatics tools such as GIMMEcpg and ReLayer. His interdisciplinary approach bridges computational methods with clinical challenges, addressing unmet needs in precision medicine and patient-centric care. He advocates for patient co-design in healthcare tools, as seen in his commentary on improving hospital letter comprehension for ophthalmic patients. His contributions span over 30 peer-reviewed articles since 2016, reflecting his expertise in AI, genomics, and translational ophthalmology.