جست‌وجوی استاد و پژوهشگر

Sujan Kumar Royمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Sujan Kumar Roy is an Assistant Teaching Professor in the Department of Computer Science at Michigan Technological University. He holds a Ph.D. in Machine Learning with Computer Engineering and Signal Processing from Griffith University, Australia, and has earned multiple academic distinctions, including the 'Award of Excellence in the Research Thesis' and consideration for the 'Chancellor's Medal for Excellence in Ph.D. Thesis 2021.' Dr. Roy's teaching focuses on Computational Intelligence, Foundations of Data Science, Machine Learning, Data Mining, and Introduction to Data Science. His research explores applications of AI, ML, and Data Science in Cybersecurity, Medical Image Analysis, Healthcare Systems, and Speech Enhancement. He has contributed extensively to speech enhancement techniques, integrating Kalman filters with machine learning and deep learning approaches. Recent research trends in his publications emphasize the development of robust algorithms for speech enhancement in noisy environments, fusion datasets for hate speech detection, and adaptive filtering methods. His work bridges signal processing and AI, aiming to improve real-time system performance and noise robustness. Awards: Award of Excellence in the Research Thesis Consideration for Chancellor's Medal for Excellence in Ph.D. Thesis 2021 In teaching, Dr. Roy emphasizes foundational concepts and their practical applications. His grants and collaborations focus on advancing AI-driven solutions for healthcare and cybersecurity challenges. He is affiliated with the Department of Computer Science at Michigan Tech, contributing to both academic and research missions.

Alisa Pieknyمشاهده پروفایل

استاد

• Cell Biology
• Cancer Research
• Drug Discovery
• +۴ مورد دیگر

Alisa Piekny, PhD, is a Professor of Biology and Associate Dean of Research and Infrastructure at Concordia University's School of Health. Her research focuses on cytoskeleton regulation during cell division, migration, and polarity, particularly in cancer cells. She employs human cell cultures, iPSCs, and collaborative drug discovery approaches to advance anti-cancer therapies and nanotechnology-based diagnostic tools. Education: PhD (University of Calgary), Post-Doc (IMP Vienna and University of Chicago). Research interests include anillin protein dynamics, microtubule-actomyosin interactions, and nanoparticle-cell interactions. Her work bridges cell biology with translational medicine, emphasizing cytokinesis mechanisms and their hijacking in cancer. Recent publications highlight innovations in drug delivery systems, fluorescent nanoparticle imaging, and molecular tools for live-cell cytokinesis analysis. Collaborations span nanotechnology, cancer biology, and developmental biology, yielding insights into cell fate regulation and therapeutic targeting. Her research has been recognized through interdisciplinary projects at the interface of biology and materials science, with a focus on practical medical applications.

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dr. Neal Bangerter is a Visiting Professor in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He specializes in medical imaging (MRI), artificial intelligence, machine learning, and signal processing. Dr. Bangerter holds adjunct appointments at INSEAD, the University of Utah, and Brigham Young University. His research focuses on ultra-high field MRI, AI applications in healthcare, and data-driven bioscience technologies. He leads the London Collaborative Ultra-High Field Scanner (LOCUS) project and advises companies on AI and innovation strategies. Education: B.S. in Physics (UC Berkeley), M.S. and Ph.D. in Electrical Engineering (Stanford University). Career highlights include roles at McKinsey & Company, Microsoft, and Reactrix, as well as founding BYU's Medical Imaging Research Center. He has pioneered cross-faculty initiatives like the Crocker Innovation Fellowship Program. Research interests include novel MRI pulse sequences, AI in medical imaging, and large-scale health data analysis. His work spans collaborations with Stanford, Oxford, Cambridge, and Siemens Healthcare. He teaches executive education at INSEAD, focusing on bridging technical concepts with business strategies. Key awards include the David Evans Chair at Brigham Young University. His contributions to the UK Biobank Neuroimaging study and development of MRI techniques like RAFO-4 highlight his impact on advancing imaging technologies and AI applications in healthcare.

Maxime Cordeilمشاهده پروفایل

مدرس ارشد

• Data Visualisation
• Immersive Analytics
• Virtual Reality
• +۵ مورد دیگر

Dr. Maxime Cordeil is a Senior Lecturer in Human Centred Computing at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on Virtual and Augmented Reality technologies for data interaction, interactive visualization systems, and AR interfaces for industry applications. He has authored over 60 publications in top-tier venues such as ACM CHI and IEEE VR, and was nominated as Australia's Field Leader in Computer Graphics in 2021 and 2022. Research Interests: Data visualization, immersive analytics, medical imaging, collaborative systems, and human-computer interaction. Current Projects: Includes embedded visualizations for sports performance, interactive machine learning in 3D environments, and mixed-reality applications in forensic science. PhD Supervision: Actively guiding students in topics like immersive gesture exploration, AR for digital health, and collaborative VR systems. His work bridges theory and practice, with tools like IATK (Immersive Analytics Toolkit) and the MADE-Axes hardware system. He collaborates with industry partners like Raytracer and CSIRO, focusing on applications in space exploration, underwater training, and remote operations. Awards: Multiple best paper recognitions at ISS and CHI, plus industry-driven research grants. Labs: Leads the Immersive Analytics research group at UQ, specializing in embodied interaction and spatial computing.

Jiebo Luoمشاهده پروفایل

استاد

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD and has been affiliated with the Department of Computer Science since 2011, following a 15-year career at Kodak Research. His research spans computer vision, natural language processing, machine learning, data mining, computational social science, and digital health. Luo is an ACM Fellow, AAAI Fellow, IEEE Fellow, SPIE Fellow, and IAPR Fellow. Education: PhD (specific discipline not explicitly stated in text). Research interests include computer vision, machine learning, data mining, social media analysis, biomedical informatics, human-computer interaction, and ubiquitous computing. He co-authored the book Deep Neural Network for Medical Image Computing: Principles and Applications (Elsevier, 2022). His work has led to nearly 600 technical papers and 90+ U.S. patents. Research Trends: Recent work focuses on large language models (LLMs), multimodal systems, AI-driven social media analysis, and healthcare applications. Key areas include bias analysis in political simulations, video understanding, and benchmark development for AI-generated content evaluation. His research bridges theoretical advancements with practical applications in healthcare, social sciences, and multimedia systems. Awards: ACM SIGMM Technical Achievement Award (2021), IEEE Region 1 Technological Innovation Award (2018), Eastman Innovation Award (2004), and multiple best-paper recognitions at top conferences. Service & Leadership: Served as program co-chair for ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017. Currently Editor-in-Chief of IEEE Transactions on Multimedia (2020–2022). Editorial board roles include several IEEE Transactions journals and conferences. Labs & Teams: Leads research groups in computer vision and multimodal computing at the University of Rochester, collaborating on projects like UroSAM (kidney stone classification) and computational social science initiatives.

• Data Science
• Machine Learning
• Harmonic Analysis
• +۷ مورد دیگر

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

Kaye Morganمشاهده پروفایل

دانشیار

• X-ray Imaging
• Phase Contrast X-ray Imaging
• Dark-field Imaging
• +۴ مورد دیگر

Kaye Morgan is an Associate Professor in the School of Physics and Astronomy at Monash University, specializing in X-ray imaging technologies with applications in medical and respiratory research. She holds an Australian Research Council Future Fellowship and has held prestigious positions including a Hans Fischer Fellowship at Technische Universität München. Her research focuses on advancing X-ray optics methodologies, particularly phase contrast X-ray imaging (PCXI) and dark-field imaging, to enhance resolution, speed, and sensitivity. These techniques are applied to study airway health in cystic fibrosis and other respiratory diseases, using synchrotron facilities like SPring-8 and the Munich Compact Light Source. She has pioneered single-grid imaging and propagation-based dark-field approaches, enabling real-time visualization of lung dynamics and treatment efficacy. Morgan leads multiple high-impact projects funded by ARC and international collaborations, with over 85 publications in journals like Optics Express and Scientific Reports. Her work contributes to UN Sustainable Development Goals related to health and innovation. Key achievements include developing lab-based X-ray sources for clinical translation and quantifying lung microstructure through advanced imaging algorithms.

Eric Greeneمشاهده پروفایل

دانشیار

• Asian Religions
• Medieval Chinese Buddhism
• Buddhist Meditation
• +۳ مورد دیگر

Eric Greene is an Associate Professor in the Department of Religious Studies at Yale University, specializing in medieval Chinese Buddhism. He has held academic positions since 2015, including Director of Graduate Studies at the Council on East Asian Studies. His research focuses on the transmission of Indian Buddhism to China, Chan/Zen Buddhism, and Buddhist meditation practices. He earned his Ph.D. in Buddhist Studies from UC Berkeley in 2012, with additional research at Kyoto University. Greene's work bridges historical analysis and textual criticism, particularly in early Chinese Buddhist texts such as the Vimalakīrti Sutra commentaries and meditation manuals. His publications include Chan Before Chan (2021) and The Secrets of Buddhist Meditation (2021), exploring meditation’s role in Chinese Buddhist identity and ritual. He has delivered invited lectures globally, addressing topics like Buddhist image worship, repentance practices, and comparative visions of meditation in medieval China and 19th-century psychology. Greene’s current research examines early Chinese Buddhist translation and commentary practices (150–350 CE), emphasizing how textual interpretation shaped Buddhist sinicization. His affiliations include the Yale Buddhist Studies Initiative and the Department of East Asian Languages and Literatures.

Paula Mendesمشاهده پروفایل

استاد

• Nanotechnology
• Materials Science
• Biomedical Engineering
• +۵ مورد دیگر

Paula Mendes is a Professor of Advanced Materials and Nanotechnology at the University of Birmingham's School of Chemical Engineering. She leads the interdisciplinary Mendes Research Group, focusing on nanoscience, biosensors, and nanotechnology applications in healthcare. Her work bridges engineering, chemistry, and biology to address challenges in biofouling, molecular diagnostics, and medical technologies. She is affiliated with the Healthcare Technologies Institute (HTI), advancing translational research in regenerative medicine and diagnostics. Education: MSc (1997) and PhD (2002) in Chemical Engineering from the University of Porto. Postdoctoral research at the University of Birmingham and UCLA under Fraser Stoddart. Academic career at Birmingham since 2006, promoted to Professor in 2013. Research Interests: Development of electrically switchable surfaces for on-demand biosensing Nanomaterials for cancer diagnostics and molecular imprinting Anti-biofouling materials and nanoelectrocatalysts for fuel cells Integration of nanotechnology with biological systems Awards: ERC Advanced Grant (2024), IChemE Global Award (2016), Women in Tech Academic Award (2019), and over 100 published manuscripts. Advising & Grants: Supervises doctoral students in molecular diagnostics and biosensor development. Holds grants including EPSRC Leadership Fellowship and ERC Consolidator/Advanced Grants. Her group collaborates across disciplines to translate nanotechnology into clinical applications. Labs/Teams: Mendes Research Group at the University of Birmingham, part of the HTI network. Active in editorial roles for journals like Responsive Materials and ChemBioEng Reviews .

• Machine Learning
• Computer Vision
• Biomedical Engineering
• +۶ مورد دیگر

Prof. Huiyu Zhou is a Professor of Machine Learning at the School of Computing and Mathematical Sciences, University of Leicester . He leads the Biomedical Image Processing Lab (BIPL) and serves as Deputy Director of the Research Centre for Artificial Intelligence, Data Analytics and Modelling (AIDAM) , and a member of the Apollo-Leicester Centre for Digital Health and Precision Medicine (CDHPM) . Previously, he held roles as Reader at the University of Leicester and Lecturer at Queen’s University Belfast. Education: BEng in Radio Technology, Huazhong University of Science and Technology, China MSc in Biomedical Engineering, University of Dundee, UK PhD in Computer Vision, Heriot-Watt University, UK His research focuses on AI applications in biomedical imaging , robotics , and health informatics . Current projects include advancing medical image analysis, secure learning systems, and interdisciplinary collaborations in precision medicine. He chairs editorial boards for journals like IEEE Transactions on Human-Machine Systems and Pattern Recognition , and serves as Associate Editor for ICRA and Area Chair for BMVC and IJCAI. Prof. Zhou holds administrative roles such as PGR Director of CMS (2022–2025) , MSc Programme Director (2018–2019) , and coordinates MSc Distance Learning in Informatics . He reviews grants for global bodies including Horizon Europe, UKRI, and the Wellcome Trust, and assesses faculty promotions in the UK, China/HK, Israel, and others. He also acts as an external examiner for institutions across the UK, Hong Kong, Australia, India, and China. Affiliations include the National Academy of Artificial Intelligence (NAAI) and collaborations in interdisciplinary teams for digital health and AI-driven biomedical research.

Hung Yew Munمشاهده پروفایل

دانشیار

• Heat Transfer
• Fluid Dynamics
• Micro-scale Heat Transfer
• +۴ مورد دیگر

Dr. Hung Yew Mun is an Associate Professor and Interim Head of the Mechanical Engineering program at Monash University Malaysia’s Malaysia School of Engineering. His expertise spans heat transfer, thermodynamics, and fluid dynamics, with a focus on micro-scale phenomena, phase-change heat transfer, and graphene-based materials. He teaches courses including MEC3454/MEC4408 (Thermodynamics and Heat Transfer) and MEC4416 (Momentum, Energy & Mass Transport). Dr. Hung’s research addresses advanced cooling solutions for electronics, energy storage, and sustainable materials. Education: PhD in Mechanical Engineering from University of Multimedia, Malaysia (2010). Research Interests: Micro-scale heat transfer, phase-change mechanisms, graphene nanostructures, and applications in thermal management. His work contributes to UN SDG goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Recent Projects: Includes studies on MXene-biochar composites for energy storage, graphene-enhanced devices for electronics cooling, and multiscale modeling of postharvest fruit water transport. He has led/co-investigated 12 projects since 2013, emphasizing interdisciplinary collaboration. Publications: Over 100 peer-reviewed articles, with recent focus on graphene-mediated heat transfer enhancement, plasma-activated cooling, and nanofluid applications. His work addresses both fundamental mechanisms and industrial applications. Labs/Teams: Active in thermal engineering and nanomaterials research groups, collaborating with institutions globally on sustainable energy and advanced materials.

Sanjana Mudduluruمشاهده پروفایل

استادیار

• Machine Learning
• Medical Image Processing
• Data Analytics
• +۳ مورد دیگر

Sanjana Mudduluru is an Assistant Professor in the School of Computer Science at the University of Oklahoma (OU). She holds a BS from Jawaharlal Nehru Technological University (India), an MS in Data Science & Analytics, and a PhD in Computer Science, all from OU. Her research focuses on applying computer vision and machine learning to biomedical imaging, particularly in cancer research and medical diagnostics. She has extensive experience in software development and programming. Education: Ph.D., Computer Science, University of Oklahoma M.S., Data Science & Analytics, University of Oklahoma B.Tech, Computer Science, Jawaharlal Nehru Technological University Her research interests span machine learning, medical image processing, data analytics, and computer science education. She explores innovative deep learning models for medical image segmentation, classification, and synthetic data generation to enhance AI efficacy in healthcare. Recent work includes hybrid models for computer-aided diagnosis and self-supervised learning for rock image analysis. Awards: Dissertation Excellence Award (2023) Tomorrows Engineer Scholarship (2021–2022) CS Alumni Graduate Fellowship (2021–2022) Dr. Mudduluru has no listed advisees but has contributed to grants related to biomedical imaging research. She is affiliated with OU’s Devon Energy Hall and actively publishes in interdisciplinary fields blending computer science with healthcare applications.

Jiayun Wangمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Computational Imaging
• +۳ مورد دیگر

Jiayun (Peter) Wang is a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). His research focuses on advancing AI-driven solutions in medical imaging, computational imaging, and computer vision. Current projects emphasize applying deep learning to diagnose ocular conditions like dry eye syndrome and improving 3D reconstruction techniques. Collaborations with institutions such as UC Berkeley, Microsoft, and NVIDIA highlight his interdisciplinary approach to solving real-world medical and imaging challenges. Research Interests: Medical AI and Healthcare Analytics Deep Learning Applications in Ophthalmology 3D Reconstruction and Scene Understanding Physics-Informed Neural Networks Compressed Sensing MRI Key Contributions: Developed machine learning models predicting dry eye-related outcomes using meibography images Pioneered physics-aware neural operators for ultrasound lung aeration mapping Advanced open-vocabulary 3D object detection systems Labs/Teams: Collaborates with Caltech's AI4Health initiative and NVIDIA's research group, contributing to medical imaging advancements through interdisciplinary teams.

Ismail Ben Ayedمشاهده پروفایل

دانشیار

• Computer vision
• Optimization
• Medical image analysis
• +۱ مورد دیگر

Ismail Ben Ayed is an Associate Professor at École de technologie supérieure (ETS) in Montreal, Canada, holding the ETS Research Chair on Artificial Intelligence in Medical Imaging. His research bridges computer vision, optimization, and medical image analysis to develop advanced algorithms for clinical applications, with particular focus on cardiac and neurological imaging. His research program centers on medical image segmentation using novel optimization techniques, graph-based methods, and deep learning models. He pioneers approaches for handling volumetric bias, shape compactness, and distribution matching in MRI and cardiac imaging, directly addressing clinical challenges in spine labeling, ventricle segmentation, and tumor detection. His work emphasizes mathematical rigor combined with practical medical relevance. Analysis of his 15 most recent publications (2014-2017) reveals dominant themes in medical image segmentation (80% of works), particularly for cardiac MRI (35%) and neurological applications (25%). Key methodological contributions include distributed optimization frameworks (20%), advanced graph cut techniques (30%), and deep learning architectures (25%), published consistently in top-tier venues including CVPR, MICCAI, and TPAMI. His scientific recognition includes: MICCAI travel award (2017) Outstanding Reviewer Award at CVPR (2015) GE innovation award (2010) He actively mentors researchers as evidenced by his recruitment of PhD students and postdocs, with research supported by the ETS Research Chair and multiple patents. His service includes chairing MICCAI 2017/2015 and IPTA 2017, plus continuous program committee roles at CVPR, ICCV, and MICCAI since 2011. Leading the ETS Research Chair on AI in Medical Imaging, he directs a collaborative team working on clinical translation of computer vision techniques. Current projects focus on cardiac motion analysis, brain tumor segmentation, and spine labeling systems with direct applications in radiology workflows.

• Atomic Force Microscopy
• Nanotechnology
• Mechatronics
• +۴ مورد دیگر

Amir Farokh Payam is a Senior Lecturer in Electronics and Software at the School of Engineering, Ulster University, UK since 2019. He holds a PhD in Electronics Engineering-Nanotechnology and has previously worked at Instituto de Ciencia de Materiales de Madrid (2012–2015), Durham University (2016–2018), and University of Bristol (2018–2019). Education: B.Sc. and M.Sc. in Electrical and Electronics Engineering, PhD in Electronics Engineering-Nanotechnology His research focuses on dynamic Atomic Force Microscopy (AFM) , NEMS/MEMS , Surface Science , and Applied Nonlinear Control . Recent work explores nonlinear harmonics in AFM, solid-liquid interfacial dynamics, and single-cell biomechanical profiling. He leads projects on quantum sensor fabrication and cancer cell viscoelasticity analysis. Key trends in his 15 most recent publications (2025–2023) include advancements in nanoscale imaging , multifrequency AFM , viscoelastic material analysis , and biomedical applications such as viral protein sensing and corneal cell profiling. Collaborative projects span institutions in Ireland, Spain, and the UK. Scientific Awards: Editor's Highlights in Journal of Applied Physics (2018) and Nanotechnology (2015) Best Innovative Idea, Second International R&D Award of Iran (2012) Distinct Graduate Student, University of Tehran Visiting Study Scholarship, Instituto de Microelectronica de Madrid (2010) M.Sc. First Class Student (top among 15) He serves as Unit Director for Mechatronics II and Electronics II modules and supervises BEng/MEng research projects. Active research grants include Royal Society funding (2024–2026) for cancer cell biomechanics and an ongoing 2022–2026 project on solid-liquid interface dynamics.