جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Machine Learning
• Biomedical Engineering
• +۱ مورد دیگر

Dr. Sahil Sharma is a Research Fellow at the School of Computing, Engineering and Intelligent Systems, Ulster University, Derry~Londonderry campus. His interdisciplinary research bridges artificial intelligence, biomedical engineering, and agricultural science, with significant contributions to medical diagnostics and sustainable agricultural practices through advanced computational techniques. Sharma's primary research domains include artificial intelligence (particularly deep learning and large language models), biomedical engineering (focusing on cardiac biomarker detection and medical data analysis), and agricultural science (specializing in plant biostimulants and sustainable farming). His work demonstrates consistent innovation in applying machine learning to solve real-world problems across healthcare and agriculture, with recent emphasis on explainable AI for medical visualization and anime recommendation systems. Analysis of Sharma's 15 most recent publications (2023-2025) reveals a dominant trend toward AI-driven healthcare solutions, including cardiac troponin I detection assays, XAI-based medical data visualization, and synthetic data generation for diagnostics. Parallel work explores agricultural applications through deep learning-based crop monitoring systems. His research outputs show strong translational impact, with significant citations (h-index 86) and practical implementations referenced in patents, particularly in the biomedical domain where his 2012 agricultural paper accumulated 85 Scopus citations.

Hang Zhouمشاهده پروفایل

پژوهشگر ارشد

• Functional and high-dimensional data analysis
• Complex-structured data analysis
• ODE/PDE modeling
• +۳ مورد دیگر

Hang Zhou is a Post-Doctoral Scholar at the University of California, Davis, specializing in advanced statistical and computational methodologies. His research focuses on functional and high-dimensional data analysis, complex-structured data including manifold and Wasserstein types, and ODE/PDE modeling. He is affiliated with the Mathematical Sciences Building at UC Davis. His research interests encompass functional data analysis, high-dimensional statistics, and the integration of differential equations into modeling frameworks. He explores complex-structured data such as manifold and Wasserstein geometries, advancing methodologies for analyzing distribution-valued processes and error detection in numerical data. Zhou’s recent work bridges machine learning and statistical theory, exemplified by studies on deep regression for repeated measurements and event-attended graph ODE frameworks. His research also emphasizes conformal inference and optimal transport representations, reflecting a focus on robust statistical methods and interdisciplinary applications in data science. As a postdoctoral researcher, Zhou has not yet listed advisees or grants in the provided information. His website ( https://hg-zh.github.io/ ) provides additional details on his ongoing projects and collaborations.

• Computer Science
• Medical Technology
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Hamit Soyel is a Research Fellow at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Dragonfly initiative. His work spans educational technology, healthcare innovation, and computer vision. His research focuses on enhancing learning through video-based methods, improving rheumatoid arthritis care via technology, and advancing facial recognition systems using geometric and algorithmic approaches. Key research themes include cognitive load optimization in educational videos, patient-clinician communication barriers in RA care, and robust image processing techniques for medical and facial analysis. His interdisciplinary approach bridges computer science with healthcare and education, aiming to develop practical technological solutions. Publications highlight trends in medical technology adoption, video instruction design, and facial expression recognition. He has contributed to patents in image enhancement and algorithms for 3D facial geometry analysis. His work reflects a commitment to both theoretical innovation and real-world applications. No scientific awards are explicitly listed, though his extensive publication record indicates recognition in his fields. He has advised no documented students but collaborates on projects involving healthcare technology and educational systems. His involvement in the Dragonfly project suggests affiliation with cross-disciplinary teams focused on interactive companions and affect-sensitive technologies. Research labs or groups are not explicitly mentioned, but his work aligns with Queen Mary's broader initiatives in AI and healthcare tech.

Frans JFM Feldbergمشاهده پروفایل

استاد

• Data-Driven Business Innovation
• Big Data Analytics
• Artificial Intelligence
• +۴ مورد دیگر

Frans JFM Feldberg is a Full Professor at the KIN Center for Digital Innovation , School of Business and Economics, Vrije Universiteit Amsterdam. His research focuses on data-driven business innovation , value creation from big data/analytics/AI, and the economics of artificial intelligence. Co-founder of Amsterdam Center for Business Analytics (ACBA) and Data Science Alkmaar Active in digital transformation projects for SMEs through EDIH NWNL initiative Principal Investigator in research projects on data-driven value propositions and innovation ecosystems His scientific work spans organizational learning , virtual environments , and strategic resource management , with publications in leading journals like Research Policy and Journal of Strategic Information Systems . He has received multiple academic awards including the JSIS Best Paper Award (2022) and Henry Chesbrough Best Student Paper (2022) . Teaches courses like Artificial Intelligence Lab and Digital Business Model Innovation Business consultant with expertise in data-driven strategy development for global firms

Sven Dummerمشاهده پروفایل

پژوهشگر

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

Sven Dummer is a Researcher at the Digital Society Institute, Mathematics of Imaging & AI group, and TechMed Centre, conducting interdisciplinary work at the intersection of computer vision, medical imaging, and mathematical AI. His roles emphasize developing computational frameworks for dynamic biological and medical imaging applications. His research focuses on Medical Imaging, Computer Vision, and Machine Learning, with specialized expertise in Neural Networks and Computational Anatomy. Key contributions include manifold learning for dynamic imaging sequences, SO(3)-equivariant neural representations for cellular modeling, and Riemannian geometry approaches to diffeomorphic image registration. His methodology consistently integrates geometric deep learning with clinical imaging challenges. Analysis of recent publications reveals a cohesive trajectory toward neural implicit representations for spatiotemporal medical data, particularly in cardiovascular motion estimation and live-cell microscopy. Work consistently leverages optimal transport theory and manifold learning to address dynamic imaging challenges, with increasing emphasis on equivariant architectures for biological structure modeling. No scientific awards were documented in the source material. No information regarding student supervision or research grants was provided in the available text. Dummer operates within the TechMed Centre (focused on translational medical technology) and Digital Society Institute (addressing societal digitalization), contributing to the Mathematics of Imaging & AI group's mission of developing mathematically rigorous AI for imaging science. His collaborative network spans computational biology, cardiology, and computer vision domains.

Michael Shahمشاهده پروفایل

مدرس ارشد

• Computer Science Education
• Software Visualization Tools
• Performance Analysis
• +۳ مورد دیگر

Michael Shah is a Senior Lecturer of Computer Science at Yale University's School of Engineering & Applied Science. His research focuses on software visualization tools, performance analysis frameworks, and innovative computing education methodologies. He has contributed to fields such as parallel computing, GPU execution visualization, and asynchronous programming patterns. Dr. Shah's work bridges technical software development with educational applications, including curriculum design for introductory graphics courses and teaching assistant training programs. His research emphasizes practical tool development for debugging and performance optimization in both academic and industry contexts. Notable projects include the Daisen GPU visualization framework, DrAsync for JavaScript anti-pattern detection, and the Iceberg static analysis tool for Java concurrency issues. His publications span topics from procedural content generation in games to runtime performance bug mitigation strategies.

Tyler Calkinمشاهده پروفایل

دانشیار

• Art and Technology
• Digital Media
• Virtual Reality
• +۷ مورد دیگر

Tyler Calkin is an Associate Professor and Head of Digital Media at the University of Nevada, Reno within the Department of Art, Art History and Design. He received his MFA in Art and Integrated Media from the California Institute of the Arts in 2011 and his BA in Art from Whitman College in 2009 (with Minor in English, Magna Cum Laude). Prior to joining UNR, he taught in the Program in Art and Technology and The Center for Integrated Media at CalArts, where he developed an experimental VR lab. Calkin's interdisciplinary practice spans objects, performance, video, augmented and virtual reality, machine learning, and motion capture, often in recursive combinations. His research focuses on artificial intelligence and machine learning, human-robot interaction, 3D modeling and fabrication with motion capture, and XR interactivity and social experience. He constructs interactive and immersive propositions for interpersonal experience, responding to current developments in consumer technologies and pandemic-era tech practices. His recent body of work demonstrates a strong focus on virtual reality, motion capture, and digital fabrication, with numerous international exhibitions including venues in Berlin, London, Venice, Beijing, Kathmandu, Seoul, Mexico, New York, and Los Angeles. His 2022 work 'Agreement Collapse' explores climate change through a gravity simulation featuring a pink mocap model in a blue cloudy sky, commenting on international climate accords. His publications and presentations reveal an ongoing exploration of digital interfaces, social interactions in virtual spaces, and the philosophical implications of emerging technologies. Calkin teaches critical theory and experimental practices in video, immersive and interactive media, and digital fabrication. His teaching emphasizes critical discourse and experimental approaches to creative coding, performance, and game design. As Area Head of Digital Media, he leads curriculum development in emerging digital art forms and technologies.

Martial Hebertمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Autonomous Systems
• +۶ مورد دیگر

Martial Hebert is the Dean and University Professor of Robotics at Carnegie Mellon University's School of Computer Science (SCS), leading since August 2019. His career spans decades at CMU's Robotics Institute (RI), where he served as Director (2014-2019) and Professor (1999-present). Broad research interests in computer vision, perception for autonomous systems, and 3D environment modeling. Current PhD advisees include Zhipeng Bao, with numerous past PhD and Master's students listed. Editor-in-Chief of the International Journal of Computer Vision and member of IEEE Robotics and Automation Society. His research focuses on computer vision and robotics , emphasizing 3D data interpretation, object recognition, and machine learning applications. Recent articles highlight advancements in 3D vision , diffusion models , and disaster response robotics , reflecting a trajectory from foundational algorithms to applied autonomous systems. Notably, he pioneered the first master's program in computer vision in the U.S. Hebert's leadership in academic and research spheres includes directing the RI and securing an operating budget peak during his tenure. His work bridges perception, intelligence, and autonomous systems, with applications in disaster scenarios , LiDAR point cloud detection , and video forecasting .

• Autonomous Vehicles
• Robotic Perception
• Sensor Fusion
• +۲ مورد دیگر

Eduardo Nebot is Emeritus Professor and former Patrick Chair in Automation and Logistics at the University of Sydney, where he founded the Australian Centre for Robotics. His research develops perception and navigation systems for autonomous vehicles, focusing on robust operation in complex environments. Nebot's work enables autonomous systems for mining, transportation, and field robotics applications. Research interests include sensor fusion, cooperative perception, probabilistic tracking, and validation methods for autonomous systems. Current projects investigate V2X-enabled cooperative driving, pedestrian trajectory prediction, and robust localization under environmental changes. Publication trends highlight multi-sensor perception systems, with recent work emphasizing domain adaptation for 3D detection, safety validation frameworks, and human-robot interaction in autonomous driving. Articles consistently address real-world deployment challenges in industrial and urban settings. Fellow of the Australian Academy of Technology and Engineering (2016) Fellow of the IEEE (2016) The Australian Centre for Robotics collaborates with industry partners on autonomous haulage systems and intelligent transportation. Nebot has supervised numerous PhD candidates in robotics and maintains research partnerships with mining and automotive sectors.

Antoine Guittonمشاهده پروفایل

استاد مدعو

• Geophysics
• Seismic Imaging
• Full-Waveform Inversion
• +۳ مورد دیگر

Antoine Guitton is an Adjunct Professor at Stanford University, specializing in geophysical signal processing and seismic imaging. His research focuses on advanced seismic data processing techniques, including full-waveform inversion, compressive sensing, and the mitigation of artifacts in seismic surveys. He is affiliated with Stanford's geophysics program and can be contacted at aguitton@stanford.edu. Key research interests include wavefield tomography, anisotropic media analysis, and machine learning applications in geophysics. His work addresses challenges such as Doppler effect correction in marine vibrator data, nonstationary statistical modeling, and fault classification using distributed acoustic sensing (DAS). Recent contributions include studies on salami publications in scientific ethics and the optimization of seismic data acquisition systems under field constraints. His publications span over two decades, demonstrating expertise in both theoretical and applied aspects of geophysical exploration.

Stefano DI TOREمشاهده پروفایل

استاد

• Educational Technology
• Artificial Intelligence in Education
• Virtual Reality (VR) and Augmented Reality (AR)
• +۶ مورد دیگر

Stefano DI TORE is a Full Professor at the Department of Political and Social Studies (DISPS) at the University of Salerno. His work focuses on leveraging emerging technologies like AI, VR/AR, and 3D printing to enhance educational practices, cultural heritage preservation, and inclusive learning environments. He actively contributes to projects such as ScanItaly, which digitizes archaeological artifacts for accessibility, and develops edugames like 'Virtual Class' and 'Trot' to improve perspective-taking and emotional intelligence. His research also explores robotics (e.g., ASD-Robot) for supporting children with autism spectrum disorder. He maintains active roles in teacher training, media education, and international collaborations through Erasmus+ programs. Education and Professional Background: While specific educational qualifications aren’t explicitly listed, his professorial role and extensive publication history in interdisciplinary fields indicate advanced academic credentials in educational technology and social sciences. Research Interests: Stefano’s work bridges technology and pedagogy, emphasizing: - AI-driven inclusion tools in schools and museums - VR/AR applications for cultural heritage and classroom innovation - Robotics and edugames for neurodiverse learners - Digital twin technologies for historical preservation - Teacher training in emerging technologies Labs/Teams: Leads projects in the DISPS department, including the Lab-H (focused on 3D printable educational resources) and collaborates on initiatives like ScanItaly. His work often involves interdisciplinary teams across education, computer science, and cultural studies. Grants/Advising: No specific grants listed, but his involvement in PNRR orientation paths and international agreements suggests funded projects. Advising records aren’t detailed in the provided texts.

• Medical Imaging
• Pediatric Cardiology
• MRI Reconstruction
• +۳ مورد دیگر

Dr. Qing Zou is an Assistant Professor in the Division of Pediatric Cardiology, Department of Pediatrics at the University of Texas Southwestern Medical Center (UTSW). He holds secondary appointments in the Department of Radiology and the Advanced Imaging Research Center (AIRC). His research focuses on medical imaging techniques, particularly magnetic resonance imaging (MRI), with an emphasis on pediatric cardiology applications. Dr. Zou’s work integrates deep learning and advanced reconstruction algorithms to address challenges in dynamic imaging, motion compensation, and undersampling problems. Education: Ph.D. in Mathematics/Computer Science (University of Iowa) Postdoctoral Training in Cardiac MRI (University of Iowa) Research Interests: Dr. Zou explores cutting-edge methodologies in MRI reconstruction, including deep learning-driven approaches for free-breathing and non-contrast-enhanced imaging. His projects address pediatric-specific needs, such as congenital heart disease diagnostics and rapid whole-heart angiography. He also investigates mathematical foundations of sampling theory and high-dimensional data analysis. Recent Research Trends: His publications highlight advancements in motion-resolved imaging, Gaussian representation-based reconstruction, and generative models for dynamic MRI. Key themes include improving temporal resolution, reducing imaging time, and enhancing vascular visualization without contrast agents. Awards: No honors or awards explicitly listed in the provided text. Advising & Grants: No formal advisees/students or grant details mentioned. His research is likely funded through institutional resources or collaborative grants. Labs/Teams: Dr. Zou is affiliated with the Advanced Imaging Research Center (AIRC) at UTSW, a hub for innovative imaging technologies and interdisciplinary research.

Ian G Andersonمشاهده پروفایل

مدرس ارشد

• Digital Humanities
• Information Systems
• Cultural Heritage
• +۵ مورد دیگر

Dr. Ian G Anderson is a Senior Lecturer in Information Studies and Head of Subject at the University of Glasgow. His academic journey began at the University of Glasgow, where he earned an MA (SocSci) in Economic and Social History, followed by an MPhil in History and Computing and a PhD in Economic History. He has held roles such as Academic Support Officer for the TLTP History Courseware Consortium and Lecturer in New Technologies for the Humanities before his current position. His research focuses on user interactions with information systems in archives, libraries, and museums, digital content creation, and the impact of Web 2.0 technologies. His notable projects include evaluations of the Cymru1914 site, the Kelvin Hall Redevelopment Project (involving opencollections.org.uk), and the Copyright and Risk project with CREATe. He has also contributed to the 3D-COFORM EU project for cultural heritage digitization and the Mapping the Practice of Sculpture in Britain and Ireland database. His teaching spans undergraduate and postgraduate courses, including Enterprise, Creativity and Citizenship, and Online Document Encoding. Anderson has collaborated internationally with institutions like the University of North Carolina’s School of Information and Library Science and has been involved in editorial boards and advisory committees, including Program: Electronic Library and Information Systems and AHDS History. His work emphasizes interdisciplinary approaches to digital humanities and user-centered design in cultural heritage.

• Medical Imaging
• MRI Physics
• Quantitative MRI
• +۴ مورد دیگر

Jie Deng, Ph.D., is a Professor in the Department of Radiation Oncology at UT Southwestern Medical Center, with dual affiliation in the Division of Medical Physics & Engineering. She holds certifications from the American Board of Medical Physics and serves as a Magnetic Resonance Safety Officer. Her expertise spans MRI physics, quantitative MRI, oncological imaging, and AI applications in medical imaging. Education: Bachelor's in Biomedical Engineering, Southeast University (China) Master's in Bioengineering, University of Illinois at Chicago Ph.D. and Master of Science in Law from Northwestern University Research Focus: Dr. Deng specializes in advanced MRI technologies for radiation therapy, imaging biomarkers, and AI-driven medical imaging solutions. Her work bridges clinical and research applications, emphasizing MR-guided radiotherapy and adaptive treatment strategies. Publications: Over 58 peer-reviewed articles focus on MRI artifact reduction, AI-driven tumor classification, and synthetic CT generation for radiotherapy planning. Recent work explores 4D-MRI reconstruction and deep learning models for fetal brain imaging localization. Affiliations: Active member of the International Society for Magnetic Resonance in Medicine (ISMRM) and American Association of Physics in Medicine (AAPM). Serves on UT Southwestern’s MRI Safety Committee. Teaching & Mentorship: Mentors medical physics residents and graduate students in MR-only simulation procedures and imaging fundamentals.

Olivier Chadebecمشاهده پروفایل

استاد پژوهش

• Computational Electromagnetics
• Electrical Energy Conversion
• Numerical Modeling
• +۶ مورد دیگر

Olivier Chadebec is a CNRS Research Director at G2Elab, the power electrical engineering research department of Université Grenoble Alpes in France. He leads the 'Models, Methods and Methodologies Applied to Electrical Engineering' research team (MAGE group) and the ERT-CMF (Low Magnetic Fields Technological Research Group) at G2Elab. He was involved in creating the International Laboratory 'James Clerk Maxwell' in collaboration with the University of Lyon and Brazilian universities. Chadebec received his engineer and Ph.D. degrees in Electrical Engineering from the Grenoble Institute of Technology in 1997 and 2001. After a post-doctorate with Schneider Electric, he joined CNRS in 2003 as a Research Associate. He received his 'Habilitation à Diriger les Recherches' in 2011 and became a Research Director in 2015. He also spent a year in 2012 as a research associate at the Federal University of Santa Catarina in Brazil. His research focuses on computational electromagnetics applied to electrical energy conversion, developing numerical models, algorithms, and simulation tools for electromagnetic device analysis. His key research areas include finite element methods, integral methods, inverse problems, and low magnetic field metrology. He actively contributes to the development of the MIPSE platform commercialized by Altair Engineering via Flux software. His recent publications (2023-2025) show a strong focus on advanced computational methods for electromagnetic problems, including multiscale modeling, tensor compression techniques, FEM-BEM coupling for magnetoelectric effects, and optimization algorithms for electrical machine design and fuel cell diagnostics. His work demonstrates a consistent progression toward more efficient computational approaches for complex electromagnetic problems. Chadebec has supervised over 30 PhD students since 2006, with thesis topics spanning computational electromagnetics, inverse problems, fuel cell diagnostics, and submarine magnetic signature analysis. His research has significant applications in electrical machine design, fuel cell technology, submarine degaussing, and electromagnetic compatibility. He leads the MAGE research team and the ERT-CMF (Low Magnetic Fields Technological Research Group) at G2Elab, and has been instrumental in developing the MIPSE simulation platform used in industry through collaboration with Altair Engineering.