جست‌وجوی استاد و پژوهشگر

Ingela Nyströmمشاهده پروفایل

استاد

• Medical image analysis
• 3D visualization
• Haptics in surgery planning
• +۳ مورد دیگر

Ingela Nyström is a Professor in Visualization at Uppsala University's Department of Information Technology. She serves as the node coordinator for InfraVis and as Director of Postgraduate Studies at the Department of Information Technology. Her interdisciplinary work bridges Uppsala University's three academic domains through collaborations with the Centre for Image Analysis (CBA), Centre for Women's Mental Health (WOMHER), Uppsala Centre for Digital Humanities (CDHU), and Medtech Science & Innovation (MTSI). Medical image analysis 3D visualization Haptics in surgery planning Interactive segmentation Digital geometry Biomedical engineering Her recent research focuses on rotation-equivariant neural networks for biomedical image classification, interactive segmentation tools for cranio-maxillofacial surgery planning, and precision evaluation of intraoral scanning technologies. Publications since 2023 demonstrate continued work on 3D imaging protocols for implant dentistry and surgical applications. 2024: Equivariant CNNs for rotation-invariant biomedical imaging 2023: In vivo precision studies of full-arch implant scans 2021: Virtual surgical planning with haptic assistance 2016-2017: Multimodal robotics perception and 3D segmentation tools 2014: Orbital morphology analysis in craniosynostoses 2005-2011: Foundational work in 3D skeletons and fuzzy object measurements

Michelle Osbornمشاهده پروفایل

دانشیار

• Functional Morphology
• Evolutionary Morphology
• Biomechanics
• +۴ مورد دیگر

Michelle Osborn is an Associate Professor in the Department of Comparative Biomedical Sciences at Louisiana State University's School of Veterinary Medicine. Her research applies functional and evolutionary morphology to study tissue degeneration in vertebrates, particularly horses, using 3D/4D biomechanical modeling and biotensegrity frameworks. Education PhD, Louisiana State University, 2013 MA, Louisiana State University, 2008 BA, University of Nevada, Las Vegas, 2001 Research Focus Dr. Osborn investigates causal relationships between body movements and structural degradation via force analysis of CT-generated models. Key projects include analyzing equine skeletomusculofascial systems and horse-rider biomechanics. Her work integrates clinical diagnostics with advanced visualization techniques. Publications Her recent articles (2020-2023) emphasize biomechanics, medical imaging optimization, and comparative anatomy, demonstrating consistent use of computational modeling across diverse vertebrate systems. Awards Dean’s Teacher Merit Honor Roll (2017-2023) Faculty Service Award (2020) Outstanding Teacher recognition from student cohorts (2017-2019) FASEB Capitol Hill Day invitee (2020) Grants & Advising She mentors NIH Veterinary Scholars and secured funding from DoD and LSU equine health programs. Current projects focus on degenerative joint disease and kinematic analysis. Lab Dr. Osborn directs a research group specializing in biomechanical analysis and anatomical visualization, though no formal lab name or URL is specified.

• Artificial Intelligence
• Machine Learning
• Constraint Optimization
• +۸ مورد دیگر

Dr. James Atlas is a Senior Lecturer in the Department of Computer Science and Software Engineering at the University of Canterbury's Faculty of Engineering, where he has been employed since July 2018. His primary affiliations include full-time academic responsibilities at this institution. His research spans: Core CS : Artificial intelligence, machine learning, constraint optimization Systems : Distributed and high-performance computing Applied domains : Medical data analysis (CT reconstruction, health prediction), earth/space exploration (flood mapping, environmental monitoring), and multi-agent systems Education : Curriculum development, threshold concept gamification Recent publication analysis (2019-2025) reveals dominant themes in deep learning applications for medical imaging (spectral CT reconstruction, bioacoustic analysis) and environmental sensing (3D tree modeling, vineyard monitoring, flood prediction), with consistent interdisciplinary collaboration. He actively supervises 10 graduate students researching: Machine learning for global flood exposure mapping CT reconstruction algorithms and artifact correction EMG-EEG hybrid prosthetics Hierarchical reinforcement learning Automated 3D digital tree modeling Bioacoustic monitoring systems

• Evolutionary Computation
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Tiago Martins is an active academic at the University of Coimbra where he serves as an Invited Assistant Professor in the Department of Informatics Engineering and researches at the Computational Design and Visualization Lab (CMS/CISUC). His academic affiliations include current and previous positions at the University of Coimbra and Universidade Católica Portuguesa. Martins holds three degrees from the University of Coimbra: PhD in Information Science and Technology (2013-2021) MSc in Design and Multimedia (2011-2013) BS in Design and Multimedia (2008-2011) His research explores the convergence of computational methods and creative practices with focus areas in: Evolutionary computation applied to design systems Generative design methodologies Computational creativity frameworks Machine learning for artistic applications Prototyping computational artifacts Recent publications demonstrate strong focus on evolutionary algorithms in creative applications, generative adversarial networks, computational typography, and AI-powered design systems. His work consistently bridges technical innovation with artistic expression. Martins has received academic recognition including: The 3% Best Students Award (2012/2013) The 3% Best Students Award (2010/2011) He has secured research funding from: Portuguese Foundation for Science and Technology Imprensa Nacional-Casa da Moeda SA QREN Mais Centro Program At the Computational Design and Visualization Lab, Martins develops generative systems exploring the intersection of computation, design, and art. His installations and artworks have been exhibited internationally, showcasing applications of his research in computational creativity.

Ali Sekmenمشاهده پروفایل

استاد

• Approximation Theory
• Sampling Theory
• High-Dimensional Data Analysis
• +۲ مورد دیگر

Dr. Ali Sekmen serves as Professor and Chair of the Department of Computer Science within the College of Engineering at Tennessee State University, where he has held leadership positions since joining in 1998. He additionally contributes to university governance as a member of the Tennessee State University Board of Trustees. His academic credentials include dual Ph.D. degrees from Vanderbilt University in Electrical Engineering (2000) and Mathematics (2012), complemented by an MS in Mathematics (2009) from Vanderbilt and undergraduate/postgraduate engineering degrees from Bilkent University. This unique interdisciplinary background fuels his research at the intersection of theoretical mathematics and practical computing. Dr. Sekmen's research program centers on Approximation Theory, Sampling Theory, High-Dimensional Data Analysis, Machine Learning, and Robotics, with particular emphasis on subspace segmentation algorithms and their real-world implementations. His work demonstrates a consistent pattern of bridging abstract mathematical concepts with tangible engineering applications, especially in robotics systems and data analysis frameworks. The publications spanning 2012-2019 reveal increasing focus on deep learning integration with traditional mathematical approaches for handling complex datasets. As an active Principal Investigator, Dr. Sekmen has secured substantial funding from major agencies including NSF, NASA, USDA, and the Department of Defense. His current projects include USDA-funded water infrastructure robotics and Army-sponsored subspace segmentation research, demonstrating sustained competitiveness in federal grant acquisition. His teaching portfolio spans foundational computer science courses to specialized topics in machine learning and robotics, reflecting his commitment to both theoretical and applied education.

• Artificial Intelligence
• Human Behavior Analysis
• Human-Robot Interaction
• +۲ مورد دیگر

Dr. Bangli Liu serves as a Senior Lecturer at De Montfort University's School of Computer Science and Informatics, Leicester, UK. Holding a PhD in Computer Science from the University of Portsmouth (2018), she brings extensive experience from prior lecturing and postdoctoral roles at the University of Portsmouth and Loughborough University, including a research visit to Tokyo Metropolitan University. Education: PhD in Computer Science, University of Portsmouth, UK (2018) Her research centers on Artificial Intelligence, Human Behavior Analysis, and Computer Vision , with significant contributions to human-robot interaction systems and deep learning applications. Key innovations include RGB-D based action recognition frameworks and eye-tracking algorithms, driving advancements in elderly care robotics and autism intervention platforms through industry-collaborative projects. Publications from 2016-2022 reveal a focused trajectory in computer vision, with 7 high-impact journal papers (including Pattern Recognition and IEEE Transactions) emphasizing human behavior analysis, image processing, and robotics applications. Her work consistently addresses real-world challenges in activity recognition and sensor-based interaction systems. Scientific Awards: IEEE Student Travel Grant Award (IAPR, Hong Kong, 2017) IEEE SMC Student Travel Grant (Budapest, 2016) As Principal Investigator, she secured £26,040 in research funding including a Royal Society grant for elderly care robotics and DMU funding for autism intervention platforms. Her leadership extends to organizing IEEE workshops and contributing to the IEEE Press Handbook, while teaching core modules in Big Data and Machine Learning. Dr. Liu actively contributes to the Institute of Artificial Intelligence (IAI) at DMU, where her lab develops vision-based systems for human-robot collaboration in healthcare contexts.

• Data visualisation
• Visual Analytics
• Information Visualisation
• +۴ مورد دیگر

Christy Jie Liang is an Associate Professor at the School of Computer Science, University of Technology Sydney (UTS), where she leads the Data Visualisation Research Lab in the Visualisation Institute. With extensive experience in both academic and industry settings, including appointments at IBM and Peking University, she has established herself as a leading researcher in data visualization and visual analytics. Dr. Liang earned her PhD in Data Visual Analytics from UTS, where she was awarded the University Medal with First Class Honours. Her educational background includes a Bachelor of Information Technology (First Class Honours) also from UTS. Professor Liang's research focuses on data visualization and visual analytics, with particular emphasis on information visualization, narrative visualization, and the application of these techniques to real-world problems. Her work spans multiple domains including finance, food safety, biomedical applications, smart cities, and social media. She has developed novel visualization techniques and owns five intellectual properties in this field. Her recent publications demonstrate a clear trajectory toward more sophisticated visualization techniques that integrate machine learning, with increasing focus on narrative visualization, user engagement across demographics, and practical applications in domains such as public health and education. The interdisciplinary nature of her work is evident in collaborations across computer science, behavioral science, and domain-specific applications. Dr. Liang has received significant recognition for her work, including: University Medal with First Class Honours from UTS Capital Markets CRC Honours scholarship Australian Postgraduate Awards As an educator, Professor Liang coordinates core subjects for Bachelor of Information Technology, Bachelor of Computer Science with Honours, Master of Interaction Design, and Master of Business Analytics programs. She has recently developed enterprise learning courses including short courses and micro-credentials in data visualization education. Her leadership extends to service roles as associate editor for JVLC and Journal Visual Informatics, program committee member for numerous conferences, and advisory board member for the Australian Computer Society and Peking University Medical Visualization Centre. Professor Liang leads the Data Visualisation Research Lab, which focuses on developing innovative visualization techniques and applying them to real-world problems. The lab maintains strong industry connections, with collaborations spanning government agencies, academic institutions, and commercial enterprises across multiple continents.