جست‌وجوی استاد و پژوهشگر

Michael A. Chapmanمشاهده پروفایل

استاد

• Image Processing
• 3D Topographic and Built Environment Modelling
• Deformation Measurement and Analysis
• +۷ مورد دیگر

Dr. Michael A. Chapman serves as a Professor in the Department of Civil Engineering at Toronto Metropolitan University, specializing in image processing, deformation analysis, and sensor-integrated geospatial modeling for infrastructure applications. His work bridges civil engineering with advanced computational techniques for real-world problem solving. His academic credentials include a BT from Toronto Metropolitan University (1977), MSc from Ohio State University (1979), and PhD from Laval University (1989). BT: Toronto Metropolitan University (1977) MSc: Ohio State University (1979) PhD: Laval University (1989) Chapman's research centers on deformation monitoring of structures like the Rogers Centre roof, mobile mapping for road condition assessment, and sensor fusion for precision geospatial models. He pioneers applications in pavement deflection measurement using Doppler lasers and mobile laser scanning for infrastructure inspection, emphasizing practical engineering solutions derived from photogrammetry and image metrology. His methodology transforms mechanical observation into digital innovation for civil infrastructure management. His publication portfolio reveals a strong trajectory in merging deep learning with geospatial engineering, particularly in hyperspectral image classification and mobile mapping systems. These works consistently address civil infrastructure challenges through advanced computational approaches, demonstrating evolving sophistication from pavement crack extraction to sea ice mapping. His distinguished recognition includes: Wild Heerbrugg Photogrammetric Award - North America (1981) Chapman actively supervises graduate students and teaches core courses including CVL 207 (Graphics), CVL 352 (Geomatics Measurement Techniques), and CV8506 (Industrial Metrology). He emphasizes adaptive pedagogy to accommodate diverse learning styles, viewing teaching as both professional duty and personal passion. His industry-relevant research often involves partnerships with transportation authorities for real-time infrastructure assessment. His laboratory work focuses on mobile mapping systems and sensor integration platforms for deformation monitoring, particularly applied to large-scale structures and transportation networks. Current projects involve real-time pavement assessment technologies and 3D modeling of built environments using multi-sensor fusion approaches.

Ryan T. Whiteمشاهده پروفایل

دانشیار

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.

Yang Liمشاهده پروفایل

استادیار

• Computer Architecture
• Machine Learning
• On-Device AI
• +۴ مورد دیگر

Yang Li is an Assistant Professor of Computer Science at Iowa State University, specializing in computer architecture, machine learning, and their intersection. He holds a Ph.D. and M.S. from Carnegie Mellon University (2020), an M.S.E. from the University of Texas at Austin (2013), and a B.E. from Tsinghua University (2011). Prior to academia, he worked as a Senior Research Scientist at Meta, a Research Scientist at Meta, and a Researcher at Microsoft. His research focuses on large language models (LLM) acceleration, on-device AI, cloud infrastructure optimization, and spatiotemporal forecasting. He has contributed to over 20 peer-reviewed publications at top venues like ASPLOS, EMNLP, and ICASSP. Research Interests: Algorithmic and systems-level acceleration of LLMs On-device AI co-design and privacy Cloud memory/power management Graph-based spatiotemporal forecasting Teaching: Taught COMS 6730 (Advanced Topics in ML), COM S 321 (Computer Architecture), and guest-lectured on graph signal processing. Recent teaching scores include 4.75/5.0 (Fall 2024) and 4.67/5.0 (Spring 2025). Awards: IBM Patent Application Award (2021) and multiple patents on power management systems for data centers. Service: Program committee member for DAC 2024, NeurIPS 2024, and ICLR 2025. Reviewer for ACM TACO, IEEE TPAMI, TPDS, and others.

Sheng Liمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۴ مورد دیگر

Sheng Li is an Adjunct Assistant Professor in the School of Computing at the University of Georgia (UGA). He holds roles such as Graduate Program Faculty and Courtesy Faculty in the Institute of Bioinformatics. Previously, he served as an Assistant Professor at UGA from August 2018 to July 2022. His research focuses on Machine Learning, Computer Vision, Data Mining, Natural Language Processing, Causal Inference, and User Modeling. Li earned his Ph.D. in Computer Engineering from Northeastern University in 2017, following degrees from Nanjing Institute of Post and Telecommunications in China. His work bridges theoretical advancements and practical applications, including AI solutions for healthcare (e.g., medication adherence monitoring), computer vision for wildlife tracking (fish re-identification), and causal inference methodologies. He has received notable awards like the Fred C. Davison Early Career Scholar Award (2022) and the Aharon Katzir Young Investigator Award (2020). His research is funded by grants from agencies such as the US Department of Defense and NIH, supporting projects like Knowledge-Guided Scene Graph Generation and Reasoning for Visual Understanding. Li’s publications span top venues in AI and computer science, addressing challenges in domain adaptation, trustworthy AI, and multimodal learning. He has collaborated on interdisciplinary projects, including bioinformatics and agricultural NLP. His contributions emphasize both technical innovation and societal impact.

Yu Liangمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Dr. Yu Liang is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on advancing computer vision and machine learning techniques, particularly in video generation, image restoration, and event-based systems. He specializes in developing innovative frameworks for tasks like motion-aware video synthesis, low-light image enhancement, and diffusion models applied to multimedia processing. Key research interests include hierarchical information flow architectures, transformer-based networks, and scalable solutions for real-world imaging challenges. His work emphasizes practical applications of generative models and cross-modal fusion techniques to achieve high-quality visual outputs. Dr. Liang has contributed extensively to the field through publications on topics such as Fractal-IR for image restoration, Uni3C for 3D-enhanced video generation, and event-based frame interpolation. His research often bridges theoretical advancements with practical implementations, addressing issues like temporal coherence, motion deblurring, and adaptive illumination estimation. Although no specific academic awards are listed, his prolific publication record (over 20+ papers since 2019) highlights his active role in the academic community. His work has led to impactful datasets like Lsdir and frameworks like SwinIR, demonstrating strong contributions to both methodology and infrastructure in computer vision.

• Computer Vision
• Photometric Stereo
• 3D Reconstruction
• +۳ مورد دیگر

Fumio Okura is a professor at Osaka University , specializing in Computer Vision and 3D Reconstruction . His work bridges Photometric Stereo , Neural Rendering , and Medical Imaging , with a focus on cognitive decline prediction and plant modeling . He collaborates extensively with researchers like Hiroaki Santo and Yasuyuki Matsushita . Education: Ph.D. in Computer Science (Osaka University) Research Interests span Computer Vision , Photometric Stereo , 3D Reconstruction , and Biomedical Applications . His recent work includes HoGS for object reconstruction and TreeFormer for botanical structure estimation. Publications trend toward neural rendering , reflectance modeling , and augmented reality . Notable contributions include PPGCN for cognitive detection and MVCPS-NeuS for multi-view photometric stereo. Labs & Collaborations include the Osaka University Computer Vision Lab , working with teams on photometric analysis and medical imaging .

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Sio Kei Im is an active researcher with a focus on computer science, machine learning, and human-computer interaction. His recent work spans multiple domains including image processing, quantum computing, and virtual reality. Publications address advanced data augmentation (LogicMix), multi-modal quantum watermarking (MMQW), and efficient neural decoding algorithms (TRHyper). Research interests include time series optimization, dialogue summarization, and haptics in VR environments. Collaborations with experts in linguistics, electrical engineering, and software development indicate interdisciplinary expertise. Key contributions involve adaptive algorithms for AI model protection, speaker recognition systems, and real-time 3D rendering techniques.

Hsiang-Yun Wuمشاهده پروفایل

پژوهشگر ارشد

• Information Visualization
• Visual Analytics
• Biological Network Visualization
• +۱۳ مورد دیگر

Hsiang-Yun Wu is a Research Fellow at the Computer Graphics department of Vienna University of Technology (TU Wien), actively contributing to information visualization and visual analytics. Her work spans biological network visualization, graph drawing, schematic network maps, and data physicalization, with a focus on interactive techniques and human-centric design. Projects: ArtVis (2022–2027) , SANE (2024–2027) , SMGV-Esprit (2024–2027) , and HumAlgo (2018–2023) . Affiliations: Member of the Visualization Group at TU Wien. Her recent publications emphasize uncertainty visualization, network physicalization, and dynamic graph representations. Notably, she received the EuroVis 2019 Honorable Mention Award for optimizing stepwise animations. Wu supervises diploma theses in areas like metabolic pathway visualization and semantic-aware character animation. Key research trends include integrating biological data with urban-style schematic maps ( Metabopolis ), physicalization workflows for anatomical education ( Slice and Dice ), and mixed labeling strategies in 3D environments.

• Virtual Reality (VR)
• Human-Computer Interaction (HCI)
• Accessibility in Technology
• +۳ مورد دیگر

John Quarles is a Professor at the University of Texas at San Antonio, specializing in Virtual Reality (VR) and Human-Computer Interaction. His research focuses on accessibility in immersive technologies, cybersickness mitigation, and inclusive design. He has contributed over 100 publications across top venues like IEEE VR, ISMAR, and IEEE Transactions on Visualization and Computer Graphics. His work addresses challenges faced by users with disabilities, such as balance impairments and mobility limitations, through innovative feedback systems and adaptive algorithms. Quarles has co-authored influential papers on cybersickness prediction, VR accessibility for persons with Multiple Sclerosis, and disability simulations to reduce societal bias. He has held leadership roles including Program Chair for IEEE VR 2023, demonstrating his influence in academic and industrial VR communities. His research spans interdisciplinary applications in healthcare, education, and rehabilitation, with notable collaborations on datasets like 'Mazed and Confused' and frameworks like SmoothRide. Key themes include multimodal feedback methods, user-centric design principles, and leveraging AI for personalized VR experiences.

Selin Ahipasaogluمشاهده پروفایل

استاد

• Operational Research
• Convex Optimization
• Robust Optimization
• +۵ مورد دیگر

Prof. Selin Damla Ahipasaoglu is a Professor in Operational Research at the University of Southampton's School of Mathematical Sciences . She serves on the management team of the UKRI CDT SustAI (Artificial Intelligence for Sustainability) as Senior Tutor and Co-Lead for the Transportation and Logistics Theme . Her work bridges mathematical optimization with practical applications in sustainability, finance, and transportation systems. Research Interests : Convex Optimization Robust Optimization Discrete Choice Theory Experimental Design Machine Learning Current Research : Focused on robust optimization and its applications in discrete choice modeling, portfolio optimization, and transportation systems. She explores theoretical frameworks alongside real-world implementations, particularly through interdisciplinary projects like the UKRI CDT SustAI. Teaching : In the 2025/2026 academic year, she teaches MATH3017: Mathematical Programming and MATH2013: Operational Research II . She supervises PhD students in Mathematical Sciences, including Kexin Lai, Samuel Jericho Ward, and others.

• Computer Vision
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Adjunct Professor Shuai Wan is affiliated with the School of Engineering at RMIT University (City Campus, Australia). His research focuses on computer vision, machine learning, 3D point cloud compression, neural video coding, and remote sensing . Key contributions include lightweight deep learning frameworks for image/video compression, spatio-temporal context models for point clouds, and adaptive quantization techniques. Research Outputs Insights : Wan’s work spans 2024–2025 , emphasizing end-to-end deep learning solutions for challenges in Exemplar-based colorization with semantic attention Rendering-oriented 3D point cloud compression Slimmable video codecs with variable bitrate G-PCC standard enhancements for quantization and entropy coding Adversarial example detection in remote sensing Technical Domains : His articles intersect artificial intelligence, signal processing, and computer graphics , with applications in cloud gaming, SAR systems, and industrial data compression. Methods include transformers, attention networks, and 3D convolutional architectures .

• Computing Education
• Student Transitions
• Identity Formation
• +۳ مورد دیگر

Alison Varey is a Senior Lecturer at the School of Computing, Edinburgh Napier University , with a focus on computing education , student identity , and transitions from further to higher education . She contributes to research on graduate apprenticeships , sociability in online communities , and participatory research methods like photovoice. Research Areas: Student transitions, identity formation, work-integrated learning, digital skills gaps Projects: Associate Student Project (ASP), Graduate Apprenticeship studies Collaborators: Debbie Meharg, Ella Taylor-Smith, Sandra Cairncross, Sally Smith Her recent work examines hybrid professional identities and motivational factors in apprenticeships . She has secured funding from Skills Development Scotland and the Scottish Funding Council . Key publications include studies on student identity formation via photovoice and work-study balance in graduate apprenticeships. Advising: Supervised the thesis "A journey that motivates": Discovering the associate student experience (2015-2022).

• Machine Learning
• Computer Science
• Artificial Intelligence
• +۳ مورد دیگر

Amauri Holanda De Souza Junior is a Postdoctoral Researcher affiliated with the Department of Computer Science , focusing on Probabilistic Machine Learning . His work bridges theoretical advancements with practical applications in graph-based models. Active research areas include Graph Neural Networks , Persistent Homology , and Simulation-based Inference . His recent publications highlight innovations in: Topological data analysis for graph representations Robust statistical methods under model misspecification Scalable Bayesian inference frameworks Equivariant architectures for graph learning

• Computer Graphics
• Augmented Reality
• 3D Modeling and Reconstruction
• +۵ مورد دیگر

Kwan H. Lee is a prominent researcher in computer graphics and augmented reality, affiliated with the University of Alberta in Canada. With a publication record spanning over three decades (1990-2020), he has established himself as a leading figure in visual computing research with 56 documented publications. His research interests focus on Computer Graphics , Augmented Reality , 3D Modeling and Reconstruction , Computer Vision , and Cultural Heritage Computing . Dr. Lee's work demonstrates particular expertise in material appearance modeling, projection mapping techniques, and spatial augmented reality applications. His research has evolved from foundational work in manufacturing systems to cutting-edge applications in cultural heritage preservation and industrial visualization. Analysis of his recent publications (2015-2020) reveals a strong emphasis on practical applications of augmented reality in construction, museum exhibitions, and cultural heritage preservation. His work consistently bridges theoretical computer graphics concepts with real-world implementations, particularly in projection mapping systems and 3D reconstruction techniques. The publications show increasing focus on industrial applications and cultural heritage preservation in his later career. Dr. Lee has collaborated extensively with researchers from Korean institutions, suggesting strong international connections. His co-author network includes Yong Yi Lee, Jong Hun Lee, Min Ki Park, and other frequent collaborators who appear across multiple publications spanning more than a decade.

Dan Casasمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Dan Casas is a Senior Applied Scientist at Amazon in Seattle and an Associate Professor (Profesor Titular) on leave from King Juan Carlos University in Spain. His research spans the intersection of Computer Graphics, Computer Vision, and Machine Learning with a focus on 3D reconstruction, modeling, and animation of virtual humans and clothing. He has authored over 40 high-impact publications in top venues including SIGGRAPH, CVPR, and NeurIPS, and holds 3 international patents. Dr. Casas received his M.Sc. degree (2009) from Universitat Autònoma de Barcelona (Spain), including a research visit at Carnegie Mellon University. He earned his Ph.D. in Computer Graphics (2014) from the University of Surrey (UK), supervised by Prof. Adrian Hilton. He completed postdoctoral research at the University of Southern California's Institute for Creative Technology (2014-2015) and the Max Planck Institute in Saarbrücken (2015-2016). His research interests center on creating realistic virtual humans and digital clothing through advanced techniques in computer vision and machine learning. Casas has pioneered methods for 3D reconstruction of humans and garments from video input, physics-based simulation of soft-tissue deformations, and data-driven approaches to character animation. His work bridges the gap between theoretical computer graphics and practical applications in virtual reality, digital fashion, and immersive communication. Analysis of his recent publications reveals a consistent focus on human digitization, with increasing emphasis on machine learning approaches. His work has evolved from traditional computer graphics techniques toward neural representations and diffusion models, particularly in the areas of 3D garment simulation and human avatar creation. The trend shows growing integration of physics-based modeling with data-driven approaches to achieve both realism and computational efficiency. Marie Skłodowska-Curie Individual Fellowship (2015) FBBVA Leonardo Fellowship (2021) Medal from the Royal Academy of Engineering of Spain for Young Researcher Award (2023) i3 certification (outstanding researcher) from Spanish Ministry of Universities (2022) Winner of 2021 IEEE Retail Digital Transformation Grand Challenge Multiple Outstanding Reviewer Awards at top conferences (CVPR, BMVC, 3DV) Dan Casas has successfully advised multiple PhD students including Suzanne Sorli, Cristian Romero, Raquel Vidaurre, and Igor Santesteban (now at Meta Reality Labs), with several ongoing students including Melania Prieto-Martin, Gonzalo Gómez-Nogales, and Andrés Casado-Elvira. He has secured significant research funding as Principal Investigator, totaling over €1.2 million from Spanish Ministry of Science projects, EU H2020 programs, and industry fellowships including the FBBVA Leonardo Fellowship. His leadership extends to conference organization as Area Chair for ICCV 2023 and General Chair for ACM i3D 2020. Dr. Casas leads research in digital human modeling with applications in virtual reality, fashion technology, and immersive communication. His team develops advanced techniques for creating personalized 3D avatars from minimal input (like smartphone videos), addressing challenges in geometry, appearance, and physical simulation of virtual humans and their clothing.