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Yu PanView profile

Research Professor

• Graph Learning
• Manifold Learning
• Representation Learning
• +4 more

Yu Pan is a Research Assistant Professor in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln (UNL). He holds a Ph.D. in Computer Science from UNL, an M.S. in Information Management from Illinois Institute of Technology, and a B.S. in Computer Science and Technology from the University of Electronic Science and Technology of China. His research focuses on Graph Learning, Manifold Learning, Representation Learning, and Data Visualization, with applications in interdisciplinary fields such as biological systems and meteorology. He designs and maintains the Scientific Data Management Infrastructure for the Institute of Agriculture and Natural Resources (IANR), implementing FAIR principles to standardize data pipelines and enhance accessibility for researchers. His work also involves High Performance Computing (HPC) frameworks like MPI and CUDA to optimize data processing and analysis. Education: Ph.D., Computer Science, University of Nebraska-Lincoln M.S., Information Management and Technology, Illinois Institute of Technology B.S., Computer Science and Technology, University of Electronic Science and Technology of China Research Interests: Dr. Pan’s work bridges computational methods with scientific data challenges. He explores techniques to extract meaningful insights from complex datasets using graph-based approaches and manifold learning, while advancing visualization tools to aid domain scientists. His infrastructure projects emphasize scalable, FAIR-compliant systems to manage heterogeneous data across disciplines. Current collaborations with biological and environmental researchers aim to enhance data-driven decision-making through advanced analytics. Advising and Infrastructure: While no formal advisees are listed, his role in IANR’s data infrastructure project underscores his leadership in collaborative scientific computing. The infrastructure facilitates interdisciplinary research by integrating spatially and temporally co-located datasets, enabling researchers to explore patterns in agriculture, climate, and natural resources. Labs/Teams: He is affiliated with the Department of Biological Systems Engineering and contributes to the IANR’s data management initiatives, fostering cross-disciplinary innovation through computational solutions.

• Computer Vision
• Deep Learning
• Object Detection
• +3 more

Chunluan Zhou is a researcher at Nanyang Technological University (NTU), Singapore, with a PhD in Computer Vision and Deep Learning. His research focuses on object detection, pedestrian detection, transformer-based tracking, and occlusion reasoning. He has published extensively in top-tier conferences and journals such as ICCV, CVPR, ECCV, TIP, and TCSVT. Education: PhD from NTU under Prof. Junsong Yuan and Prof. Kai-Kuang Ma, M.Eng from Zhejiang University, and B.Eng from Harbin Institute of Technology. Research Interests: Computer Vision and Deep Learning, with emphasis on object detection, occlusion reasoning, transformer networks, and video analysis. His work addresses challenges like heavily occluded pedestrian detection, weakly supervised learning, and scene-debiasing action recognition. Professional Contributions: Active reviewer for top conferences (CVPR, ICCV, ECCV, NeurIPS) and journals (TIP, TCSVT, PR). His recent research includes scene-debiasing open-set action recognition (SOAR), cyclic self-training for object detection, and transformer-based visual tracking methods like AiATrack. Labs/Teams: Collaborates with Prof. Junsong Yuan's research group, focusing on computer vision and deep learning applications.

• Machine Learning
• Information Security
• Pattern Recognition
• +6 more

Dr. Yasser Alginahi is a Professor of Computer Science at Adrian College, USA. He holds a Ph.D. in Electrical and Computer Engineering from the University of Windsor, Canada (2004) and an M.Sc. in Electrical Engineering from Wright State University (1997). Previously, he served as an Associate Professor at Taibah University, Saudi Arabia (2005–2017), and as a research associate at the University of Windsor. His research focuses on machine learning, information security, pattern recognition, and 5G networks. He has authored over 80 publications and a book on document image analysis, and edited a volume on authentication technologies for cloud computing and IoT. Education: Ph.D., Electrical and Computer Engineering, University of Windsor, Canada (2004) M.Sc., Electrical Engineering, Wright State University, USA (1997) Research Interests: Machine Learning Applications in Cybersecurity IoT and SDN Network Security Pattern Recognition in Biomedical and Image Processing Cloud Computing Authentication Professional Affiliations: Senior Member, IEEE (2010–present) Senior Member, International Association of Computer and Information Technology (IACSIT) (2011–present) Member, International Association of Engineers (IAENG) (2013–present) Editorial Board Member of multiple journals His recent work emphasizes robust frameworks for intrusion detection in SD-IoT networks, plagiarism detection algorithms, and AI-driven wildfire prediction systems. He has also contributed to the design of e-payment systems and thermal conductivity modeling of nanofluids.

• Blockchain Technology
• Deep Learning
• Cybersecurity
• +4 more

Yuhang Ye is an Assistant Lecturer in the Department of Computer and Software Engineering at the Technological University of the Shannon: Midlands Midwest. Their research focuses on blockchain technology, cybersecurity, video streaming optimization, and deep learning. Key areas include smart contract-based systems, adversarial machine learning, and network security. Research Interests: Blockchain applications (e.g., payment channels, decentralized agents), cybersecurity (e.g., backdoor attacks, anomaly detection), and video processing (super-resolution techniques). Recent work explores efficient neural architectures and defense mechanisms against adversarial AI. Publications (2021–2025) highlight contributions to blockchain scalability, video super-resolution algorithms, and AI security. Notable collaborations include studies on Ripple transaction networks and IoT security. No awards or grants explicitly listed. No student advisees identified in provided data. Research aligns with UN Sustainable Development Goals related to innovation and infrastructure (SDG 9).

Srijan DasView profile

Assistant Professor

• Computer Vision
• Robotics
• Video Representation Learning
• +3 more

Srijan Das is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Charlotte. His research focuses on Video Representation Learning, Robotic Vision, and applications in healthcare through affiliations with the AI4Health Center and Charlotte Machine Learning Lab (CharMLab). He holds a PhD from INRIA (France) and completed postdoctoral work at Stony Brook University. Education: PhD in Computer Science (INRIA, 2020), Post-Grad (NIT Rourkela). Research interests span multimodal learning, vision-language-action models, diffusion models for video generation, and AI for healthcare. Recent achievements include NSF grant funding, 3rd place in WACV 2025's Elderly Action Recognition Challenge, and serving as NeurIPS 2025 Area Chair. His lab advises multiple PhD students and undergraduates in domains like action detection and medical imaging. Key contributions include the LLAVIDAL model (CVPR 2025), GECKO for histopathology (ICCV 2025), and foundational work on pose-induced video transformers. His research bridges computer vision with robotics applications, emphasizing interpretable AI and real-world deployment.

Dieter FellnerView profile

Professor

• Computer Graphics
• Digital Libraries
• Computational Geometry
• +10 more

Dieter Fellner is a Professor of Computer Science at Technical University of Darmstadt, Germany , and Director of the Fraunhofer Institute of Computer Graphics (IGD) . He also serves as Founding Director of the Institute of Computer Graphics and Knowledge Visualization at Graz University of Technology, Austria. His career spans multiple prestigious institutions including University of Bonn, Memorial University of Newfoundland, and University of Technology Braunschweig. Dr. Fellner's research focuses on algorithms integrating modeling and rendering , digital libraries , virtual/augmented reality , collision detection , and radio wave propagation simulation . His work in digital libraries led to a DFG-funded strategic initiative (1997-2005) involving 50 researchers annually across 21 groups. The 15 most recent articles highlight his expertise in 3D document modeling , stereoscopic projection , subdivision surfaces , and color quantization . His publications include influential textbooks like the German standard on computer graphics (1988) and Digitale Bibliotheken (2000). 2000 Fellow of the Eurographics Association Best Technical Paper Award (Günther Enderle Award) at Eurographics’98 2007 IST Advisory Group (ISTAG) Member for the European Commission 2019 Honorary Doctorate from University of Rostock Dr. Fellner actively participates in editorial boards of journals like Computer Graphics Forum , IEEE CG&A , and JOCCH , and serves on program committees for conferences such as Eurographics, VSMM, and ACM Web3D. His leadership roles include Chairmanship of the Eurographics Association (1999-2000) and Directorship of Fraunhofer IGD (since 2006).

Huang QunyingView profile

Professor

• Geographic Information Science
• Spatial Big Data Analytics
• GeoAI
• +4 more

Huang Qunying is a Professor in the Department of Geography at the University of Wisconsin–Madison, where she directs the Spatial Computing and Data Mining Lab . She holds a PhD in Earth Systems and Geoinformation Sciences from George Mason University (2011), an MS in Cartography and GIScience from Peking University (2007), and a BS in Survey and Mapping Engineering from Central South University (2004). Her research integrates GeoAI, spatial big data analytics, and remote sensing to address challenges in disaster management, environmental justice, and urban informatics. Key domains include real-time flood/wildfire mapping, human mobility modeling, and social inequality quantification using multimodal data (e.g., satellite imagery, social media). Her publications (2022–2025) demonstrate a strong focus on deep learning applications for geospatial problems , including transformer networks for mobility prediction, self-supervised building damage assessment, and multimodal land use mapping. Recurring themes include disaster resilience, computer vision, and scalable computing for Earth observation. Awards & Honors: Vilas Associates Award (2020) Microsoft AI for Earth Award (2020) Madison Teaching and Learning Excellence Fellow (2016) NSF Hazards & Disasters Researcher (2014) She advises PhD/MS students on topics like flood mapping, GeoAI, and social sensing. Major grants include NSF funding for disaster analytics ($299K) and USDA projects for crop yield prediction ($650K). Her lab collaborates with NOAA, NASA, and Microsoft to develop cloud-based geospatial tools.

• Machine Learning
• Materials Science
• Physics
• +3 more

Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut (Storrs). Her research focuses on computational science applied to physical sciences, particularly machine learning for materials, physics, and chemistry. She holds a Ph.D. in Computational and Mathematical Engineering from Stanford University and a B.A. in Applied Mathematics/Computer Science from Harvard College. Before joining UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory Group. Her work emphasizes developing scalable models from molecular dynamics data, model reduction for nonlinear systems, and addressing challenges in scientific datasets (e.g., small, clustered, or imbalanced data). Research Interests : Machine Learning for Materials Science, Data-Driven Modeling, Uncertainty Quantification, Chemical Reaction Networks, and Multiscale Simulation. Awards : Best Instructor Award (Stanford, 2015/2016), Best Student/Postdoc Presentation Award (Materials Research Society, 2015). Teaching : Taught courses on Machine Learning for Physical Sciences, Computer Architecture, and computational methods at UConn and Stanford. Known for interdisciplinary approaches and student engagement. Labs/Teams : Leads research on computational modeling and hosts the Materials and Megabytes podcast, featuring interdisciplinary discussions with leading researchers.

• Computer Vision
• Machine Learning
• Image Processing
• +5 more

Dr. Xin Lin is a Professor at the School of Computer Science and Technology, University of Science and Technology of China in Hefei. With an extensive publication record spanning computer vision, machine learning, and artificial intelligence, Dr. Lin leads a research group focused on solving challenging problems in image processing, robotics, and wireless communications. His work bridges theoretical advancements with practical applications across healthcare, autonomous systems, and industrial manufacturing. Dr. Lin's research interests encompass computer vision, machine learning, image processing, and artificial intelligence, with particular expertise in image restoration, 3D object detection, and human pose estimation. His laboratory develops innovative approaches to handle multiple image degradations simultaneously and create lightweight, efficient vision systems suitable for real-world deployment. The research demonstrates strong interdisciplinary connections, applying computer vision techniques to medical imaging, satellite communications, and industrial IoT applications. Analysis of Dr. Lin's recent publications reveals a strong focus on multi-task learning approaches that address multiple image degradation problems simultaneously. His work shows increasing sophistication in handling complex real-world scenarios, from low-light conditions to rain interference, while maintaining computational efficiency. The research trajectory demonstrates a clear path from fundamental image processing techniques to practical applications in autonomous driving, healthcare, and industrial systems. Dr. Lin has received recognition for his contributions to the field through numerous publications in top-tier venues including CVPR, IEEE Transactions, and ACL. His work on image restoration, particularly the Dual Degradation Representation framework, has gained significant attention in the computer vision community. Dr. Lin actively supervises graduate students and collaborates with researchers worldwide. His laboratory works on cutting-edge projects involving digital twins for manufacturing, satellite communications, and medical imaging applications. Current research directions include developing more robust and efficient models for real-world deployment scenarios, with particular attention to resource-constrained environments.

Tingting MuView profile

Senior Lecturer

• Machine Learning
• Computer Vision
• Text Mining
• +3 more

Tingting Mu is a Senior Lecturer in Machine Learning at the University of Manchester's Department of Computer Science. She holds a B.Eng. from the University of Science and Technology of China (2004) and a Ph.D. from the University of Liverpool (2008). Prior to joining Manchester in 2016, she was a lecturer at Liverpool’s Department of Electrical Engineering and Electronics. Her research focuses on advanced mathematical modeling and large-scale optimization in machine learning and data analytics. Key areas include developing algorithms for human intelligence simulation, analyzing complex data (e.g., text, images, signals), and applications in text mining and scientific data analytics. Teaching includes courses like COMP24112 Machine Learning and COMP34111 Natural Language Systems. Her work contributes to UN Sustainable Development Goals related to Digital Futures and Sustainable Futures. Notable projects include MCAIF (Centre for AI Fundamentals) as a researcher and a Knowledge Exchange Partnership with Fusion21 Ltd. Over 60 research outputs span topics like neural networks, adversarial learning, and ontology-based systems. Current projects emphasize AI fundamentals, surrogate models for engineering systems, and interdisciplinary applications. Her lab explores techniques to enhance model interpretability, scalability, and robustness across vision, NLP, and multimodal domains.

• Game Theory
• Multi-Agent Reinforcement Learning
• Cooperative Multi-Agent Systems
• +3 more

Karl Tuyls is a Professor of Computer Science at the University of Liverpool's School of Electrical Engineering, Electronics and Computer Science, with additional research affiliation at DeepMind. His work bridges theoretical game theory and practical multi-agent reinforcement learning systems. His research focuses on cooperative multi-agent systems, where he pioneers value decomposition techniques and emergent communication protocols. Key contributions include frameworks for handling team reward structures, analyzing inequity aversion in social dilemmas, and developing differentiable game mechanics that separate potential and Hamiltonian game dynamics. Analysis of his 2017-2019 publications reveals dominant trends in cooperative multi-agent reinforcement learning, particularly value decomposition architectures and emergent communication systems. These works span artificial intelligence, game theory, and behavioral economics, with specific focus on fair reward allocation, relational inductive biases in deep learning, and open-source framework development for experimental validation. Professor Tuyls leads collaborative research between the University of Liverpool and DeepMind, directing projects on multi-agent coordination and social dilemma modeling through frameworks like OpenSpiel that enable reproducible experimentation in complex game-theoretic scenarios.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +3 more

Dr. Alan Blair is a Senior Lecturer at the School of Computer Science and Engineering, University of New South Wales. His research spans artificial intelligence, machine learning, and neural networks, with applications in medical imaging, evolutionary computation, and cybersecurity. Key research areas: Artificial Intelligence, Machine Learning, Neural Networks, Evolutionary Computation, Medical Imaging, Computational Linguistics Recent publications focus on reinforcement learning for medical diagnosis, dialogue act tagging, and phishing detection using feature-free tools. Collaborations include work with Sowmya A, Iyer S, and Dawes L on 3D organ localization and fracture detection in CT scans. Blair's work integrates hybrid neural-logical reasoning models for visual question answering and hierarchical evolutionary algorithms for optimization tasks. He has contributed to adversarial image generation and game theory applications.

Zihan ZhouView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Image Processing
• +8 more

Zihan Zhou is an Assistant Professor at the College of Information Sciences and Technology, Penn State University, specializing in computer vision, machine learning, and 3D reconstruction. His research bridges geometric modeling, image processing, and human-computer interaction, with applications in assistive technology and creative design. Email: zuz22@psu.edu His work focuses on robust face recognition, sparse representation, and vision-language approaches for converting 2D CAD drawings into 3D parametric models. Recent projects include neural rendering for wireframe-to-image translation and data-driven 3D scene modeling. The 15 most recent publications highlight his contributions to end-to-end floorplan generation, depth estimation, trajectory prediction, and structured 3D modeling. These works integrate convolutional neural networks, graph construction, and optimization algorithms. Projects like Building Energy Savings by Tuning Indoor Lighting underscore his interdisciplinary approach, combining computer vision with environmental sustainability.

Shmuel AvidanView profile

Professor

• Object Tracking
• 3D Object Modeling
• Computer Vision
• +3 more

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

Luwen HuangfuView profile

Assistant Professor

• Artificial Intelligence
• Business Analytics
• Public Health
• +4 more

Luwen (Vivian) Huangfu serves as an Assistant Professor in the Management Information Systems Department at San Diego State University's Fowler College of Business. Holding a PhD from the University of Arizona (2019), she has published over 30 peer-reviewed articles in premier AI venues including IEEE Transactions and ACM conferences, accumulating 300+ citations. Her research bridges artificial intelligence with business analytics, public health, and transportation systems. Her educational foundation includes: PhD in Management Information Systems, University of Arizona (2019) Dr. Huangfu's research pioneers few-shot learning and generalized zero-shot recognition with applications spanning pavement distress detection , mental health intervention , and code search optimization . She develops novel architectures for multi-label image classification and document clustering using large language models, with significant contributions to weakly supervised learning and metric network design. Her work consistently addresses real-world challenges in transportation infrastructure, cybersecurity, and healthcare analytics. Recent publications (2023-2025) reveal a strategic focus on efficient deep learning for resource-constrained environments, particularly in transportation and medical imaging. She integrates spatial contextual awareness in multiple instance learning while advancing prompt-based refinement for long-tailed distributions. Her growing emphasis on LLM-driven security intelligence and social media mental health analysis demonstrates cross-domain impact. Her scientific accolades include: Corporation for Education Network Initiatives in California (CENIC) Award (2024) NIH-supported Summer Institute 2024/2025 Cohort NSF-supported University of Maryland Travel Award (2024) Department of Energy (DOE) Award (2022, 2023) NSF-DOE-Jointly-Supported Travel Award (2023) Management Information Systems Quarterly (MISQ) Scholarly Development Academy (2022) 24 total awards including multiple NIH/NSF grants and teaching fellowships As a principal investigator, she has secured funding from DOE, NIH, and NSA while serving as advisor for undergraduate and graduate research scholarships. Her service includes NSF ACCESS Program advisory, NIH/NSF grant review panels, and editorial roles for IEEE Transactions and Journal of Medical Internet Research. She actively mentors students through SDSU scholarship committees and DEI initiatives while maintaining rigorous peer-review commitments across 15+ journals and conferences.