جست‌وجوی استاد و پژوهشگر

• Cybersecurity
• Authentication Systems
• Blockchain Technology
• +۶ مورد دیگر

Jun-Ho Huh is a Professor at Sungkyunkwan University's College of Information and Communication Engineering, Department of Computer Science and Engineering. His extensive research portfolio demonstrates leadership in cybersecurity, authentication systems, and AI applications across multiple domains including financial technology, maritime security, and agricultural technology. Dr. Huh's research interests span cybersecurity, authentication systems, blockchain technology, artificial intelligence, edge computing, and IoT security. His work bridges theoretical computer science with practical applications in smart systems engineering. His research has significant implications for improving security in emerging technologies like DeFi, autonomous systems, and smart infrastructure. Analysis of Dr. Huh's recent publications reveals a strong focus on practical applications of AI and security technologies. His work covers diverse areas including cybersecurity for maritime autonomous systems, authentication mechanisms, financial technology applications, and agricultural technology solutions. The publications demonstrate consistent productivity and relevance in addressing contemporary challenges in computer science and engineering. Dr. Huh has made significant contributions to authentication systems research, particularly in biometric and behavioral authentication methods. His work on keystroke dynamics, voice spoofing detection, and wearable device authentication has advanced the field of usable security. His research extends to practical applications in smart factories, agricultural technology (AgTech), and financial technology. The integration of blockchain with AI for various applications represents a significant thread throughout his recent work, demonstrating interdisciplinary approaches to solving complex technological challenges.

• Artificial Intelligence
• Computational Linguistics
• Natural Language Processing
• +۷ مورد دیگر

Sandra Carberry is a Professor in the Department of Computer and Information Sciences at the University of Delaware with a joint appointment in Linguistics. Her research integrates artificial intelligence, natural language processing, and user modeling to develop intelligent systems for digital libraries, medical decision support, and educational applications. She leads the NLP/AI laboratory and teaches core AI and data mining courses. Her primary research focuses include: Intelligent Interfaces : Developing systems that recognize user goals and generate tailored responses through advanced user modeling Digital Libraries : Pioneering methods to extract meaning from information graphics using perceptual effort analysis and Bayesian reasoning Medical Informatics : Creating TraumaCASE for generating clinical training scenarios and improving trauma care decision support Educational Systems : Building adaptive medical tutoring systems that dynamically adjust case complexity Dialogue Systems : Advancing transformation-based learning for dialogue act recognition and cooperative response generation Her publication trend (2002-2007) reveals a strategic shift toward information graphics understanding, where she established that 61% of graphics convey unique information not present in text. This work combines cognitive psychology with machine learning to recognize graphic intentions through perceptual effort metrics and communicative signals, significantly advancing digital library capabilities. Key recognitions include: James Chen 2006 Award for Best Paper (UMUAI Journal) Best Paper Award at Diagrams Conference (2006) Springer Best Paper Award at User Modeling Conference (2007) Funded by NSF grant IIS-0534948, her digital libraries project demonstrates strong grant acquisition capability. Her extensive co-authorship with junior researchers indicates active mentorship, though formal student lists aren't published. University service includes departmental committees and professional activities documented in her vita. She directs the NLP/AI laboratory, which maintains active research in natural language processing, artificial intelligence applications, and multimodal information systems, with current work extending graphic understanding to complex visual representations.

Steffen Staabمشاهده پروفایل

استاد

• Web Science
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Professor Steffen Staab holds dual appointments as Professor of Web and Computer Science at the University of Southampton (since 2015) and Chair for Analytic Computing at the Institute for Parallel and Distributed Systems, Universität Stuttgart (since 2020). His research focuses on making data meaningful through ontologies, machine learning, and human-computer interaction, with a particular emphasis on Web Science. He leads the Institute for Web Science and Technologies (WeST) and is affiliated with organizations such as the Web Science Trust and ELLIS Unit Stuttgart. Education: PhD in Computer Science (1998) and Habilitation (2002) from Universität Karlsruhe (KIT). Prior roles include founding the WeST Institute at Universität Koblenz-Landau (2009–2020). Research interests span ontology development, data mining, and ethical AI applications. He is a Fellow of the European Association for Artificial Intelligence and has advised PhD students like Shatha Saad Algarni. His work bridges technical innovation with societal impact, particularly in accessibility and industrial automation.

Chen LIمشاهده پروفایل

دانشیار

• Human Robot Interaction
• Industrial Robotics
• AI for Manufacturing
• +۳ مورد دیگر

Chen LI is an Associate Professor in the Department of Materials and Production at Aalborg University, Denmark. His work focuses on advancing smart manufacturing through AI-driven robotics and 5G-enabled production systems. He leads research projects involving historical figure emulation in robotics, human-robot interaction, and AI at the edge for industry 5.0. His interdisciplinary approach bridges software architecture, industrial automation, and cognitive modeling. Key projects: Time-Traveling Conversations (Villum Foundation), Human Robot Interaction dialog systems, and AI REDGIO 5.0 for SME manufacturing Collaborations span institutions like Seoul National University and industry partners Research interests include: AI ethics in dialogue systems Edge computing for real-time manufacturing Virtual assistant architectures Robot speaking style imitation Over 70 publications (2009-2025) span topics like anomaly detection frameworks, defect prediction models, and hybrid power system analysis. Active in conferences such as HRI2021 and workshops on AI in performing arts.

Yezhou Yangمشاهده پروفایل

دانشیار

• Computer Vision
• Artificial Intelligence
• Robotics
• +۴ مورد دیگر

Dr. Yezhou (YZ) Yang is a tenured Associate Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). He leads the ASU Active Perception Group , focusing on Cognitive Robotics, Computer Vision, and Generative AI . His work integrates vision, deep learning, and AI to interpret human actions and environmental geometry. Education: Ph.D. Computer Science, University of Maryland, College Park (2016) M.S. Computer Science, University of Maryland, College Park (2013) B.E. Computer Science and Engineering, Zhejiang University, China (2010) Research Interests: Dr. Yang’s research emphasizes Visual Recognition with Knowledge (VR-K) , Autonomous Vehicles , and Multimodal Learning . Key projects include: - Developing Secure and Decentralized Generative Models (NSF SaTC Grant) - Enhancing Robustness in Vision-Language Tasks (NSF Robust Intelligence Grant) - Traffic Safety Assessment via Infrastructure Cameras (Institute of Automated Mobility) Publications: Recent work spans Text-to-Image Generation (e.g., RefEdit), Event-based Vision (e.g., SEVD Dataset), and Connected Autonomous Vehicles (e.g., CAROM). His team’s contributions to benchmarks like VL-GLUE and ConceptBed advance multimodal reasoning and model evaluation. Awards & Recognition: NSF CAREER Award (2020) Amazon AWS ML Research Award (2019) Co-founder of ARGOS Vision Inc. Lab & Teams: The ASU Active Perception Group collaborates with industry (e.g., Arizona DOT) and academic partners. Ongoing projects include Robust Multimodal Foundational Models and Socially-Adept Autonomous Vehicles .

• Machine Learning
• Computer Vision
• Multimodal Learning
• +۷ مورد دیگر

Yunhui Guo is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced machine learning techniques including multimodal learning, continual learning, audio-visual recognition, and domain adaptation. He explores challenges in model robustness, cross-modal interactions, and efficient training strategies for deep neural networks. Key research areas include: Developing robust multimodal models for video entailment and dynamic 3D human reconstruction Improving audio-visual segmentation and sound separation through novel adaptation frameworks Advancing continual learning methods to handle domain shifts and out-of-distribution data Creating submodular optimization strategies for active learning in 3D object detection His recent work emphasizes real-world applications like medical image analysis (skin cancer sub-typing), robotics (LiDAR segmentation), and secure AI systems (model watermarking). The research also addresses foundational AI topics such as model uncertainty quantification and adaptive predictive systems. Publications focus on cutting-edge areas like multimodal LLM adaptation, hierarchical out-of-distribution detection, and bimodal online adaptation techniques. Current projects explore the intersection of multimodal perception and lifelong learning systems.

• Authentication
• Brain-Computer/Machine Interface
• Cybersecurity
• +۱ مورد دیگر

Dr. Garima Bajwa is an Assistant Professor in the Department of Computer Science at Lakehead University, where she joined in January 2021. Her research focuses on authentication systems, brain-computer/machine interfaces, cybersecurity, and machine learning applications. She holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2016), an MEng in Electrical and Computer Engineering from the University of Waterloo (2011), and a B.Tech in Electronics and Communication Engineering from Mody Institute of Technology & Science, India (2009). Education: Ph.D., Computer Science & Engineering, University of North Texas (2016) MEng, Electrical & Computer Engineering, University of Waterloo (2011) B.Tech, Electronics & Communication Engineering, Mody Institute (2009) Her research interests span cybersecurity frameworks for neurotechnology, explainable AI in healthcare diagnostics, and multimodal fusion of EEG and image data. Recent work includes EEG signal anonymization, federated learning for medical diagnosis, and reproducibility in BCI research. Her publications explore cutting-edge topics like BCI standardization, driver distraction detection, and quantum computing integration. Grants and lab affiliations are not explicitly mentioned in the provided text.

• Multimodal Learning
• Brain Decoding
• Computer Vision
• +۳ مورد دیگر

Weihao Xia is a postdoctoral researcher at the University of Cambridge's Department of Computer Science and Technology, affiliated with the CORE Lab under Cengiz Öztireli. His work bridges computer science and human cognition, focusing on multimodal learning, brain decoding, and tactile representation. He specializes in integrating visual, textual, neural, and tactile data to enhance AI systems for perception and creativity. Research interests include neural decoding from brain signals, tactile representation learning, and generative models for cross-modal translation. Notable projects include UMBRAE (unified multimodal brain decoding) and DREAM (visual decoding via human visual system reversal). His work addresses challenges in generating realistic images, interpreting neural signals, and improving tactile-based material editing. Teaching: Supervises Part II, Part III, and M.Phil. projects at Cambridge. Research topics for students include brain-based art creation, tactile material editing, and benchmarking sensory modalities. Projects emphasize multimodal alignment, brain-imagery reconstruction, and tactile representation learning. Contact: wx258@cam.ac.uk | Room GC01, William Gates Building.

• Machine Learning
• Natural Language Processing
• Cybersecurity
• +۵ مورد دیگر

Cécile Paris is a researcher at CSIRO , focusing on interdisciplinary applications at the intersection of Artificial Intelligence , Cybersecurity , and Human-AI Collaboration . Her work spans domains such as biomedical informatics, social media analysis, and robotics. Research Interests: Developing collaborative frameworks for human-AI teams Explainable AI in cybersecurity Trust modeling in social networks Fake news detection via temporal and graph-based networks Image captioning with multimodal learning Stance classification across domains Article Trends: Recent publications emphasize adaptive alert prioritization (2025), systematic reviews on cybersecurity challenges (2024), and foundational work in NLP for epidemic intelligence (2018-2020). Key methodologies include graph neural networks, transformer models, and adversarial training.

Hyang-Sook Kimمشاهده پروفایل

دانشیار

• Health communication
• Persuasion in communication technology
• Consumer psychology in strategic communication
• +۳ مورد دیگر

Hyang-Sook Kim is an Associate Professor in the Department of Mass Communication at Towson University's College of Fine Arts and Communication. She holds a Ph.D. from Pennsylvania State University (2012), an M.S. from Kansas State University (2007), and both B.A. and M.A. degrees from Sogang University in Seoul, South Korea (2000 and 2002). Dr. Kim specializes in health communication, persuasion in communication technology, and consumer psychology in strategic communication. Ph.D., Pennsylvania State University (2012) M.S., Kansas State University (2007) M.A. & B.A., Sogang University, South Korea (2002 & 2000) Her research focuses on health promotion strategies using communication technology, particularly examining how digital platforms influence health behaviors and message processing. Recent work analyzes YouTube cancer communities, wearable health technology effects, and cognitive load in entertainment-education content. Key publication venues include Journal of Health Communication , Computers in Human Behavior , Health Communication , and Journal of Broadcasting & Electronic Media . Current projects investigate digital health interventions, social media's role in health support networks, and multimedia message processing dynamics. Contact: hkim@towson.edu

Yen-Ling Kuoمشاهده پروفایل

استادیار

• Robot Learning
• Human-AI/Robot Interaction
• Artificial Intelligence
• +۱ مورد دیگر

Yen-Ling Kuo is an Assistant Professor in the Department of Computer Science at the University of Virginia and a member of the Link Lab. Her research focuses on robot learning, human-AI/robot interaction, and integrating artificial intelligence with cognitive science to enable robots to generalize reasoning skills for human interaction, including language understanding and common sense reasoning. She holds a PhD from MIT CSAIL, advised by Boris Katz and Andrei Barbu. She teaches courses such as Artificial Intelligence and Learning for Interactive Robots, emphasizing foundational AI concepts, robotics applications, and generative AI. Research Interests: Robot Learning Human-AI/Robot Interaction Cognitive Science Integration Machine Learning for Robotic Systems Awards: NSF CAREER Award (2024) Advising & Grants: Actively recruiting students for her UVA research group, focusing on interactive robotics and AI collaboration. Her work is supported by grants including the NSF CAREER Award. Labs: Member of the Link Lab, a leading center for robotics and autonomous systems at UVA.

• Artificial Intelligence
• Medical Technology
• Knowledge-Based Systems
• +۳ مورد دیگر

Asma Belhadi is a Postdoctoral Fellow at Oslo Metropolitan University's Faculty of Technology, Art and Design, Department of Computer Science. Her research focuses on Applied Artificial Intelligence, bridging domains like Neuroscience, Medical Technology, and Intelligent Transportation Systems through innovative methodologies such as Visual Transformers, Federated Learning, and Graph Algorithms. Primary affiliation: Oslo Metropolitan University Academic discipline: Computer Science Research areas: Deep Learning, Explainable AI, Biomedical Signal Processing, Sustainable Systems Her recent work explores cross-domain applications including: EEG classification using enhanced visibility graphs Medical imaging with ensemble fuzzy deep learning Secure pattern mining for Medical Internet of Things Knowledge-enhanced object detection for agriculture Metaverse security using transformer models and GANs

Nadeen Fathallahمشاهده پروفایل

پژوهشگر

• Web Accessibility
• Natural Language Processing
• Computer Vision
• +۳ مورد دیگر

Nadeen Fathallah is a researcher at the University of Stuttgart, affiliated with the Analytic Computing group at KI. Her work spans AI applications for accessibility, computer vision, and knowledge engineering. Research Focus: Web accessibility, ontology learning, and LLM-based solutions for Deaf/Hard of Hearing communities Projects: Key contributor to the IKILeUS project (Integrated AI in Teaching) at the University of Stuttgart Teaching: Has served as teaching assistant and assistant lecturer at German International University, German University in Cairo, and The Knowledge Hub Her research explores: Automated detection/correction of web accessibility violations (e.g., AccessGuru platform) Improving video captions using large language models Accessibility tools for tabular data (EchoTables) Ontology learning pipelines (NeOn-GPT, LLMs4Life) Recent work shows a focus on combining LLMs with domain-specific challenges across multiple fields, particularly emphasizing inclusive design principles. Contact details: Office at Universitätsstraße 32, Stuttgart, Germany (Room: 2.312b). Available via +49 711 685 88130.

Helia Farhoodمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Dr. Helia Farhood is an Honorary Senior Research Fellow at the School of Computing, Macquarie University, specializing in Artificial Intelligence, Machine Learning, and Image Processing with applications in educational technology and object recognition. Her academic qualifications include a PhD in Computer Systems and Artificial Intelligence from the University of Technology Sydney (awarded November 2021) and a Master's degree in Computer-AI from Amirkabir University of Technology (Tehran Polytechnic, awarded September 2013). Dr. Farhood's research spans interdisciplinary AI applications, with significant contributions in student outcome prediction using generative adversarial networks, explainable AI through LIME heatmaps, and image-based storytelling systems. Her work integrates machine learning with educational data mining to enhance creativity assessment and learning analytics, while maintaining strong technical focus on 3D reconstruction and object recognition. Analysis of her 16 publications (2020-2025) reveals three dominant research trajectories: (1) AI-driven educational analytics for student performance prediction, (2) advanced image processing techniques for object recognition and 3D reconstruction, and (3) systematic reviews establishing methodological foundations in presentation attack detection and image-based storytelling. Her recent work increasingly emphasizes explainability and ethical considerations in AI deployment. Dr. Farhood has participated in externally funded research projects, including the 2022 project "Estimating the Number of Tyres in Stockpiles" (October-December 2022). No information is available regarding students she has advised. No information is available about specific research laboratories or teams led by Dr. Farhood.

Michael Ryooمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Robotics
• +۳ مورد دیگر

Michael Ryoo serves as a SUNY Empire Innovation Associate Professor in the Department of Computer Science at Stony Brook University while concurrently working as a Research Scientist with Google Brain's "Robotics at Google" team. Previously, he held positions as an Assistant Professor at Indiana University Bloomington and a Staff Researcher at NASA's Jet Propulsion Laboratory. His academic background includes a Ph.D. from the University of Texas at Austin (2008) and a B.S. from Korea Advanced Institute of Science and Technology (KAIST) in 2004. Ryoo's research centers on deep learning and computer vision with specific focus on convolutional neural network (CNN) models for video semantic understanding. His work bridges visual perception and robotic action through applications in robot perception, robot learning, and human-robot interaction. Key innovations involve developing efficient architectures for processing multimodal data and translating visual understanding into robotic control systems. Analysis of his 15 most recent publications reveals strong emphasis on multimodal AI integration, particularly vision-language models applied to robotics. His 2025 work shows significant advancement in token-efficient video representation, motion-controllable diffusion models, and zero-shot learning frameworks specifically designed for robotic control systems. The research trajectory demonstrates consistent focus on making video understanding more accessible and applicable to real-world robotic scenarios. Scientific awards: No awards were mentioned in the provided source material. Regarding academic advising, the source text does not list any students or mentoring activities. Similarly, no grant funding information is provided, though his dual academic-industry role suggests substantial research support. His Google Brain affiliation likely involves industry-sponsored research initiatives. Ryoo maintains active laboratory affiliations through Stony Brook's AI Innovation Institute and Google's Robotics team. His prior work at NASA JPL indicates experience with space robotics systems, while his current Google role focuses on large-scale robot learning infrastructure. The LAM SIMULATOR project represents his current focus on advancing data generation techniques for training large action models.