جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۱۰ مورد دیگر

Hoda Eldardiry is an Associate Professor in the Department of Computer Science at Virginia Tech, where she directs the Machine Learning Laboratory. Her research focuses on artificial intelligence and machine learning, particularly in building human-machine collaborative AI systems that can learn context-aware and explainable models from multisource and interconnected data. Prior to joining Virginia Tech, she led research at Palo Alto Research Center (Xerox PARC) in the machine learning research group. Dr. Eldardiry received her educational qualifications from: BE in Computer and Systems Engineering from Alexandria University, Egypt MS and PhD in Computer Science from Purdue University Her research interests span multiple domains of AI and machine learning. She specializes in robust machine learning for information extraction, forecasting, and control. Her work integrates graph neural networks, time-series analysis, and relation extraction to develop explainable and context-aware AI systems. She also investigates the intersection of AI with ethics, policy, and governance, exploring how to build responsible AI systems that align with human values and societal needs. Dr. Eldardiry's recent publications demonstrate a strong focus on advancing graph-based time-series modeling, zero-shot learning techniques, and optimal control systems. Her work bridges theoretical advancements with practical applications in healthcare, transportation, and e-commerce. She has made significant contributions to knowledge graph construction, explainable AI, and federated learning frameworks that operate efficiently in resource-constrained environments. Her scientific achievements have been recognized with several prestigious awards: Purdue University College of Science Early Career Scientist Award for the Department of Computer Science (2021) Honorable Mention Best Paper Award for Exploring Approaches to Artificial Intelligence Governance: From Ethics to Policy (IEEE Ethics 2023) Most Cited Paper Award for COVID-19 Pandemic Impacts on Traffic System Delay, Fuel Consumption and Emissions (2023) Purdue CS Women's History Month Celebration Recognition (2022) VT CS Women's History Month Celebration Recognition (2023) Early Career Distinguished Scientist Award from Purdue University College of Science (2021) Purdue University College of Science Distinguished Alumni (2021) Dr. Eldardiry has successfully secured substantial research funding, with total grant funding of $27,424,460 ($13,808,328 share) from diverse sources including VT, IARPA, DOE, NSF, DARPA, NIH-iTHRIV, CCI, EBAY, SIEMENS, ADOBE, P&G and XEROX. Her current projects include NSF-funded research on Advancing Health Equity using Interactive Condition Assessment and Monitoring and Exploring How AI Engineers Perceive and Develop Translational Ethical Competency, as well as industry collaborations with EBAY on Heterogeneous Hypergraph Modeling for Zero-Shot Product Aspect Identification. As director of the Machine Learning Laboratory at Virginia Tech, Dr. Eldardiry leads a research team that bridges theoretical AI advancements with real-world applications. Her lab collaborates extensively with industry partners and government agencies to develop practical AI solutions while maintaining a strong commitment to ethical considerations and societal impact.

• Computer Vision
• Multimedia Computation
• Machine Learning
• +۲ مورد دیگر

Litao Yu is a Part-Time Lecturer and Visiting Scholar at the Faculty of Engineering and Information Technology, University of Technology Sydney. Concurrently, he serves as a Senior Data Analyst at Australia's Department of Agriculture, Fishery and Forestry. His academic appointments include Research Fellow at UTS (2019-2024) and post-doctoral positions at Griffith University and Queensland University of Technology. Education: PhD from The University of Queensland (2013-2016) MPhil from Dalian University of Technology (2009-2012) BSc from Dalian Maritime University (2003-2007) Dr. Yu's research focuses on computer vision and machine learning with applications in agriculture, multimedia systems, and multimodal learning. His work spans few-shot learning, semantic segmentation, fine-grained recognition, and multimodal fusion techniques. Key application domains include animal welfare monitoring, aquaculture quality control, and travel information enhancement. His publications demonstrate strong thematic convergence around efficient visual recognition systems , with recurring focus on few-shot learning paradigms and attention mechanisms. Recent work shows increasing emphasis on agricultural applications of computer vision, including poultry monitoring, fish processing automation, and sheep tracking. Blockchain integration for supply chain transparency represents another emerging theme. No scientific awards are mentioned in available records. Teaching responsibilities at UTS include courses on Real-time Operating Systems and Shell Programming . No information is available regarding research grants, supervised students, or laboratory affiliations.

Hadar Averbuch-Elorمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Multimodal Perception Systems
• +۷ مورد دیگر

Hadar Averbuch-Elor is an Assistant Professor at the Cornell Ann S. Bowers College of Computing and Information Science and Cornell Tech , with prior roles at Tel Aviv University and postdoctoral work at Cornell Tech. Her research bridges computer graphics and computer vision , focusing on integrating 3D geometry and natural language into multimodal perception systems. Education: B.S. in Electrical Engineering, Technion Israel Institute of Technology Ph.D. in Computer Science, Tel Aviv University Her recent publications highlight advancements in diffusion models , 3D reconstruction , and image generation , with applications in text-guided shape editing , floorplan localization , and cuneiform digitization . Papers span top conferences like ICCV , SIGGRAPH , CVPR , and Eurographics . Scientific Awards: Zuckerman Postdoctoral Scholar Fellowship Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences Rising Star in Electrical Engineering and Computer Sciences, UC Berkeley She advises PhD students including Morris Alper , Etai Sella , and Daniel Garibi , and has mentored former MS/BS students like Gal Fiebelman and Rachel Mikulinsky .

Adrien Doerigمشاهده پروفایل

استاد مهمان

• Computational Neuroscience
• Cognitive Neuroscience
• Machine Learning
• +۵ مورد دیگر

Adrien Doerig is a Visiting Professor in the Department of Education and Psychology at Freie Universität Berlin, where he leads research in the Cognitive Computational Neuroscience Lab. He is also affiliated with the Bernstein Center for Computational Neuroscience. His work bridges cognitive science, computational modeling, and neural mechanisms of perception. Doerig's research focuses on the intersection of computational neuroscience and artificial intelligence, with particular emphasis on visual perception, consciousness, and the development of biologically plausible neural network models. His work explores how the brain processes visual information, with special attention to phenomena like crowding, feature integration, and the temporal dynamics of perception. He has made significant contributions to understanding the limitations of convolutional neural networks for modeling human vision and has pioneered work on topographic neural networks that better capture cortical organization. A key trend in Doerig's recent publications is the exploration of connections between language models and visual processing in the brain. His groundbreaking 2025 Nature Machine Intelligence paper demonstrated that large language model representations align closely with visual representations in the human brain, opening new avenues for using language-based AI in modeling biological visual processing. His work consistently challenges existing paradigms while developing more biologically plausible computational frameworks. Doerig teaches a wide range of courses including Master's in Cognitive Neuroscience, Probability and statistical modeling (both theoretical and practical with focus on ANNs and neuroimaging), Introduction to programming, Applied computational cognitive neuroscience, and specialized courses on vision, language, affective & social neuroscience, and consciousness. He also supervises graduate students through the Cognitive Computational Neuroscience Lab, which includes postdocs, predocs, master's students, and interns working on cutting-edge research questions at the intersection of cognitive science and machine learning.

• Explainable AI
• Human-like machine learning
• Explanation Generation
• +۷ مورد دیگر

Professor Ute Schmid is a Full Professor of Cognitive Systems at the University of Bamberg, where she has been a faculty member since September 2004. She leads the Cognitive Systems Group within the Bamberg Center of AI (BaCAI), focusing on creating AI systems that generate human-like explanations and reasoning processes. Her research bridges cognitive science and artificial intelligence to develop methods for explanation generation, inductive programming, and interactive machine learning. Professor Schmid's work emphasizes practical applications of explainable AI across diverse domains including image classification, medical diagnosis, and educational technologies. Her research on contrastive explanations, near misses, and human-AI alignment has significantly advanced the field of XAI. She has also pioneered research on AI literacy, recognizing the growing importance of basic AI understanding for responsible tool usage by non-experts. Her publication record demonstrates exceptional productivity and impact, with numerous articles in top-tier venues including Nature Machine Intelligence, IEEE Transactions on Visualization and Computer Graphics, and the Journal of Web Semantics. Her 2025 paper 'Aligning generalization between humans and machines' represents a significant theoretical contribution to understanding human-machine cognitive alignment. Professor Schmid actively contributes to gender diversity research in computer science through studies examining why women pursue PhDs in the field. She has also made important contributions to computing education, investigating how students acquire programming skills and how AI tools like code generators are integrated into learning processes. As an educator and researcher, Professor Schmid maintains strong international collaborations, with co-authors spanning multiple countries and institutions. Her interdisciplinary approach is evident in her diverse publication venues and collaborative work that bridges computer science, cognitive science, education, and application domains.

• Cognitive Development
• Language Acquisition
• Counterfactual Reasoning
• +۳ مورد دیگر

Patricia Ganea is a Professor in the Department of Applied Psychology and Human Development at the Ontario Institute for Studies in Education (OISE), University of Toronto, where she also directs the Dr. R.G.N. Laidlaw Research Centre. Her research examines early cognitive development, emphasizing how children use language for hypothetical reasoning, update mental representations, and overcome misconceptions in science. She has secured sustained funding from SSHRC, NSERC, CIHR, and NSF for projects on counterfactual reasoning, conceptual change, and social-cognitive development. Her investigations span: Language's role in reasoning about non-present entities and possibilities Counterfactual thinking as a scaffold for scientific learning Social and pragmatic influences on early communication Recent publications demonstrate a focus on children's learning from digital media, narrative comprehension, and the design of educational tools. Article trends reveal: Robust methodologies: eye-tracking, longitudinal designs, and controlled experiments Cross-cutting themes: conceptual revision, symbolic representation, and executive function Practical applications: picture books, games, and digital interventions for STEM education Awards include the Ontario Early Researcher Award. Grant highlights: SSHRC grants for counterfactual reasoning studies (2023–2027) CIHR funding for placental health-cognition linkages (2020–2022) NSERC support for language processing research (2016–2022) At the Laidlaw Research Centre, she leads studies on child development, collaborating with interdisciplinary teams to explore cognitive, linguistic, and neurodevelopmental trajectories. The lab employs diverse paradigms to translate basic research into educational strategies.

Sakeena Muntahaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Image Processing
• +۳ مورد دیگر

Sakeena Muntaha serves as a Junior Researcher at the University of Applied Sciences St. Pölten, affiliated with the Institute of Creative\Media/Technologies and the Department of Media and Digital Technologies since 2017. Currently on leave, she contributes to the institution's research mission through interdisciplinary projects spanning computer vision and applied machine learning. Her academic foundation includes a Master's degree in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan (2016) and a Bachelor's degree in Computer System Engineering from the NFC Institute of Engineering and Technology (NFCIET), Pakistan (2012). These qualifications underpin her technical expertise in visual computing systems. Dr. Muntaha's research program centers on machine learning and computer vision with dual application tracks: medical diagnostics (skin lesion segmentation, dermoscopy analysis) and environmental/urban systems (building footprint extraction, flood monitoring, real estate analysis). Her methodological approach integrates deep learning architectures with classical image processing techniques like level sets and Gabor filters, demonstrating versatility across domains from cultural heritage preservation to cybersecurity. Recent work shows increasing focus on robustness evaluation and real-world deployment challenges in vision systems. Analysis of her 15 most recent publications reveals strong thematic continuity in computer vision applications, with growing sophistication in handling real-world data constraints. Early work focused on medical imaging and malware detection, while recent publications emphasize urban infrastructure analysis and environmental monitoring, reflecting strategic alignment with societal challenges. The consistent use of deep learning frameworks across diverse domains highlights her technical agility. As an active member of the Media Computing Research Group, she contributes to projects including IMREA (Intelligent Multimodal Real Estate Assessment), Scribe ID AI (cultural heritage analysis), and ImmBild (location assessment via computer vision). Her collaborative research involves partnerships with institutions across Austria and Pakistan, though specific grant details and advising activities are not documented in available sources.

Timothy Rogersمشاهده پروفایل

استاد

• Semantic Cognition
• Cognitive Neuroscience
• AI and Health
• +۴ مورد دیگر

Timothy Rogers is a Professor in the Department of Psychology at the University of Wisconsin. His research focuses on the intersection of semantic cognition , cognitive neuroscience , and artificial intelligence . Based in Madison, Wisconsin, he operates the Rogers Lab at the Discovery Building (330 N. Orchard Street), utilizing advanced neuroimaging techniques like 7T-fMRI to decode semantic representations in the brain. Education: BA in Psychology and English Literature (University of Waterloo), PhD in Psychology (Carnegie Mellon University) His work explores semantic control mechanisms , neural coding of concepts, and human-machine collaboration in creative tasks. Recent projects investigate LLM alignment with human judgment, context inference , and representational motifs in perception. Research trends show integration of multivariate decoding , sparse modeling , and collective intelligence to analyze semantic organization in cognition and neural systems. His lab applies these methods to problems in health AI , educational technology , and neurodegenerative disorders . Contact: 1.608.316.4339 , Discovery Building, Madison, WI 53715.

• Robotics Perception
• Computer Vision
• Lidar and Radar Sensing
• +۵ مورد دیگر

Pascal Vasseur is a University Professor at the University of Picardie Jules Verne (UPJV) in the Faculty of Sciences, Computer Science Department. He leads the Robotics Perception group within the MIS Laboratory (Modeling, Information and Systems) and serves as a member of the National University Council (Section 61) since 2016. Additionally, he has been an Associate Editor for IEEE Robotics and Automation Letters since 2016, demonstrating his significant standing in the robotics research community. Professor Vasseur's research focuses on robotics perception systems, with particular expertise in vision (including omnidirectional and event-based cameras), Lidar, and Radar technologies. His work addresses fundamental challenges in pose estimation, mapping, and localization for robotics and automotive applications. His recent publications show a strong emphasis on advanced sensor technologies, multi-sensor calibration techniques, and practical implementations for autonomous systems. His publication record demonstrates consistent high-impact research output, with numerous articles in top robotics and computer vision venues including IEEE Robotics and Automation Letters, IEEE Transactions on Intelligent Vehicles, and CVPR. He has also authored two comprehensive books on Omnidirectional Vision (2023-2024), establishing himself as a leading expert in this specialized area of computer vision. Professor Vasseur has coordinated multiple significant research projects including ANR projects CaViAR and pLaTINUM, the international DrAACaR project, and PHC STAR and AMADEUS projects. He currently serves as scientific officer for the ANR CLARA Project, continuing his leadership in advancing robotics perception research. His research group actively contributes to solving practical challenges in automotive vision systems, drone navigation, and forest environment mapping, with applications spanning autonomous vehicles, robotics navigation, and environmental monitoring systems. The group's work bridges theoretical advances in computer vision with real-world robotics implementations.

• Semantic Web
• Natural Language Processing
• Information Retrieval
• +۵ مورد دیگر

Dr. Jörg Waitelonis serves as Scientific Co-Worker at FIZ Karlsruhe – Leibniz Institute for Information Infrastructure and Senior Researcher at Karlsruhe Institute of Technology's Institute of Applied Informatics and Formal Description Methods (AIFB), working under Prof. Dr. Harald Sack. His career spans over 15 years in semantic technologies research, beginning at Hasso-Plattner-Institute (2009-2018) and continuing at KIT/FIZ Karlsruhe. Dr. Waitelonis' research focuses on practical implementations of Semantic Web technologies across diverse domains. His work bridges theoretical knowledge representation with real-world applications in cultural heritage informatics, materials science, historical document analysis, and sports science. Key contributions include developing domain-specific ontologies (CourtDocs, PMD Core), advancing FAIR data implementation within Germany's NFDI framework, and creating knowledge graph solutions for cross-disciplinary research data integration. His publication record demonstrates consistent output in top Semantic Web venues (ISWC, SEMANTiCS) with recent work (2023-2025) emphasizing practical ontology engineering for national research data infrastructure projects. Current research directions show increasing focus on materials science ontologies, historical document processing, and BFO-based knowledge representation frameworks. As founder of yovisto GmbH since 2012, Dr. Waitelonis maintains strong industry connections while pursuing academic research. His work demonstrates the practical application of semantic technologies to solve real-world data integration challenges across multiple scientific domains.

Chenxi Yuanمشاهده پروفایل

دانشیار

• Civil Infrastructure Systems
• Sensing and Informatics
• Visualization Techniques
• +۵ مورد دیگر

Dr. Chenxi Yuan is an Associate Professor in the Department of Construction Management at Southern Illinois University Edwardsville's School of Engineering. He directs the Transparent Civil Infrastructure System (T-CIS) Lab and maintains affiliations with McCarthy Construction Company through a faculty residency fellowship. Previously, he worked as an Assistant Engineer at ARUP. Education includes: PhD in Civil Engineering, Purdue University (2018) Master of Civil Engineering, Tongji University, China (2010) Bachelor of Mechanics Engineering, Central South University, China (2007) Dr. Yuan's research develops transparent civil infrastructure systems through sensing, informatics, and visualization. His work integrates Building Information Modeling (BIM) , mixed reality , and machine learning to enhance buildability, readability, and socio-responsibility of infrastructure. Research spans automated inspection systems, earthmoving operations optimization, and subsurface utility mapping. Publications demonstrate strong focus on construction informatics and infrastructure visualization , with recent work incorporating large language models for bridge inspection and ontology-based knowledge systems. Consistent themes include computer vision applications, risk-based inspection frameworks, and geospatial data integration. Awards and honors: K.B. Woods Award, TRB (2019) Best Paper Award (First Place), TRB Committee on Construction Management (2018) Robert L. Bowen Industry Residency Fellowship (2022) Dr. Yuan has supervised 6+ graduate students in thesis research on construction automation and infrastructure informatics. Funded projects include: NSF-funded ontology-based bridge inspection system ($10K) Real-time decision frameworks for manufacturing ($14K) Mixed reality alignment systems ($14K) The T-CIS Lab features drones, AR/VR systems (HoloLens, HTC Vive), and construction simulators to support research in infrastructure transparency and visualization technologies.