جستجوی استاد راهنمای دانشگاهی

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.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Wenjie Li is a Professor at the Department of Computing, Hong Kong Polytechnic University, and holds a PhD from the Chinese University of Hong Kong (1997). His research spans Natural Language Processing, Artificial Intelligence, and Machine Learning , focusing on Large Language Models (LLMs) Multimodal Systems Dialogue and Recommender Systems Speculative Decoding and Inference Optimization Chain-of-Thought Reasoning Recent work emphasizes generative retrieval , personalized web agents , and error-resilient LLM frameworks . Key contributions include the JobFormer for skill-aware recommendations, STeCa for trajectory calibration, and TokenSkip for controllable reasoning compression. Publications reveal a trend toward enhancing multimodal alignment and safety mechanisms in aligned LLMs. Li actively collaborates with institutions like Queen's University , Tsinghua University , and Nanjing University , working on projects such as text-image interleaved retrieval and speculative decoding surveys . His 2024-2025 output includes 15+ papers at venues like ACL, CVPR, ICLR , and journals like IEEE Transactions on Neural Networks .

Philipp Cimianoمشاهده پروفایل

استاد

• Knowledge Representation
• Ontologies
• Knowledge-Based Natural Language Processing
• +۶ مورد دیگر

Philipp Cimiano is a Professor at the Faculty of Engineering, Bielefeld University, and leads the Semantic Databases Group. He holds additional roles as Coordinator of the Cognitive Interaction Technology Center (CITEC) and Director of the Joint Artificial Intelligence Institute (JAII). His research focuses on the intersection of language, semantics, and knowledge representation, with applications in Explainable AI , Knowledge Graphs , and AI in Medicine . Education: University of Stuttgart, University of South Australia, Karlsruhe Institute of Technology (KIT) Key Research Areas: Knowledge Representation, Ontologies, Explainable AI, Clinical Decision Support His recent work emphasizes dialogue-based XAI , federated learning , and semantic data integration . He has secured funding from the German Research Foundation (DFG) and the European Union for projects like TRR 318 "Constructing Explainability" and Pret-a-LLOD. Scientific Awards Carl Adam Petrie Prize, KIT Faculty of Business and Economics Editorial Roles Co-editor, Journal of Applied Ontology Area Editor, Semantic Web Journal Editorial Board, Journal of Web Semantics Notable Projects TRR 318 (Subprojects B01, C05, INF) 3B: Bots Building Bridges for online deliberation LLM4KMU: Open Source LLMs for SMEs

• Computational Complexity Theory
• Machine Learning
• Parallel Computation
• +۵ مورد دیگر

Leslie Valiant serves as the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University's School of Engineering and Applied Sciences, where he has been faculty since 1982. Previously, he held positions at Carnegie Mellon University, Leeds University, and the University of Edinburgh. His academic journey began with education at King's College Cambridge, Imperial College London, and Warwick University, where he earned his PhD in computer science in 1974. Valiant's research spans theoretical computer science with primary focus areas including computational complexity theory, machine learning foundations, parallel computation systems, computational neuroscience, and evolutionary computation. His work bridges artificial and natural computational phenomena, addressing fundamental limitations in both engineered systems and biological processes. Key contributions include the development of the PAC (Probably Approximately Correct) learning model, holographic algorithms, robust logics for reconciling reasoning and learning, and theoretical frameworks for understanding cortical computation and evolvability. His publication record demonstrates sustained impact across decades, with recent work focusing on cortical computation primitives, multi-core algorithm design, and evolutionary dynamics with drifting targets. These publications reveal a consistent trajectory toward understanding computational principles in both artificial systems and biological cognition. Nevanlinna Prize (1986) Knuth Award (1997) EATCS Award (2008) A.M. Turing Award (2010) Fellow of the Royal Society Member of the National Academy of Sciences Valiant's research program integrates theoretical rigor with profound questions about natural computation, maintaining active engagement with both computer systems design and fundamental neuroscience questions. His work continues to influence multiple disciplines through formal frameworks that address computational limitations in learning, evolution, and neural processing.

Zhi Huangمشاهده پروفایل

مدرس

• biomedical AI
• human-AI collaboration
• medical image platforms
• +۲ مورد دیگر

Dr. Zhi Huang serves as an Instructor and incoming Assistant Professor in the Department of Pathology and Laboratory Medicine, with a secondary appointment in the Informatics Division of the Department of Biostatistics, Epidemiology, and Informatics. His academic work bridges biomedical research with artificial intelligence to advance healthcare solutions. His research expertise spans critical areas in medical AI: Biomedical AI : Developing AI models for clinical decision support Human-AI Collaboration : Designing intuitive interfaces for clinician-AI teamwork Medical Image Platforms : Creating scalable infrastructure for medical imaging analysis Digital Pathology : Implementing AI-driven tissue analysis systems Precision Medicine : Tailoring treatments using genomic and clinical data integration Analysis of his publication record reveals a strong interdisciplinary trajectory connecting computer vision, multi-agent systems, and clinical applications. His work demonstrates consistent innovation in translating autonomous systems research—particularly in scene graph generation, motion planning, and visual question answering—into medical contexts including digital pathology platforms and precision diagnostics. Recent contributions emphasize open-source frameworks for accessible medical AI development.