جستجوی استاد راهنمای دانشگاهی

Davood Rafiei is a Professor in the Faculty of Science at the University of Alberta, specializing in the Department of Computing Science. His research bridges databases, natural language processing, and web technologies, with current projects on large language models and data integration. He holds a B.Sc. in Computer Engineering from Sharif University of Technology (1990), an M.Sc. in Computer Science from the University of Waterloo (1995), and a Ph.D. from the University of Toronto (1999). His work explores semantic annotation, table transformations, and adversarial analysis in social media. Recent publications emphasize applications of LLMs to structured data tasks like text-to-SQL conversion and knowledge graph integration. Research trends show consistent innovation in NLP-driven data management solutions, with collaborations spanning Google, Kyoto University, and the University of Paris. No awards, grants, or lab/team details are documented.

• Natural Language Interfaces to Databases
• Data Integration
• Large Language Models
• +۱ مورد دیگر

Weitong Zhang is an Assistant Professor at the School of Data Science and Society at the University of North Carolina at Chapel Hill. His research focuses on developing robust reinforcement learning and machine learning algorithms for scientific discovery applications, with theoretical guarantees. He completed his PhD in Computer Science at UCLA and is actively recruiting students for research opportunities. Research interests span reinforcement learning theory, efficient algorithms for decision-making in complex systems (LLMs, agentic systems), and AI applications in chemistry/physics. His work emphasizes foundational understanding and practical implementations with interdisciplinary potential. Awards include the NVIDIA Academic Grant (2025) and UCLA Dissertation Year Fellowship (2023).

• reinforcement learning
• machine learning

Vincent Gripon is a Full Professor at IMT Atlantique, France's leading engineering institutions. He leads the BRAIn team within Lab-STICC (CNRS UMR 6285), focusing on Artificial Intelligence intersections with Deep Learning, Signal Processing, and Neuroimaging. IMT Atlantique Lab-STICC (CNRS UMR 6285) Mathematical and Electrical Engineering (MEE) Department Research Interests span Artificial Intelligence with specialization in: Few-Shot Learning Neural Network Pruning & Quantization Brain-Computer Interfaces Graph Signal Processing Thrifty AI for Edge Computing Geometry-Driven Model Optimization Selected Publications demonstrate his focus on efficient AI systems through: Training-free adaptation techniques for large models Quantization-aware hardware design Manifold intrusion prevention methods Neuroimaging data analysis frameworks Honors & Distinctions : CVPR 2025 publication (ProKeR) AMD Open Hardware Competition winner (2023) DCASE 2023 Task 5 Jury Award

• Artificial Intelligence
• Deep Learning
• Few-Shot Learning
• +۸ مورد دیگر

Nic Lane is a Professor of Machine Learning Systems at the Department of Computer Science and Technology, University of Cambridge. He leads research at the intersection of machine learning systems, computer architecture, and edge computing. Research Focus : Scalable and robust end-to-end ML systems, multi-modal data processing (speech, vision, location), distributed execution, and hardware-software co-design. Teaching : Offers courses on Deep Neural Networks, Federated Learning, and Principles of Machine Learning Systems. Labs : Leads the Cambridge Machine Learning Systems (CaMLSys) group. His recent work emphasizes Federated learning for large language models Hardware-efficient AI training and inference Carbon footprint analysis of AI systems AI regulation compliance frameworks 3D rendering acceleration via neural methods

• Machine Learning Systems
• Federated Learning
• Computer Architecture
• +۱۲ مورد دیگر

Qiaozhu Mei is a Professor at the University of Michigan, serving as Associate Dean for Research and Innovation at the School of Information. He holds a courtesy appointment as a Professor in the Department of Electrical Engineering and Computer Science. Previously, he founded the Master of Applied Data Science program. He earned his PhD from the University of Illinois at Urbana-Champaign and a Bachelor's degree from Peking University. His research focuses on AI, including data mining, information retrieval, machine learning, and NLP, with applications in healthcare, social networks, and scientific discovery. He leads The Foreseer Group, exploring human-centric AI systems. Key awards include the ACM Distinguished Member (2017) and NSF CAREER Award (2011). He has served as General Co-Chair for SIGIR 2018 and holds editorial roles in journals like JMLR and IEEE TBD. Teaching spans courses such as Information Retrieval, Big Data Analytics, and Search & Recommender Systems. His work emphasizes practical AI impact through interdisciplinary collaboration and ethical considerations.

• Artificial Intelligence
• Data Mining
• Information Retrieval
• +۵ مورد دیگر

Ruihong Qiu is a Lecturer at the School of Electrical Engineering and Computer Science, The University of Queensland, with an ARC DECRA fellowship (2025-2027). Research focuses include graph neural networks, large language models, and sequential data modeling. Research Interests: Advanced graph neural network techniques Recommender systems using reinforcement learning Time series foundation models Legal case retrieval with text-attributed graphs Recent Research Trends: Publications emphasize graph condensation for continual learning, OOD detection in graph models, and LLM integration for legal and public health applications. Scientific Recognition: ARC Discovery Early Career Researcher Award ATSE Tech Bridge Grant Supervision & Collaboration: Currently supervising multiple PhD students across topics including legal case retrieval, graph representation learning, and time series analysis, typically in collaboration with Professor Helen Huang and other co-advisors.

• Data Science
• Graph Neural Networks
• Recommender Systems
• +۵ مورد دیگر

Associate Professor Wei Liu is a full-time academic in the Department of Computer Science and Software Engineering at The University of Western Australia (UWA), leading the UWA Centre for Natural and Technical Language Processing. Her research focuses on knowledge discovery from natural language text, generative AI, semantic technologies, deep learning for knowledge graphs, and sequential data mining. She holds a PhD from the University of Newcastle (2003) and an MEng from Huazhong University of Science and Technology (China). Research Interests: Generative AI and Large Language Models (LLM) Multimodal Knowledge Graph Construction Human-AI Collaboration Systems Clinical Data Analysis (Ophthalmology) Technical Language Processing for Maintenance Data Neural-Symbolic Computation Recent Work Trends: Her publications emphasize multimodal systems integration, knowledge graph refinement, and adversarial AI applications. Notable 2025 work includes Docs2KG (human-LLM collaboration), Auto-Regressive Diffusion for 3D interactions, and spherical embeddings for logical queries. Scientific Awards: First Prize in IEEE ICDM/ICBK Knowledge Graph Contest (2020) Best Contribution to Science Award at DICTA 2020 2022 Students Choice Award for academic support Advising & Grants: Leads 25 active grants including ARC Training Centres in Critical Resources and Maintenance Data Science. Collaborates with industry partners like Aspire Digital Group and Minerals Research Institute of Western Australia. Labs/Teams: Directs UWA's Natural and Technical Language Processing Centre, focusing on projects like maintenance work order analysis, geological survey data fusion, and clinical data integration in ophthalmology.

• Natural Language Processing
• Generative AI
• Knowledge Graph Construction
• +۶ مورد دیگر

Dr. Jin Hong is a Senior Lecturer at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing and the Department of Computer Science and Software Engineering. Previously, he served as a Postdoctoral Research Fellow at the University of Canterbury (NZ) from 2016 to 2018, where he also earned his PhD in 2015. His research focuses on security modeling, intelligent security solutions, human factors in cybersecurity, and education. Education: PhD in Computer Science, University of Canterbury, 2015 Postdoctoral Research Fellow, University of Canterbury, 2016–2018 Research Interests: Security modeling and analysis Moving Target Defense (MTD) techniques Privacy-preserving technologies Cybersecurity in robotics and AI Cybersecurity education Awards: Claude McCarthy Fellowship ($8,000 NZD, 2014–2015) Grants: Australia DST Research Grant ($200,000 AUD, 2020–2021) Qatar National Research Fund ($726,000 USD, 2016–2019) Multiple DSC Collaborative Research Grants (totaling $300,000 AUD, 2023–2025) Teaching: CITS2006 – Defensive Cybersecurity CITS3006 – Penetration Testing Past courses include CITS1003 (Intro to Cybersecurity) and CITS2401 (Computer Analysis & Visualization)

• Cybersecurity
• Network Security
• Moving Target Defense
• +۳ مورد دیگر

Marco Kuhlmann is a Professor and Deputy Head of Department at the Department of Computer and Information Science (IDA) at Linköping University. He leads the Natural Language Processing Group and is part of the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His research focuses on natural language processing (NLP), combining theoretical computer science and machine learning to develop algorithms for semantic parsing and contextualized language representations. Key projects include collaborations with Chalmers University and Recorded Future to enhance LLM interpretability and the TrustLLM initiative for European languages. He teaches courses such as Language Technology, Natural Language Processing, and Deep Learning for NLP across multiple faculties. His work emphasizes practical applications of NLP in human-computer interaction and text mining. Kuhlmann actively engages in public outreach, explaining AI advancements through events like European Researchers’ Night, where he discusses societal impacts of language models like ChatGPT. His research spans semantic parsing frameworks, neural network architectures, and the integration of contextualized representations. Ongoing efforts address challenges in low-resource languages and scalable learning methods for imbalanced datasets. Kuhlmann’s contributions include benchmark datasets for dependency parsing and community-building initiatives for NLP evaluation standards.

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

Dr. Iman Azimi is a Postdoctoral Scholar at the Donald Bren School of Information & Computer Sciences, University of California, Irvine. Their work focuses on advancing healthcare through wearable technology, machine learning, and IoT systems. Key areas include maternal health monitoring, physiological signal analysis, and AI-driven health solutions. Research interests span health informatics, biomedical signal processing, and personalized medicine. Notable projects include long-term maternal monitoring using IoT devices and LLM-powered agents for physiological data analysis. Azimi also explores causal ML approaches to understand health impacts of lifestyle factors during pregnancy. Recent work demonstrates contributions in smartwatch-based vital sign estimation, explainable AI for wearable data, and privacy-preserving mental health monitoring systems. Their research bridges computational methods with clinical applications, aiming to improve patient outcomes through innovative technology integration. Key platforms developed include ZotCare and PHAS, showcasing edge-assisted healthcare systems and open-source analytics stacks. Current efforts emphasize leveraging large language models and transformers for healthcare insights while addressing ethical AI challenges in medical chatbots.

• Health Informatics
• Wearable Technology
• Machine Learning in Healthcare
• +۳ مورد دیگر

Assoc Prof Wang Jue is an Associate Professor at the School of Social Sciences and Director of the Nanyang Centre for Public Administration (NCPA) at Nanyang Technological University (NTU). She holds a PhD in Public Policy from Georgia Tech. Her work focuses on innovation management, science policy analysis, and AI applications in governance. Current projects explore AI implications for policy research and cross-border innovation systems. Education: PhD in Public Policy (Georgia Tech) Prof Wang’s research interests span Entrepreneurship and Innovation , Artificial Intelligence Governance , Regional Innovation Systems , and Public Policy Evaluation . She examines how technological advancements reshape policy frameworks and global talent dynamics, with notable studies on Hong Kong-Shenzhen innovation collaboration and global academic labor markets. Her recent articles analyze disciplinary AI adoption patterns in education, policy diffusion mechanisms in China, and LLM biases in public opinion modeling. These studies highlight emerging trends in computational policy analysis and cross-border knowledge ecosystems. Labs/Teams: Leads the Nanyang Centre for Public Administration, focusing on public sector innovation and governance research. Grants/Advising: Active in securing research funding on science policy and innovation metrics, though specific grants are not detailed here.

• Entrepreneurship and Innovation
• Library and Information Management
• Artificial and Augmented Intelligence
• +۳ مورد دیگر

Sean Trott is Assistant Teaching Professor in Cognitive Science at UC San Diego, specializing in language comprehension, computational modeling, and large language models. His research examines how humans and language models represent meaning and resolve ambiguity. Research combines behavioral experiments, corpus analysis, and LLM probing to investigate lexical ambiguity, context effects, and semantic representation. Current projects explore cognitive plausibility of LLMs and computational accounts of language processing. Publications analyze word meaning representation, theory of mind capabilities in LLMs, cross-linguistic differences in language processing, and educational applications of large language models. Recent work appears in Psychological Review, Cognitive Science, and ACL proceedings. Teaching covers computational social science, programming, statistics, and language-related courses. Developed the RAW-C dataset for studying ambiguous words in context. Maintains the Counterfactual newsletter on cognitive science and AI. Laboratory develops methods for using LLMs as cognitive models and evaluates their alignment with human language processing.

• Computational Linguistics
• Cognitive Modeling
• Large Language Models
• +۳ مورد دیگر

Eunsol Choi is an Assistant Professor in Computer Science at New York University's Courant Institute and the Data Science program. Previously, she held an Assistant Professor position at the University of Texas at Austin (2020–2023) and was a researcher at Google AI (New York City). She earned her Ph.D. in Computer Science from the University of Washington, advised by Luke Zettlemoyer and Yejin Choi. Her research focuses on advancing NLP and machine learning, particularly in areas like continual learning, long-form question answering (QA), and spoken language processing. Key projects include developing benchmarks like PropMEND, SituatedQA, and AmbigDocs, as well as exploring model robustness and human-LM interaction. Her work emphasizes practical applications, such as improving QA systems, reducing model hallucinations, and scaling style-prompted TTS datasets. Research Interests : Continual Learning & Knowledge Editing Long-form QA and Multimodal Reasoning Human-Machine Interaction in NLP Spoken Language and Speech-to-Speech Translation Benchmark Design for NLP Evaluation Her lab's research has been supported by grants from Google, Open Philanthropy, Cisco, Sony, Home Depot, Apple, and the NSF. Notable awards include the Outstanding Paper Award at EMNLP 2021 for SituatedQA. Key Contributions : Proposed frameworks for knowledge propagation in LLMs. Designed datasets addressing ambiguity in QA (e.g., AmbigDocs). Advanced TTS systems with rich style annotations (e.g., ParaSpeechCaps). Lab & Students : Her research group (nicknamed by students as the (E)xplaining and (U)nderstanding (N)ature and (S)tructure/Synthesis (O)f (L)anguage lab) has produced over 20 Ph.D. and undergraduate researchers, many advancing to roles at top tech companies and academia.

• Natural Language Processing (NLP)
• Machine Learning
• Question Answering (QA)
• +۶ مورد دیگر

Dr. Donghwan Shin is a Lecturer in Software Testing at the School of Computer Science, University of Sheffield since 2022. He holds a BSc (2006-2010), MSc (2010-2012), and PhD (2012-2018) from KAIST, South Korea. Prior to joining Sheffield, he was a Research Associate (2018-2020) and Research Scientist (2020-2022) at SnT, University of Luxembourg. Education PhD in Computer Science - KAIST (2012-2018) MSc in Computer Science - KAIST (2010-2012) BSc in Information Technology - KAIST (2006-2010) Research Interests His research focuses on mutation testing, testing for ML-enabled cyber-physical systems (e.g., automated driving systems), and log analysis including model inference and anomaly detection. He develops novel testing methodologies for complex AI systems and reliability engineering solutions. Publication Trends Recent publications demonstrate a strong focus on testing machine learning systems, autonomous driving validation, log-based failure prediction, and novel testing methodologies. His work consistently appears in top venues including ICSE, ICST, and IEEE Transactions on Software Engineering. Grants & Projects PI for UKRI grant: "SimpliFaiS: Simplification of Failure Scenarios for Machine Learning-enabled Autonomous Systems" (2024-2026, £464,344) Professional Activities Leads research in the Software Testing Lab and collaborates with industry partners on testing autonomous systems. Serves as School Student Experience Lead.

• Mutation Testing
• ML-enabled Cyber-Physical Systems
• Log Analysis
• +۳ مورد دیگر

Prof. Amin Beheshti is a Full Professor of Data Science at Macquarie University and Adjunct Professor of Computer Science at UNSW Sydney. He leads the Centre for Applied Artificial Intelligence, the Data Science Lab, and the Big Data Society. His research focuses on applying AI to solve real-world challenges in healthcare, cybersecurity, and industry. Beheshti holds a PhD in Computer Science from UNSW Sydney and has secured over $40 million in research funding through 40+ projects. Education: PhD in Computer Science and Engineering (UNSW Sydney, 2013) Master's and Bachelor's in Computer Science (First Class Honours) Research Interests: Artificial Intelligence Graph Neural Networks Healthcare Analytics Cybersecurity AI Ethics & Commercialization Awards & Recognition: Australian AI Award (2024): AI Academic/Researcher of the Year Macquarie Excellence Awards (2022–2023) APA Scholarship (2009–2012) Grants & Projects: 40+ research projects funded ($40M+) Leadership in Federated Learning, Healthcare AI, and Cybersecurity initiatives Labs & Initiatives: Centre for Applied Artificial Intelligence Data Science Lab Big Data Society

• Artificial Intelligence
• Data Science
• Machine Learning
• +۵ مورد دیگر