جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.

• Table Representation Learning
• Generative Models for Tabular Data
• Semantic Data Type Detection
• +۴ مورد دیگر

Madelon Hulsebos is a Researcher at CWI in Amsterdam, where she leads the Table Representation Learning (TRL) Lab and contributes to the Database Architectures group. She is also a faculty member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Amsterdam unit. Her career bridges academia and industry, including a postdoctoral fellowship at UC Berkeley and prior industry experience in automating data analysis pipelines with ML. Education : PhD in Computer Science (University of Amsterdam, 2023), with research at Sigma Computing and MIT; Postdoctoral Fellow (UC Berkeley, 2024). Her research focuses on establishing tabular data as a key AI modality through Table Representation Learning , generative models for relational data, and robust systems for data analysis. Key interests include: Relational Table Embeddings LLMs for QA/text2SQL and data wrangling Retrieval over Data Lakes and Databases Agentic Systems for Data Science Democratizing insights from structured data Recent work highlights trends in benchmarking table retrieval (TARGET), semantic column detection (AdaTyper, Sherlock), and large-scale tabular data curation (GitTables, SchemaPile). These projects address challenges in metadata utilization, data lake search, and end-to-end systems for structured data. She has secured significant funding, including the NWO AiNed Fellowship Grant ($1M) for her 5-year DataLibra project. Madelon organizes workshops at NeurIPS , SIGMOD , and ACL , and reviews for top venues like VLDB and NeurIPS. Scientific Awards : NWO AiNed Fellowship Grant ($1M) She actively mentors students and collaborates on European AI initiatives, including monthly TRL seminars and workshops. Her lab's tools (GitTables, TARGET) are widely adopted for training foundation models on tabular data.

• Natural Language Processing
• Knowledge Graphs
• Machine Learning
• +۵ مورد دیگر

Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.

• Natural Language Processing
• Natural Language Generation
• Machine Learning
• +۴ مورد دیگر

Prof. Anya Belz is Full Professor of Computer Science at Dublin City University's School of Computing and Science Lead at ADAPT Research Centre. A leading NLP researcher with PhD-level expertise, she specializes in natural language generation, evaluation methodologies, and multimodal systems. Recipient of multiple best paper awards and NAACL Test of Time Award nomination. Research innovations include foundational work on statistical language generation (deployed in weather forecasting systems), comparative evaluation frameworks, vision-language integration, and reproducibility quantification. Current EPSRC-funded ReproHum project coordinates 20 global labs studying evaluation consistency. Achievements : Developed industry-deployed generation systems for accessibility applications Pioneered cross-modal alignment techniques for image description Authored 100+ publications spanning generation, evaluation, and reproducibility

• Art History
• Early Modern European Art
• Museum Studies
• +۲ مورد دیگر

Robert Wellington serves as Associate Professor and Director of the Centre for Art History & Art Theory at the School of Art & Design, Australian National University (ANU), within the College of Arts & Social Sciences. His academic leadership focuses on advancing art historical research and public engagement through institutional initiatives. His research spans Art History, Early Modern European Art (particularly Louis XIV's France), Museum Studies, Public Humanities, and Cultural Heritage. Wellington investigates transcultural object exchange, royal iconography, and identity construction through material culture, with emphasis on Versailles, medals, and marginalia. His work bridges historical scholarship with contemporary public humanities applications in museums and digital archives. Analysis of his 2020-2024 publications reveals concentrated exploration of Louis XIV's cultural legacy, Versailles' global impact, and the evolving role of humanities institutions. He pioneers digital critical editions while examining how objects shape identity across cultural boundaries, consistently connecting historical practices to modern museum and archival challenges. No scientific awards were documented in the provided materials. As Centre Director, Wellington oversees research programs and institutional partnerships, though specific student advisees and grant details remain unlisted in available sources. His leadership facilitates interdisciplinary projects including the Jean-Benjamin de Laborde digital edition initiative. The Centre for Art History & Art Theory functions as a research hub under Wellington's direction, driving public humanities innovation through the ANU's Art & Design Seminar Series, Visiting Artist Program, and collaborative projects with national collections. Current initiatives focus on digital heritage and community-engaged scholarship within the School of Art & Design framework.

Devis Tuiaمشاهده پروفایل

دانشیار

• Earth Observation
• Remote Sensing
• Machine Learning
• +۵ مورد دیگر

Devis Tuia serves as Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), holding appointments in the Institute of Environmental Engineering (IIE) within the School of Architecture, Civil and Environmental Engineering (ENAC). He leads the Environmental Computational Science and Earth Observation Laboratory (ECEO) since 2020 and contributes to EPFL's Doctoral Program in Civil and Environmental Engineering. His academic journey began in Lausanne with studies at UNIL and EPFL, culminating in a PhD in remote sensing from UNIL. Postdoctoral research followed at institutions in Valencia, Boulder, and EPFL, focusing on machine learning model adaptation. He progressed from Research Assistant Professor at University of Zurich to Associate and Full Professor at Wageningen University before joining EPFL. Tuia's research bridges Earth observation with artificial intelligence, specializing in interpretable deep learning for environmental applications. His lab develops algorithms for making remote sensing accessible, with particular emphasis on digital wildlife conservation through automated censuses using drone and satellite imagery. Current projects tackle the 'black box' problem in environmental modeling while advancing spatial intelligence for sustainable urban development. His 2023-2025 publication portfolio reveals three dominant trends: (1) species distribution modeling using incomplete observations, (2) multimodal fusion of satellite/drone data with textual descriptions, and (3) interpretable AI frameworks for environmental decision-making. This work consistently addresses real-world challenges like wildfire forecasting and biodiversity monitoring. As an educator, Tuia supervises 12 current PhD students and has graduated 4 former EPFL doctoral candidates. His teaching portfolio includes Frontiers of Deep Learning for Engineers , Sensing and Spatial Modeling for Earth Observation , and Image Processing for Earth Observation courses. The ECEO laboratory maintains active collaborations with ESA-NASA initiatives and conservation organizations globally.

• Multimodal Discourse Analysis
• Systemic Functional Linguistics
• Digital Humanities
• +۶ مورد دیگر

Professor Kay O'Halloran serves as Chair Professor and Head of Department of Communication and Media within the School of the Arts at the University of Liverpool since August 2019. She concurrently holds the position of Co-Director for the Digital Media and Society Institute (DMSI), demonstrating significant leadership across academic and research domains. Her career spans prestigious institutions including Curtin University (2013-2019) and National University of Singapore (1998-2013), where she directed the Multimodal Analysis Lab and served as Deputy Director of the Interactive & Digital Media Institute. PhD from Murdoch University (1996) Postdoctoral position at Martin Luther University (1997-1998) Visiting Distinguished Professor at Shanghai Jiaotong University (2017-2020) Professor O'Halloran's research focuses on multimodal discourse analysis, particularly the interaction of language with visual and mathematical resources. Her pioneering work in systemic functional multimodal discourse analysis (SF-MDA) has significantly impacted mathematics education and digital communication studies. Current research emphasizes digital tools for multimodal analysis and mixed methods approaches to big data analytics, addressing contemporary challenges in misinformation and public health communication. Her recent publications reveal strong thematic concentration in pandemic-related communication analysis, misinformation resistance mechanisms, and multimodal argumentation across social and cultural contexts. The 15 most recent articles demonstrate consistent application of multimodal frameworks to urgent societal issues including public health crises, political discourse, and digital literacy challenges. Founding editor of Routledge Studies in Multimodality book series (57+ volumes) Over 150 publications in leading international journals 50+ plenary/keynote presentations globally Competitive funding from National Research Foundation, MOE Singapore, US Air Force, ARC, NHMRC, and others Professor O'Halloran has directed interdisciplinary research teams comprising social scientists, computer scientists, and designers. Her leadership extends to developing commercial multimodal analysis software (Multimodal Analysis Image and Video) adopted internationally. Current projects include Eurovision 2023 wellbeing evaluation and COVID-19 misinformation immunity development, demonstrating continued relevance to contemporary societal challenges.

• Machine Learning
• Artificial Intelligence
• Large Language Models
• +۷ مورد دیگر

Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine. Education: PhD in Machine Learning, School of Computer Science, Carnegie Mellon University Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery. Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation. Scientific Awards: NIH MIRA Award (2025) NSF CAREER Award (2024) Best Graduate Teacher Award – UCSD ECE (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) UCSD Faculty Career Development Award (2022) Tencent Faculty Award (2021) Outstanding Reviewer – ICLR (2021) AMIA Doctoral Dissertation Award Finalist (2020) Amazon AWS Research Award (2020) Tencent AI-Lab Faculty Award (2020) Innovator Award – Pittsburgh Business Times (2018) Siebel Scholarship (2014) Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students. Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models . His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.

Paola Cascante-Bonillaمشاهده پروفایل

استادیار

• Computer Vision
• Natural Language Processing
• Embodied AI
• +۵ مورد دیگر

Paola Cascante-Bonilla is an Assistant Professor in the Department of Computer Science at Stony Brook University, with expertise in computer vision, natural language processing, and embodied AI. Her research focuses on developing systems for compositional reasoning, common-sense inference, and trustworthy AI using vision-language models, while addressing cultural bias and explainability challenges.

Sungsoo Ray Hongمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Computer-Supported Cooperative Work
• Data Science
• +۵ مورد دیگر

Sungsoo Ray Hong is an Assistant Professor at George Mason University's Department of Information Sciences and Technology, directing the Alignment Lab (A-lab). His research focuses on bridging human mental models with AI systems through Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW). Dr. Hong earned his PhD in Human Centered Design and Engineering at the University of Washington. Research Domains: Human-AI Collaboration, Interactive Data Annotation, Steerable Deep Neural Networks, and AI-Driven Applications Key Grants: NSF Future of Work at the Human-Technology Frontier (2021-2024, $160K) Email: shong31@gmu.edu Hong's research explores how humans interact with machine learning systems to improve decision-making and task productivity. His lab develops tools for collaborative data annotation, interpretable model building, and AI-augmented creativity in professional contexts. Recent work includes applications for neurodiverse workers and comic professionals. The 15 most recent publications show strong focus on Human-AI collaboration (7 articles), interactive systems design (11 articles), and accessibility applications (5 articles). Topics span from explainable AI frameworks to collaborative annotation interfaces and creative industry tools. Dr. Hong maintains active collaborations with KAIST researchers, including Dr. Jaegul Choo and Dr. Juho Kim. His lab offers remote internships and accepts PhD students for summer positions.

Mark Steedmanمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Combinatory Categorial Grammar
• +۲ مورد دیگر

Mark Steedman is Professor of Cognitive Science at the University of Edinburgh's School of Informatics, with adjunct appointment at University of Pennsylvania. His research spans computational linguistics, AI, and cognitive science, focusing on Combinatory Categorial Grammar (CCG) and its applications. His research examines: Combinatory Categorial Grammar parsing and semantics Language model capabilities and limitations Cross-linguistic semantic inference Brain modeling of language processing Recent publications analyze hallucination sources in large language models, cross-linguistic entailment graphs, and brain-computer parallels in structure-building. He develops computational models integrating symbolic and distributional approaches to semantics. Honors include ACL Lifetime Achievement Award (2018) and George E. Davis Medal (2001). He serves on editorial boards of major linguistics journals and has authored influential books including 'The Syntactic Process' and 'Taking Scope'.

Iro Armeniمشاهده پروفایل

استادیار

• Computer Vision
• 3D Scene Understanding
• Mixed Reality
• +۳ مورد دیگر

Dr. Iro Armeni is Assistant Professor of Civil and Environmental Engineering at Stanford University, leading the Gradient Spaces research group. Her interdisciplinary research bridges architecture, civil engineering, and computer vision to develop data-driven methods for sustainable and adaptive built environments. Professor Armeni's work focuses on creating gradient environments that blend physical and digital realities through mixed reality technologies. She develops computational methods for 3D scene understanding, generative design, and adaptive spaces that respond to human needs. Her research integrates AI with architectural design to improve sustainability, inclusivity, and reusability of built spaces. Current projects include 3D scene graph representations, automated BIM modeling from visual data, and neuro-symbolic approaches for design optimization. She has developed tools like HoloLabel (AR semantic labeling) and SemSpray (VR annotation) for construction information management. Professor Armeni holds a PhD from Stanford University, supported by a Google PhD Fellowship, and completed postdoctoral research at ETH Zurich with an ETH Fellowship. She teaches courses on Computer Vision for the Built Environment and Mixed Reality applications.

• Statistical Machine Learning
• Deep Learning
• Computational Biology
• +۲ مورد دیگر

Rui Li is an Associate Professor in the Ph.D. program at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. She directs the Lab for Use-inspired Computational Intelligence (LUCI), focusing on AI applications in computational biology and medical imaging. Education includes: B.Sc. in Computer Science, Harbin Institute of Technology M.Sc. in Computer Science, Tianjin University of Technology Ph.D. in Computing and Information Sciences, RIT Research integrates statistical machine learning with computational biology, medical image analysis, and human visual attention modeling. Current projects include deep learning for histopathology, multimodal medical image registration, and gene network inference. Publications demonstrate consistent focus on medical AI applications, with recent advances in unsupervised image registration, interactive segmentation, and multimodal fusion techniques. Key trends include self-supervised learning, uncertainty-aware models, and human-AI collaboration frameworks. Awards include the NSF CAREER Award for developing adaptive machine intelligence systems. Advises multiple PhD students on projects spanning deep learning architectures, biomedical image analysis, and biological network modeling. Leads several NSF-funded projects including human-centered image understanding systems and gene-protein network inference tools. Directs LUCI lab investigating machine learning for healthcare applications and teaches graduate courses in Statistical Machine Learning and Deep Learning.

Olga Vekslerمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Olga Veksler is a Professor at the University of Waterloo's Department of Computer Science, part of the Faculty of Mathematics. She holds a Ph.D. and M.Sc. from Cornell University (1999) and a B.A. from New York University (1995). Her research focuses on computer vision, machine learning, and discrete optimization, with notable contributions to image segmentation, graph algorithms, and deep learning integration. Her work emphasizes semantic segmentation, salient object detection, and efficient optimization techniques for graphical models. Education: Ph.D. in Computer Science, Cornell University, 1999 M.Sc. in Computer Science, Cornell University, 1999 B.A. in Computer Science, New York University, 1995 Her research explores intersections between machine learning and traditional computer vision challenges, particularly leveraging graph-based optimization and CRF models. Recent trends in her work include weakly supervised learning, sparse non-local CRF applications, and test-time adaptation strategies for salient object detection. She has pioneered methods for shape priors in multi-object segmentation and efficient graph-cut algorithms. Her advising and grant activities are foundational to her research, though specific grant details are not listed here. She maintains a lab focused on advancing computer vision through algorithmic innovation, with contributions to both theoretical frameworks and practical applications in medical imaging and scene understanding.

Jim Smithمشاهده پروفایل

استاد

• Interactive Artificial Intelligence
• Statistical Disclosure Control
• Privacy Leakage from AI Models
• +۷ مورد دیگر

Jim Smith is a Professor in Interactive Artificial Intelligence at the University of the West of England (UWE), Bristol, affiliated with the School of Computing and Creative Technologies and the Department of Computer Science and Creative Technologies. He serves as Director of the Computer Science Research Centre and leads the AI@UWE theme. His research is supported by UKRI, Innovate UK, and partnerships with organizations including Health Data Research UK, Office for National Statistics, NHS Scotland, and DSTL. University: University of the West of England School: School of Computing and Creative Technologies Department: Department of Computer Science and Creative Technologies Role: Professor in Interactive Artificial Intelligence Leadership: Director, Computer Science Research Centre Research Interests : Jim Smith's work focuses on Interactive Artificial Intelligence, particularly at the intersection of AI and privacy preservation when using sensitive data for public good. His research includes statistical disclosure control, privacy leakage from AI models, evolutionary computation, machine learning, and systems that learn through human interaction or self-adaptation. He explores how AI can automate privacy checks in research outputs and assess vulnerabilities in trained models. Recent Publications : His recent work spans AI privacy in trusted research environments (e.g., SACRO, SDC-Reboot), dialogue act classification, human-robot interaction, and visualization of deep learning models. Themes include privacy-preserving AI, automated disclosure control, interactive machine learning, and neuromorphic computing. Machine Learning & Privacy Evolutionary Computation Interactive AI Systems Human-Computer Interaction Statistical Disclosure Control Federated Learning Security Scientific Awards : No specific awards are mentioned in the provided texts. Advising and Grants : He currently supervises PhD students on topics including spatio-temporal air quality modeling, federated learning privacy, and threat detection in mobile networks. He leads Innovate UK and UKRI-funded projects such as SACRO and SDC-Reboot, focusing on AI-driven solutions for data confidentiality in public sector research. Interactive Machine Learning for Claim Settlement (Innovate UK) SDC-Reboot (DARE UK/Health Data Research UK) Threat Identification in Mobile Networks (Ribbon Communications) Labs and Teams : He leads the AI@UWE initiative and the Computer Science Research Centre at UWE. His work involves collaboration through DARE UK and open-source development via the AI-SDC GitHub organization, which hosts tools from SACRO and GRAIMATTER projects.