جست‌وجوی استاد و پژوهشگر

Andrea Vedaldiمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Unsupervised Learning
• +۳ مورد دیگر

Andrea Vedaldi is a Professor of Computer Vision and Machine Learning at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). He specializes in unsupervised methods for understanding images and videos, focusing on 3D geometry and semantics. His research bridges foundational AI and practical applications, with contributions to generative models, neural fields, and self-supervised learning. Education: PhD in Computer Science (2008), University of California, Los Angeles MSc in Computer Science (2005), UCLA BSc in Information Engineering (2003), University of Padua Research Interests: Unsupervised learning, 3D perception, generative AI, neural rendering, and scalable vision systems. His work emphasizes ethical, responsible AI aligned with ERC-funded projects like UNION (ERC Consolidator Grant). Key Contributions: Co-developer of VLFeat and MatConvNet libraries Leader in 3D reconstruction and diffusion models (e.g., CatFree3D) Recipient of the PAMI Thomas S. Huang Prize and multiple best paper awards Grants & Service: Principal Investigator on £2.3M ERC Consolidator Grant (UNION) Co-organizer of major conferences (ECCV 2020 Program Chair, CVPR 2023 Area Chair) Reviewer for top journals/conferences (PAMI, CVPR, NeurIPS) Labs & Teams: VGG Group at Oxford, collaborating on projects like Meta 3D Gen and Common Objects in 3D (CO3D).

Mark Steedmanمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Cognitive Science
• Natural Language Processing
• +۹ مورد دیگر

Mark Steedman is a Professor in the School of Informatics at the University of Edinburgh, where he conducts research in Artificial Intelligence, Computational Cognitive and Social Science, and Natural Language and Speech Processing. He is affiliated with the Institute for Language, Cognition and Computation (ILCC), the Centre for Speech Technology Research (CSTR), and the Human Communications Research Center (HCRC). He also holds an adjunct professorship in Computer and Information Science at the University of Pennsylvania. His research focuses on Combinatory Categorial Grammar (CCG) , computational linguistics , prosody and intonation , temporal semantics , gesture in communication , and computational music analysis . He has authored foundational books including Surface Structure and Interpretation , The Syntactic Process , and Taking Scope . The recent publications reflect a strong trend toward integrating formal grammatical frameworks like CCG with modern neural and distributional models, particularly in semantic parsing, entailment reasoning, and cognitive modeling. His work bridges symbolic and statistical approaches in NLP, often focusing on robust, wide-coverage parsing and semantic interpretation. Best Paper Award at AACL/IJCNLP 2023 for 'Smoothing Entailment Graphs with Language Models' Best Paper Award at ACL 2023 for 'Extrinsic Evaluation of Machine Translation Metrics' Influential Paper Award 2017 from IFAAMAS for 'Animated Conversation' Mark Steedman has supervised numerous PhD students and collaborated widely across institutions. He leads research in formal grammar applications to cognitive modeling, dialogue, and multimodal communication. His lab contributes to CCG software and semantic parsing tools, and he continues to be actively involved in advancing the integration of symbolic and neural AI.

Lourdes Agapitoمشاهده پروفایل

استاد

• 3D Vision
• Non-Rigid Structure from Motion (NR-SFM)
• Computer Vision
• +۵ مورد دیگر

Lourdes Agapito is a Professor of 3D Vision at the Department of Computer Science, University College London (UCL), within the Faculty of Engineering Sciences. She leads research in Non-Rigid Structure from Motion (NR-SFM) and 3D reconstruction from monocular video sequences. Her work addresses dynamic scenes, deformable objects, and articulated structures, with applications in robotics and computer vision. She holds an ERC Starting Grant (2008–2014) and led the EU Horizon 2020-funded Second Hands project (2014–2019), collaborating with institutions like EPFL and KIT to develop robots with 3D visual perception for maintenance tasks. Her research group focuses on dense optical flow estimation, video registration, and deformable tracking. Agapito’s research interests include monocular 3D reconstruction, non-rigid motion analysis, and neural approaches to 3D modeling. She has supervised multiple PhD students and postdocs, including notable researchers such as Ravi Garg and Marco Paladini (Sullivan Prize recipient). Her contributions to conferences include roles as Program Chair for CVPR 2016 and CVPR 2017, and she has authored influential papers on topics like Video-Popup (ECCV 2014) and Modal Space (CVPR 2017). Current projects involve advancing neural parametric models and real-time 3D reconstruction techniques. Awards include the ERC Starting Grant and recognition for her team’s work in non-rigid reconstruction. She actively mentors students and collaborates on grants, with recent openings for postdocs and PhD candidates in 3D vision and robotics.

Michael O'Boyleمشاهده پروفایل

استاد

• Heterogeneous Code discovery and Optimisation
• Neural Machine Translation and Neural Synthesis
• Deep Neural Network system stack
• +۷ مورد دیگر

Michael O'Boyle is a Professor at the University of Edinburgh's School of Informatics, where he serves as Director of the ARM Research Centre of Excellence and the EPSRC Centre for Doctoral Training in Pervasive Parallelism. Holding an EPSRC Established Career Research Fellowship, he leads pioneering work in compiler technology for heterogeneous architectures, bridging theoretical advances with practical high-performance computing applications. Professor O'Boyle's research spans multiple cutting-edge areas including heterogeneous code discovery and optimization, neural machine translation for program synthesis, deep neural network system stack optimization, software-defined hardware, and compiler/architecture co-design. His approach integrates constraint analysis, program synthesis, and machine learning to address complex challenges in high-performance computing across diverse hardware platforms. His recent publications reveal a strong trend toward integrating machine learning with traditional compiler techniques, particularly in neural program synthesis, tensor optimization, and architecture-aware compilation. This work represents a paradigm shift in compiler design, moving from rule-based systems to learning-based approaches that can automatically adapt to diverse hardware targets. IEEE/ACM CGO 2025 Distinguished Paper Award for 'Tensorize: Fast Synthesis of Tensor Programs from Legacy Code' IEEE/ACM CGO 2024 Test of Time Award ACM GPCE 2023 Best Paper Award for 'C2TACO: Lifting Tensor Code to TACOM' ACM ASPLOS 2021 Distinguished Paper Award IEEE HPCA 2021 Best Paper Award for 'Prodigy: Improving the Memory Latency of Data-Indirect Irregular Workloads' Professor O'Boyle has successfully mentored numerous PhD students who have secured prominent positions in academia (including at Cambridge, Edinburgh, Leeds, and McGill) and industry (including Meta, NVIDIA, Qualcomm, Huawei, and Microsoft). His research is supported by significant funding from EPSRC, ARM, and European projects including Bonseyes and Transmuter, demonstrating strong international recognition and industry impact. He leads the influential Compiler and Architecture Design (CArD) Group at the University of Edinburgh and is a founder of the HiPEAC Network of Excellence, which has grown into a major European initiative connecting researchers and practitioners in high-performance and embedded computing.

Sam Statonمشاهده پروفایل

استاد

• Programming Language Theory
• Probabilistic Programming
• Quantum Computing
• +۳ مورد دیگر

Sam Staton is a Professor of Computer Science at the University of Oxford and Senior Research Fellow at Jesus College. He holds a Royal Society University Research Fellowship and leads the ERC-funded BLaSt project on probabilistic programming. His research focuses on programming language theory, particularly probabilistic and quantum programming, and category theory. Staton earned his PhD from the University of Cambridge in 2007, with prior roles as a lecturer and researcher at Cambridge, Paris, and Nijmegen. Research Interests: His work explores foundational aspects of programming languages, including semantics, algebraic effects, and applications to quantum computing and statistical modeling. Recent grants include the ARIA Safeguarded AI initiative and an AFOSR award. Education: PhD in Computer Science (2007), BA from Cambridge (2002). Students & Collaborators: Supervises multiple PhD students and postdocs, including those funded through his grants. Notable advisees include Swaraj Dash (now at Heriot-Watt) and Mathieu Huot (postdoc at MIT). Awards & Grants: Royal Society Fellowship, ERC Consolidator Grant (BLaSt), EATCS Best Paper Award, and Facebook Research Award. Labs & Teams: Leads the BLaSt project and collaborates on quantum programming via algebraic effects. Engaged in editorial roles for ACM Transactions on Quantum Computing and program committees for major conferences like POPL and LICS.

• Artificial Intelligence
• Neurosymbolic AI
• Logic and AI
• +۷ مورد دیگر

Vaishak Belle is a Reader in Logic and Learning at the School of Informatics, University of Edinburgh, and serves as Director of Research and Innovation at the Bayes Centre (40% position). His academic career focuses on the critical intersection of artificial intelligence, formal logic, and machine learning systems. His research spans multiple domains within AI: Neurosymbolic AI integration approaches Logic-based machine learning frameworks Ethics, fairness, and responsibility in AI systems Generative AI and its societal impact Robotics with advanced reasoning capabilities Causal modeling and probabilistic reasoning Belle leads a research laboratory dedicated to advancing neurosymbolic AI, which combines the explainability of symbolic systems with the learning capabilities of neural networks. His recent work has explored abduction in logical frameworks, formal languages for AI safety, and the relevance of logic for general-purpose AI systems. He has made significant contributions to bridging the gap between theoretical AI foundations and practical applications. His publications demonstrate consistent focus on creating AI systems that can reason formally while learning from data, with applications ranging from robotics to ethical decision-making frameworks. His research addresses fundamental challenges in developing trustworthy, explainable AI that can operate safely in complex environments. Beyond technical research, Belle is actively engaged in AI education through executive programs on AI leadership and ethics, and has contributed to public understanding through his children's book "The girl and the robot" which explores human-robot relationships and emotional projection onto artificial agents. He maintains an active presence in the international AI research community, regularly participating in major workshops including Dagstuhl seminars on neurosymbolic AI, and collaborating with researchers across disciplines from computer science to philosophy.

• Programming Language Theory
• Semantics of Programming Languages
• Domain Theory
• +۷ مورد دیگر

Gordon Plotkin is a Professor at the School of Informatics, University of Edinburgh, where he is affiliated with the Laboratory for Foundations of Computer Science (LFCS). His research lies at the intersection of theoretical computer science and programming language semantics, with a profound influence on the formal understanding of computation. His research interests include Programming Language Theory, Semantics of Programming Languages, Domain Theory, Operational Semantics, Lambda Calculus, Type Theory, Concurrency Theory, and Algebraic Effects. His seminal work on structural operational semantics and domain theory has laid the foundation for modern semantics of programming languages. His publications span over five decades, showing a sustained and evolving research trajectory from foundational work in lambda calculus and domain theory to recent contributions in algebraic effects, probabilistic computation, and biochemical systems modeling. The articles demonstrate a consistent focus on formal methods, mathematical rigor, and the algebraic structure of computational effects. He has collaborated with leading researchers including Martín Abadi, John Power, Glynn Winskel, and John Reynolds. His work continues to influence both theoretical and practical developments in programming languages and systems. Gordon Plotkin has made foundational contributions to computer science, particularly through his development of structural operational semantics and domain-theoretic models of computation. He has advised numerous researchers and supervised many influential PhD theses, though specific student names are not listed in the provided text. His work has been supported by long-standing affiliations with the Laboratory for Foundations of Computer Science and the University of Edinburgh, and he has contributed to major collaborative projects in programming language design and verification. He is associated with several research groups and labs, most notably the Laboratory for Foundations of Computer Science (LFCS), which serves as a hub for theoretical research in programming languages, semantics, and logic at the University of Edinburgh.

• Artificial Intelligence
• Computer Security
• Multi-Agent Systems
• +۳ مورد دیگر

Dr. Chunyan Mu serves as a Senior Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen, actively contributing to both academic instruction and cutting-edge research in computing science while currently accepting new PhD students. Her research program centers on Trustworthy AI and Safe Autonomy, with specialized expertise in formal verification of responsibility, accountability, and privacy mechanisms within multi-agent systems. She investigates resilience frameworks for autonomous intelligent systems and develops advanced methodologies for information flow security analysis, bridging theoretical computer science with practical security implementations. Analysis of her publication trajectory (2014-2025) reveals consistent innovation in applying formal methods to security-critical systems. Key thematic developments include probabilistic strategy logic for observability analysis, quantitative verification of opacity properties, and game-theoretic approaches to security verification, demonstrating increasing sophistication in handling multi-agent accountability and system resilience challenges. Dr. Mu currently supervises PhD candidates and offers a fully funded doctoral position focused on formal verification of safety properties in autonomous systems, providing comprehensive financial support including tuition coverage, £20,780 annual stipend, and dedicated research funding for candidates with strong backgrounds in formal methods and artificial intelligence.

Eric Atwellمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۸ مورد دیگر

Professor Eric Atwell is a Professor of Artificial Intelligence for Language at the University of Leeds' School of Computer Science, part of the Faculty of Engineering and Physical Sciences. He holds additional roles as a LITE Fellow at the Leeds Institute for Teaching Excellence (40% part-time), Turing Fellow at the Alan Turing Institute, and member of the Leeds Institute for Data Analytics (LIDA) and Language at Leeds (LATL). His research focuses on AI applications in corpus linguistics, text analytics, and computational analysis of religious and medical texts, with a strong emphasis on Arabic and Islamic studies. He leads the AI4L research group and has supervised over 60 research students and fellows, many of whom have pursued careers in academia, tech, and AI-driven fields. Education: PhD in Corpus Linguistics and Language Learning (University of Leeds, 2008), BA (First Class) in Computing and Linguistics (Lancaster University, 1981) Grants: Includes EPSRC-funded projects like Natural Language Processing with Arabic and Islamic Studies (£337K, 2013-2015) and EDUBOTS chatbots for education (£94K, 2019-2022) Teaching: Leads modules in Data Mining, Text Analytics, and AI across multiple programs, including online Masters and PhDs. Known for innovative teaching approaches and student support, receiving positive feedback for engagement and course design. Research Interests: AI applied to corpus linguistics, Quranic text analysis, Arabic NLP, chatbots for education, and decolonizing curricula. Notable projects include Quranic semantic search tools, Hadith corpus analysis, and AI-driven fact-checking systems. Publications: Over 277 publications, with recent work focusing on Quranic QA systems, Arabic dialect identification, and generative AI applications in education. His research is widely cited, earning recognition from ResearchGate for high readership. Awards: Recognized for his most-read research items on ResearchGate (June 2021). Gallup StrengthsFinder highlights his top traits as Learner, Achiever, Ideation, Intellection, and Maximizer. Labs/Teams: Leads the AI4L research group and collaborates with international institutions including SUSTECH Sudan, King Saud University, and SWJT University (China). Active in research networks like LIDA and LATL.

Andrew Riceمشاهده پروفایل

استاد

• Programming Languages
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)

• Computational Logic
• Symbolic Machine Learning
• Neuro-symbolic AI
• +۳ مورد دیگر

Professor Alessandra Russo leads the Structured and Probabilistic Knowledge Engineering (SPIKE) research group at Imperial College London's Department of Computing. With expertise spanning computational logic, symbolic machine learning, and neuro-symbolic AI, she develops foundational AI techniques applied to security, network management, healthcare, and adaptive systems. Professor Russo holds a PhD in Computing from Imperial College London and an MSc in Computer Science from Ionian University. Her research pioneers logic-based learning systems for intelligent adaptive technologies, with projects including declarative networking for security management, privacy-preserving federated learning, and hybrid neuro-symbolic approaches for robust reasoning. Her current work focuses on developing interpretable AI systems through neuro-symbolic integration, creating frameworks that combine neural networks with symbolic reasoning for explainable decision-making. Recent publications explore rule learning from knowledge graphs, transformer-based world models, and formal methods for representation learning. Professor Russo teaches courses on Logic-Based Learning and AI Applications, and has received the Google PhD Fellowship for her research contributions. She mentors numerous PhD students in areas spanning theoretical foundations and practical applications of computational logic and machine learning.

Jeremy Gibbonsمشاهده پروفایل

استاد

• Functional Programming
• Programming Languages
• Software Engineering
• +۳ مورد دیگر

Jeremy Gibbons is a Professor of Computing at the University of Oxford, affiliated with the Department of Computer Science within the Faculty of Computer Science. He serves as Director of the Professional Programmes, overseeing part-time postgraduate degrees in Software Engineering. His roles include Chair of the Faculty of Computer Science (2012–2016), Director of the Software Engineering Programme, and Fellow of Kellogg College. Gibbons' research focuses on programming methodologies, particularly functional and object-oriented languages, with an emphasis on program calculation, design patterns, and bidirectional transformations. He leads the Algebra of Programming research group and is Editor-in-Chief of the Journal of Functional Programming and The Art, Science, and Engineering of Programming . Education includes a D.Phil. from Oxford University. His work spans formal methods, domain-specific modeling for clinical trials (e.g., CancerGrid project), and semantic frameworks for software systems. He has advised numerous students and contributed to open-access initiatives in publishing. Key collaborations include roles in ACM SIGPLAN and IFIP Working Groups 2.1 and 2.11. Research interests emphasize foundational aspects like profunctor optics, categorical programming, and algorithm design. Notable projects include datatype-generic programming and metadata-driven engineering for clinical trials. His work bridges theoretical computer science with practical applications in software architecture and system design.

• Comparative Education
• Hermeneutic Pedagogy
• Queer Theory in Education
• +۵ مورد دیگر

Karsten Kenklies is a Senior Lecturer in Education at the Strathclyde Institute of Education, University of Strathclyde. Previously, he served as Junior-Professor (Chair for Comparative Education) at Jena University, Germany from 2003-2016. His work bridges Hermeneutic Pedagogy with intercultural comparisons, focusing on European, Japanese, and American educational theories. Key interests include Bildung , queer pedagogy, and Japanese arts-based education. Education: Multiple Masters (Science, Education, Philosophy, Arts) and PhD (Jena University, Germany). Research Focus: Systematic-conceptual analysis of education across cultures. Current projects explore pre-modern Japanese educational theories, continental Bildung concepts, and Anglo-Saxon self-formation ideas. Co-founder of ExET (Experiments in Education Theory), promoting conceptual research globally. Awards: Award for Fundamental Research (2007), Research & Conference Fellowship (2018). Teaching: Leads MSc Education Studies program, teaches History of Education, Pedagogical Theory, and Contemporary Challenges. Supervises PhD projects in areas like queer education, Japanese pedagogy, and hermeneutic approaches. Key Contributions: Over 78 publications including books, articles, and reviews. Active in international networks like the Philosophy of Education Society of Great Britain. Co-investigator on projects like 'What’s the ‘use’ in Higher Education?' (2025-2026).

• Computer Graphics
• Computer Vision
• Human-Computer Interaction
• +۷ مورد دیگر

Changjian Li is an Assistant Professor in the School of Informatics at the University of Edinburgh. He leads the GraphViX Group (Graphics, Vision and X) and is a member of the Institute of Perception, Action and Behaviour (IPAB). His research spans computer graphics, computer vision, and human-computer interaction with a focus on 3D generation and analysis. Education: Bachelor's Degree from Shandong University (2014) Ph.D. from the University of Hong Kong (2019) under Prof. Wenping Wang Postdoc at University College London (UCL) with Prof. Niloy Mitra Starting Researcher position at Inria with Dr. Adrien Bousseau Research Interests: Changjian's research focuses on sketch-based 3D modeling, CAD modeling, point cloud processing, and medical imaging applications. He develops systems that bridge intuitive sketching with precise CAD workflows, enhances 3D animation pipelines, and applies neural methods to sparse medical data reconstruction. Scientific Recognition: Best Paper Honorable Mention Award (MICCAI 2021) CADTalk selected as Highlight (CVPR 2024 top 10%) ACM SIGGRAPH Asia 2018 cover image selection ACM SIGGRAPH Asia 2015 technical paper highlight CVPR 2019 poster highlighted in 'Computer Vision News' Advising & Collaborations: He mentors postdocs and PhD students including Duolikun Danier, Haocheng Yuan, Ankan Bhunia, and Lei Zhong. Former advisees include Salvatore Esposito (now at Edinburgh), Guangshun Wei (Shandong University), and Mingjun Yang (University of Melbourne). Collaborates with Oisin Mac Aodha, Hakan Bilen, and Niloy Mitra. Professional Service: Currently serves as Associate Editor for IEEE TVCG and participates in program committees for SIGGRAPH Asia, SIGGRAPH, EuroGraphics, and Geometry Design and Computing (GDC) conferences.

Timothy Harrisمشاهده پروفایل

مدرس

• Distributed Systems
• Operating Systems
• Machine Learning
• +۵ مورد دیگر

Timothy Harris is an Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology, where he jointly teaches courses on multicore semantics and programming. Currently, he works at OpenAI, focusing on performance optimization for GPU inference of large language models, including the Azure OpenAI Service. Previously, he held roles at Microsoft, AWS, Oracle Labs, and was a faculty member at the University of Cambridge (2000–2004). His research spans distributed systems, runtime systems, operating systems, and high-performance computing, with an emphasis on scalability and performance. He contributed to projects like the Xen hypervisor and the Barrelfish research OS. Key research interests include distributed training of PyTorch models in the ONNX runtime, large-scale storage performance with Amazon S3, and runtime systems for in-memory graph analytics. His work often bridges 'big data' and high-performance computing techniques. Notable contributions include the book Transactional Memory (2010) and the Barrelfish OS, alongside numerous publications in top-tier conferences like SOSP, ASPLOS, and EuroSys. He has served as PC chair for ISMM 2025, VEE 2017, and EuroSys 2015, reflecting his leadership in the systems research community. His awards include a Best Paper Award at PACT 2010. Beyond academia, Harris is an avid hiker, aiming to complete the UK coastline, and maintains a photography portfolio at tlhphotography.uk .