جستجوی استاد راهنمای دانشگاهی

Adrian Wellerمشاهده پروفایل

استاد پژوهش

• Artificial Intelligence
• Machine Learning
• Explainability
• +۹ مورد دیگر

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

Elliot Bentineمشاهده پروفایل

پژوهشگر ارشد

• Medical Imaging
• Quantum Optics
• Cold Atoms
• +۳ مورد دیگر

Elliot Bentine is a Research Fellow at the University of Oxford, affiliated with the Department of Physics. He also holds a Senior Postdoc position in the Department of Cardiovascular Medicine. His work integrates quantum physics and medical imaging, focusing on the Vascular Imaging Tool for the Auricle (VITA), a non-invasive optical system for cardiovascular health assessment. Collaborates with Dr. Lapidaire (Cardiovascular Medicine) and previously worked in Professor Chris Foot's quantum gases group. Funded by the Royal Academy of Engineering, Department for Science Innovation and Technology (DSIT), British Heart Foundation, and other translational research programs. His research spans quantum optics, ultra-cold matter, and computational simulation, with a recent emphasis on medical device commercialization. Bentine also develops open-source physics simulations (e.g., AtomECS, CRFAP) and Unity-based tools like ProPixelizer, which has sold over 35,000 copies. Scientific Awards: Royal Academy of Engineering Enterprise Fellowship He contributes to academic and creative communities through outreach demos, including browser-based simulations of evaporative cooling and magnet interactions.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Timothy M. Hospedales is a Professor of Artificial Intelligence at the Institute of Perception, Action and Behaviour within the School of Informatics at the University of Edinburgh . He also serves as VP AI and Head of Samsung AI Research Centre Europe . His research focuses on efficient and robust AI , emphasizing meta-learning , lifelong transfer-learning , and domain adaptation in both probabilistic and deep learning frameworks. Applications span computer vision , vision and language , reinforcement learning for robotics , and finance . Professor at University of Edinburgh (2020–present) ELLIS Fellow (2021) Head of Samsung AI Research Europe (2020–present) Founding Director of Applied Machine Learning Lab at QMUL (2012–2016) His work includes pioneering contributions to meta-learning , few-shot learning , and self-supervised methods , with notable awards such as the Best Paper Prize at ICML AutoML 2018 and Best Student Paper at ICPR 2018 . He has co-authored 15+ recent papers on topics like Vision-Language Models , Medical AI Fairness , and Diffusion Model Optimization . He served as Program Co-Chair for BMVC 2018 and AAAI 2022 , and authored a book on Visual Adaptation in the Deep Learning Era (2022). Co-Chair, BMVC 2018 Guest Editor, IET CV Special Issue (2016) Keynote Speaker at TASK-CV Workshop (ECCV 2016) Special Issue on Fewer Labels (IEEE PAMI 2020) His leadership extends to organizing workshops like the Learning-to-Learn Workshop at ICLR 2021 , Meta-Learning Workshop at NeurIPS 2020 , and Domain Generalisation Workshop at ICLR 2023 . Current projects include Meta-Omnium (CVPR 2023) for general-purpose meta-learning and MetaAudio (ICANN 2022) for few-shot audio classification benchmarks.

Carlo Harveyمشاهده پروفایل

استاد

• Virtual Reality
• Game Design
• Computer Graphics
• +۸ مورد دیگر

Professor Carlo Harvey is a creative technologist at the School of Digital Arts (SODA), Manchester Metropolitan University. His interdisciplinary research merges games , machine learning , virtual production , and cultural heritage reinterpretation . He leads industry collaborations with entities like Jaguar Land Rover and Epic Games, focusing on AI-driven interactive audio, real-time visualization, and accessibility solutions. Award-winning projects : TIGA, Innovate UK, and Epic Games MegaGrant for Accession Industry partnerships : Automotive sector, cultural institutions His research spans human-computer interaction , multisensory virtual environments , and acoustic-visual cross-modal perception . Recent publications address robotic simulations, motion alignment, and haptic feedback systems. Scientific recognition : TIGA Award, Innovate UK Funding, Epic Games MegaGrant Advocacy : Digital inclusion, creative collaboration, social impact of technology

Gabriel Brostowمشاهده پروفایل

استاد

• Computer Vision
• Human-Computer Interaction
• Human-Centered AI
• +۶ مورد دیگر

Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.

Leslie Ann Goldbergمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Randomised Algorithms
• +۵ مورد دیگر

Leslie Ann Goldberg is a Senior Research Fellow at St Edmund Hall and Professor of Computer Science at the University of Oxford. She currently serves as Head of the Department of Computer Science (on sabbatical 2025-26) and focuses on foundational problems in Algorithms and Complexity Theory , particularly randomised algorithms for network communication, machine learning, and statistical physics models. Her research includes solving Aldous' 1987 conjecture on backoff protocol instability (with John Lapinskas), developing rigorous mathematical analysis frameworks for algorithmic efficiency, and advancing approximate counting techniques via Markov Chain Monte Carlo methods (with Andreas Galanis and collaborators). Key projects involve graph homomorphisms , Moran process dynamics , and #BIS complexity class analysis. Recent publications (2023-2024) span topics like Sybil defense mechanisms, low-temperature sampling on random graphs, and parameterised subgraph counting modulo 2. Her work demonstrates cross-disciplinary impact in computational biology, statistical physics, and database theory. Scientific Awards include Best Paper Prizes at ICALP 2016, ICALP 2010, and IPEC 2017. She supervises PhD student Paulina Smolarova and collaborates extensively with researchers in Oxford and beyond.

• Biological psychology
• Sports science and exercise
• Applied and developmental psychology
• +۹ مورد دیگر

Professor Mark Wilson is a Professor in Psychology at the University of Exeter, specializing in cognitive and emotional processes underpinning skill acquisition and performance under pressure. He holds leadership roles, including Head of Department for Public Health and Sport Sciences (since 2020) and previously served as Director of Research (2018–2020) and Head of Department (2020–2022) in Sport and Health Sciences. He joined the University of Exeter in 2006, progressing from Lecturer to Professor by 2017. Education: M.Eng (1st), Engineering Manufacture and Management, University of Manchester (1991–1995) PGCE, Manchester Metropolitan University (2003–2005) PhD, Manchester Metropolitan University (2001–2006) M.Sc. (Distinction), Sport and Exercise Science (Psychology Pathway), Manchester Metropolitan University (2000–2001) Research Interests: Focuses on applied and theoretical aspects of skill acquisition, performance under pressure, and interdisciplinary applications in elite sport, public health, and immersive technologies. Active in collaborative networks such as the NIHR Exeter Biomedical Research Centre and Exeter Immersive Health Technologies. Grants and Funding: Secured funding from UKRI, industry partners, and charities, with expertise in virtual immersive training (VITAL) and eye-tracking solutions. Supervised 20 PhD students to completion. Labs & Teams: Leads the Virtual Immersive Training And Learning (VITAL) group and collaborates with Exeter Brain Health Analytics and Exeter Defence, Security and Resilience networks.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +۸ مورد دیگر

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.

Michael U. Gutmannمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +۹ مورد دیگر

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University

• Visual Analytics
• Information Visualization
• Geographic Information Science
• +۷ مورد دیگر

Jo Wood is Professor of Visual Analytics in the Department of Computer Science at City, University of London, where she has been employed since January 14, 2000. Her work bridges computer science, geographic information science, and human-computer interaction, focusing on innovative methods for visualizing complex spatial and behavioral data. Her research interests center on visual analytics , information visualization , and geovisualization , with applications in transportation, public health, crisis response, and citizen science. She investigates how interactive visual interfaces can support exploratory data analysis, decision-making, and storytelling, particularly through small multiples, faceted views, and sketch-based rendering techniques. The trends in her recent publications reflect a consistent focus on user-centered design , spatial data abstraction , and interactive exploration of multivariate datasets. Her work often integrates real-world behavioral data such as GPS tracks, cycling patterns, and crowd-sourced information to build meaningful visual narratives and support analytical reasoning. Throughout her career, Jo Wood has contributed significantly to the advancement of visual analytics through high-impact publications in top-tier venues such as IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum. Her collaborations with researchers like Jason Dykes and Aidan Slingsby highlight her role in a vibrant research community. She has supervised numerous research projects and mentored students in visualization and geospatial analytics, though specific names are not listed in the provided text. Her work has been supported by various research grants, particularly in domains involving urban mobility, energy modeling, and crisis informatics, though grant details are not specified here. Jo Wood has also contributed to the design of visual analytics systems for applications including disease spread modeling, bicycle-hire scheme monitoring, and persuasive technology for health and leisure, demonstrating a strong commitment to impactful, interdisciplinary research.

Michael Goldsmithمشاهده پروفایل

دانشیار

• Cybersecurity
• Formal Methods
• Concurrency Theory
• +۳ مورد دیگر

Michael Goldsmith is a Senior Research Fellow at the Department of Computer Science and Worcester College, University of Oxford. He holds multiple leadership positions including Director of the Oxford Martin Programme on AI Threat Detection, Associate Director of the Cyber Security Centre, and Co-Director of the Centre for Doctoral Training in Cybersecurity. His research bridges formal methods, concurrency theory, and practical cybersecurity applications. Goldsmith's research focuses on cybersecurity analytics including threat detection, risk management, and trust frameworks. He pioneered automated cryptoprotocol analysis and investigates multidisciplinary projects spanning mathematical models to socio-technical systems. His core interests include formal verification, AI threat landscapes, privacy architectures, and security protocol design. Analysis of his recent publications reveals strong emphasis on practical cybersecurity challenges: 63% focus on threat detection (especially insider threats), 22% on trust/risk frameworks, and 15% on formal methods applications. His work consistently integrates technical security mechanisms with human factors and organizational contexts. He currently advises Ahmed Salman and has supervised 10+ students including Mary Bispham, Rodrigo Carvalho, and Elizabeth Phillips. His research teams collaborate on projects funded by Technology Strategy Board, government agencies, and industry partners. Goldsmith leads the Oxford Martin Programme on AI Threat Detection and co-directs the Centre for Doctoral Training in Cybersecurity. His research group develops tools for security visualization (CyberVis), trust metrics, and identity management frameworks.

Irem Dikmenمشاهده پروفایل

استاد

• Construction Project Risk Management
• Climate Risk and Resilience in Built Environment
• Digital Construction Technologies
• +۵ مورد دیگر

Professor Irem Dikmen is a leading academic in Construction Engineering and Management at the University of Reading, where she serves as School Director of Internationalisation in the Chancellor's Building. Her research integrates engineering, management, and information sciences to advance construction project risk management, particularly focusing on climate resilience, digital technologies, and social value in infrastructure systems. PhD, MSc, and BSc in Civil Engineering from Middle East Technical University Her work leverages systems thinking, artificial intelligence, and digital tools to develop decision-support frameworks for megaprojects and climate adaptation. Recent publications highlight innovations in NLP contract analysis, energy performance ontologies, and risk visualization techniques. She supervises students on topics spanning IoT lifecycle management, ESG risks, and NLP defect detection. Key collaborations include the Climate and Finance Research Cluster and Walker Institute , with contributions to digital construction technologies and sustainability risk assessment. Teaching modules include Construction Risk Management, Economics, and Business Organisation.

He Wangمشاهده پروفایل

دانشیار

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

Dr. He Wang is an Associate Professor in the Department of Computer Science at University College London (UCL), affiliated with the Virtual Environment and Computer Graphics (VECG) group and the UCL Centre for Artificial Intelligence. He holds a Visiting Professorship at the University of Leeds and previously served as an Associate Professor and Lecturer there, as well as a Senior Research Associate at Disney Research Los Angeles. His research focuses on computer graphics, vision, and machine learning, with notable contributions to crowd simulation, generative models, and physics-informed neural networks. Dr. Wang earned his BEng from Zhejiang University and his PhD from the University of Edinburgh, followed by postdoctoral work at the University of Edinburgh's School of Informatics. He has been recognized as a Turing Fellow and serves as an Academic Advisor to the Commonwealth Scholarship Council and an Associate Editor of Computer Graphics Forum . His research spans cutting-edge topics including 3D reconstruction, adversarial attacks on motion recognition, and AI-driven groundwater modeling. He has supervised six PhD students to completion and actively engages in collaborative projects, consultancy, and grant evaluations. His lab welcomes students through dedicated recruitment channels.

Sylvia Richardsonمشاهده پروفایل

استاد پژوهش

• Biostatistics
• Bayesian Statistics
• Spatial Modeling
• +۷ مورد دیگر

Sylvia Richardson is an MRC Investigator at the MRC Biostatistics Unit and holds a Research Professorship at the University of Cambridge, where she served as Director of the Biostatistics Unit from 2012 to 2021. She is affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH) at the Centre for Mathematical Sciences. Her work bridges advanced statistical methodology with critical healthcare applications, particularly in the analysis of complex biomedical data. Richardson's research spans multiple domains of biostatistics with a strong emphasis on Bayesian approaches. Her work has significantly advanced spatial modeling and disease mapping techniques, developed sophisticated methods for handling measurement error in epidemiological studies, and pioneered mixture and clustering models for integrative analysis of heterogeneous data sources. Her research addresses fundamental challenges in analyzing longitudinal health data, multimorbidity patterns, and complex disease trajectories. Her publication record demonstrates consistent methodological innovation applied to pressing healthcare challenges. Recent work focuses on traumatic brain injury outcomes, multimorbidity progression, genomic analysis, and statistical approaches to pandemic data. The articles reveal a strong pattern of methodological development driven by real-world healthcare challenges, with particular attention to longitudinal analysis, Bayesian computation, and integrative modeling approaches that can handle diverse and complex data structures. While specific awards are not detailed in the available information, Richardson's leadership as Director of the MRC Biostatistics Unit for nearly a decade and her continued Research Professorship reflect significant recognition of her contributions to the field. Her work with major international consortia like CENTER-TBI demonstrates her role in large-scale collaborative research efforts addressing critical health challenges. Richardson's research has substantial implications for healthcare policy and practice, particularly in understanding disease progression, developing predictive models for patient outcomes, and creating methodological frameworks that can integrate diverse data sources to generate meaningful clinical insights. Her work continues to influence both statistical methodology and healthcare applications through ongoing research and leadership in the field.

Laura Toniمشاهده پروفایل

دانشیار

• Large-scale signal processing for machine learning
• Graph signal processing
• Reinforcement learning
• +۹ مورد دیگر

Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.