Find Academic Supervisors

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).

• Computer Vision
• Machine Learning
• Unsupervised Learning
• +3 more

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.

• 3D Vision
• Non-Rigid Structure from Motion (NR-SFM)
• Computer Vision
• +5 more

Dr. Dong Nguyen is an Associate Professor in Leadership Management and Change in Education at Durham University's School of Education. He serves as Director of the Master’s Programme in Educational Leadership and Change, which focuses on leadership, management, and research in education. Additionally, he is Editor-in-Chief of the International Journal of Educational Management (IJEM), a core journal in educational leadership. His career includes roles as a lecturer (Assistant Professor) and Programme Leader at the University of Glasgow, a Research Associate at Nanyang Technological University, and a teacher in Vietnam. Nguyen’s research emphasizes building leadership capacity in the Global South, comparative education studies, and innovation in education. He has led projects such as an impact evaluation of a Rwanda school leadership development program (2023–2026) funded by VVOB and a review of school leadership’s role in teacher retention funded by the Education Endowment Foundation (EEF). His work explores leadership practices across sectors like education, business, and healthcare, with a focus on equitable systems and professional learning. Nguyen has received a Discretionary Award from Durham University (2023) and has delivered numerous invited talks, including at the Chinese University of Hong Kong (2024), the Asian Conference on Business and Economic Studies (2024), and Stockholm institutions (2023). He has served as an external examiner for UK universities (e.g., Leeds, Newcastle) and evaluator for grants like UK Research & Innovation and Swiss National Science Foundation. His supervision focuses on topics like school leadership, inter-organizational collaboration, and professional development. Nguyen’s recent articles highlight leadership’s role in teacher retention, autonomy, and PLCs in the Global South. He advocates for leadership practices that foster teacher collaboration, instructional support, and participatory decision-making, particularly in contexts like Singapore and Vietnam. His funded projects and editorial roles underscore his commitment to evidence-based educational policy and practice.

• Educational Leadership
• Comparative Research
• Innovation in Education
• +3 more

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.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +6 more

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.

• Heterogeneous Code discovery and Optimisation
• Neural Machine Translation and Neural Synthesis
• Deep Neural Network system stack
• +7 more

Catherine Butler is a Senior Fellow of the Higher Education Academy and Programme Director of the DClinPsy Programme at the University of Bath's Department of Psychology. She holds external examiner roles at Cardiff University, University College London, and others. Her academic journey includes a Doctor of Clinical Psychology from the University of East London, an MBA from the Open University, and advanced training in Systemic Supervision at the University of Exeter. Her research focuses on Inclusion, Whiteness, Gender and Sexual Minorities, Intersectionality, and Systemic Therapy, with contributions to anti-racism and climate crisis studies. Key projects include exploring systemic therapy supervision, validation of therapeutic scales, and shared learning courses with clinical psychology trainees. Dr. Butler’s scholarly work addresses topics such as gender dysphoria in neurodivergent populations, relapse experiences in addiction therapy, and systemic training in clinical psychology programs. She has been recognized for her contributions through awards like the Senior Fellow of the Higher Education Academy. Her professional activities include roles on the British Psychological Society committee, conference presentations, and advisory roles in academic and clinical settings. She has supervised 18 doctoral students and led projects funded by UK charities such as the Association for Family Therapy.

• Inclusion
• Gender and Sexual Minorities
• Intersectionality
• +5 more

Bernadka Dubicka is a Clinical Professor and Honorary Clinical Chair in the Division of Neuroscience at the University of Manchester. She holds dual roles as a research lead at Pennine Care NHS Foundation Trust and as the child and adolescent mental health research lead for Health Innovation Manchester. She is the Editor-in-Chief of the Journal of Child and Adolescent Mental Health and previously served as Chair of the Child and Adolescent Faculty at the Royal College of Psychiatrists (2017–2021). Her academic qualifications include a BSc in Psychology and MBBs from University College London, followed by FRCPsych accreditation in child psychiatry. She earned a gold medal for her MD thesis on behavioral disorder implications in depression. Dubicka’s research focuses on adolescent depression, mood disorders, and brief interventions, including the IMPACT and STADIA trials evaluating psychological treatments and standardized diagnostic assessments. Her work also addresses technology’s role in youth mental health and the eco-crisis’s mental health impacts. Education: BSc Psychology, University College London MBBs, University of London MD Thesis (Gold Medal), University College London FRCPsych, Royal College of Psychiatrists Her research spans depression treatment efficacy, behavioral activation, and cross-national studies on pediatric bipolar disorder. She collaborates internationally and has led over 49 publications. Awards include a gold medal for her thesis and recognition as an RCPsych Fellow. Her work also emphasizes policy advocacy, such as addressing digital rights for children and mental health responses to climate change (e.g., COP26 contributions). She trains clinicians in brief psychosocial interventions and behavioral activation through academic programs and national courses. Grants & Funding: Co-investigator on the £1.1M HTA-funded STADIA trial evaluating remote mental health assessments. Principal investigator on the IMPACT trial, one of the largest adolescent depression trials globally. Labs/Teams: Collaborations include the University of Cambridge (IMPACT trial), University of Nottingham (STADIA), and international taskforces on treatment-resistant depression.

• Mood Disorders
• Adolescent Mental Health
• Depression Treatment
• +3 more

Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.

• Probabilistic Machine Learning
• Environmental Prediction
• Spatio-temporal Modelling
• +4 more

Dr. Simon Beaumont is an Associate Professor in the Department of Chemistry at Durham University , with additional responsibilities as Associate Dean (PGR) in the Faculty of Science. His research program integrates heterogeneous catalysis , nanomaterials , and in situ spectroscopic techniques to develop sustainable chemical processes. BA & MSci Natural Sciences, University of Cambridge (2003-2007) PhD in Heterogeneous Catalysis, University of Cambridge (2010) Postdoctoral Fellowship at UC Berkeley (2010-2012) Research foci include mechanistic studies of catalytic processes, nanoparticle synthesis , and in situ characterization via X-ray absorption (NEXAFS), DRIFTS, and Raman. His work addresses challenges in CO2 hydrogenation , biomass conversion , and environmental remediation , supported by national/EU/industrial funding. Recent publications highlight trends in selective hydrogenation (furfural), multi-functional catalysts (acid-base systems), and nanoparticle stability under reactive conditions. All studies emphasize molecular-level understanding for practical catalyst design. Scientific awards include Leverhulme Trust and Addison Wheeler fellowships. Teaching portfolio spans first-year laboratories , organic chemistry tutorials , and advanced catalysis lectures . Supervision of five research postgraduates and leadership of industry-funded projects further demonstrate his academic impact.

• Heterogeneous Catalysis
• Nanomaterials
• In Situ Spectroscopic Techniques
• +7 more

Professor John Pickett serves as Chair of the School of Chemistry at Cardiff University, where he holds the position of Professor of Biological Chemistry. His research program has established him as a leading figure in chemical ecology with significant contributions to understanding chemically mediated interactions between organisms. Professor Pickett's research focuses on chemical ecology involving chemically mediated interactions between various organisms, particularly pests attacking plants and animals. His work encompasses the chemical characterisation of pheromones and other semiochemicals across plants and animals, including insect pests and beneficial organisms. He investigates stress-related chemical signalling by plants and vertebrates (including humans and farm animals), elucidates biosynthetic pathways to pheromones and other semiochemicals, and explores how these compounds can be exploited in pest management through ecological approaches and genetic modification. Analysis of Professor Pickett's recent publications reveals a sustained and impactful research trajectory spanning chemical ecology, agricultural science, and sustainable development. His work demonstrates remarkable continuity in investigating semiochemicals and pheromones while expanding into interdisciplinary areas including AI applications for sustainable futures and the economic impacts of agricultural innovations. A consistent theme throughout his career is the practical application of chemical ecology principles to develop sustainable pest management solutions, particularly through the development and refinement of push-pull farming systems that have transformed agricultural practices in Africa.

• Chemical Ecology
• Pheromone Research
• Semiochemicals
• +6 more

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.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +8 more

Anil Madhavapeddy serves as Professor of Planetary Computing at the University of Cambridge's Department of Computer Science and Technology and directs the Cambridge Centre for Carbon Credits (4C). A Fellow of Pembroke College, he integrates systems research with environmental conservation through the Computer Laboratory's Environment and Energy Group. His career spans industry leadership (NetApp, Citrix, Intel), academic appointments (Cambridge, Imperial, UCLA), and entrepreneurial ventures (XenSource, Unikernel Systems, Docker). Madhavapeddy earned his PhD at Cambridge's Computer Laboratory in 2006. His research bridges computational systems and planetary-scale environmental challenges, with deep expertise in open-source development (OCaml, Xen, Docker, OpenBSD) and technology strategy advising for organizations including Zededa, Tezos Foundation, and Tarides. His work centers on environmental computing and climate informatics, leveraging distributed systems and functional programming to develop sensing infrastructure for conservation. Recent projects focus on carbon credit systems, AI-driven biodiversity monitoring, and sustainable computing architectures that minimize ecological footprints while maximizing analytical capability. Analysis of his 2025 publications reveals a concentrated effort on AI-integrated conservation tools, privacy-preserving carbon accounting, and energy-efficient computing. Key themes include spatial networking for ecological data, LLM-enhanced evidence retrieval in conservation science, and novel metrics for extinction risk assessment—demonstrating computational innovation applied to urgent planetary boundaries. No scientific awards were documented in the source material. Madhavapeddy advises multiple technology firms on strategic development while leading the Cambridge Centre for Carbon Credits, though specific grant funding details remain unreported. He actively contributes to the Environment and Energy Group at Cambridge's Computer Laboratory and directs the interdisciplinary Cambridge Centre for Carbon Credits (4C). His open-source leadership spans critical infrastructure projects including OCaml, Xen, and Docker, fostering collaborative development communities that underpin modern cloud and container technologies.

• Environmental Computing
• Climate Change Informatics
• Distributed Systems
• +5 more

Professor Emilio Artacho is a faculty member in the Department of Physics at the University of Cambridge, based at the Cavendish Laboratory. He transitioned from the Department of Earth Sciences in 2011, where he was granted a Professorship in 2006. His research focuses on computational simulations of non-equilibrium processes in condensed matter, particularly using first-principles molecular dynamics and density-functional theory. He co-developed the SIESTA program for linear-scaling electronic structure calculations, widely utilized in computational materials science. Artacho’s work spans far-from-equilibrium phenomena in irradiated matter, multiferroics, nanoconfined water systems, and surface chemistry. His contributions include studies of electronic stopping power in materials, 2D electron gas formation at ferroelectric interfaces, and the structural dynamics of water under confinement. His academic roles include adjunct positions at Ikerbasque (Nanogune, Spain) and visiting professorships at institutions like the University of California, Berkeley, and École Normale Supérieure de Lyon. Research interests are anchored in theoretical condensed matter physics, with applications to nanomaterials, radiation effects, and interfacial phenomena. His computational methods bridge quantum mechanics and classical dynamics, enabling insights into complex systems like proton-irradiated solar cells and confined water films.

• Condensed Matter Physics
• Computational Physics
• Molecular Dynamics
• +4 more

Shanshan Xu is a Dame Kathleen Ollerenshaw Fellow and Academic Lecturer in Catalysis at the Department of Materials, University of Manchester, since January 2025. She specializes in heterogeneous catalytic systems for sustainable chemical reactions, including hydrogen production, nitrogen fixation, and CO2 conversion, employing operando X-ray spectroscopy and DRIFTS techniques to study catalytic mechanisms. Previously, she worked on the EU-funded Laurelin project, focusing on CO2 conversion to renewable methanol using nonthermal plasma catalysis. She earned her PhD in Chemical Engineering (2021) and MSc in Materials Science and Engineering from the University of Manchester. Her research interests span catalyst design (metal oxides, porous materials like zeolites and MOFs), operando spectroscopy (XAS, XPDF, IR), and sustainable chemistry. She leads the UoMaH research group at the University of Manchester-Harwell, collaborating internationally. Xu is actively mentoring PhD students and supervising projects in catalysis, with funding opportunities through scholarships like the President’s Doctoral Scholarship and the University of Manchester-CSC joint program. Notable awards include the Dame Kathleen Ollerenshaw Fellowship (2024), Dean’s Doctoral Scholarship (2017), and First Prize in the China ShaoXing Innovation Competition (2023). Her work aligns with UN Sustainable Development Goals, contributing to clean energy and sustainable industrial processes. Xu’s lab focuses on advancing catalyst design through operando studies, with emphasis on nonthermal plasma systems. She collaborates on projects like the UoMaH initiative, exploring nanoparticle behavior and catalytic materials for industrial applications.

• Catalysis
• Nonthermal Plasma Catalysis
• Heterogeneous Catalysis
• +6 more

Silvia Santos is a Group Leader at the Francis Crick Institute, leading the Quantitative Stem Cell Biology Lab since January 2018. Her research focuses on understanding cell decision-making during transitions, specifically cell division and differentiation in early development using human embryonic stem cells. She combines experimental techniques with theoretical approaches, including advanced microscopy, genomics, and computational modeling. Education and Career: PhD in Molecular and Cell Biology from EMBL-Heidelberg (2008), followed by postdoctoral training at Stanford University (2009-2014). She held an MRC Career Development Award at Imperial College London (2014-2017) before joining the Crick. Her work emphasizes interdisciplinary methods to study cellular processes in health and disease. Research Interests: Spatial-temporal control in cell decisions, stem cell differentiation, cell cycle regulation, and modeling embryonic development. She advocates for women in science and mentorship programs for early-career researchers. Key Achievements: Recipient of Marie Curie E-Star, EMBO, and HFSP fellowships. Recognized with the BioModels’ Model of the Year 2023 for contributions to systems biology. Her lab develops models like gastruloids to study embryonic development. Grants and Mentorship: Supported by MRC and other grants. Committed to fostering excellence in training and mentorship, previously chairing mentorship initiatives at Imperial College London. Labs and Teams: Quantitative Stem Cell Biology Lab at the Crick, collaborating with interdisciplinary teams on projects involving proteomics, genomics, and high-throughput screening.

• Developmental Biology
• Cell Biology
• Cell Cycle & Chromosomes
• +3 more