جستجوی استاد راهنمای دانشگاهی

Heather Battey is a Professor in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. Her work bridges foundational statistical theory with practical scientific applications, focusing on parametrization effects, sparsity, and high-dimensional inference. Education PhD, University of Cambridge (2008-2011) Research Interests Battey's research examines how model structure and parametrization influence inferential procedures, particularly in high-dimensional settings. She investigates the equivalence between sparsity and reparametrization, and challenges traditional Fisherian statistical abstractions through modern practices. Her publications reveal a pattern of innovation in high-dimensional regression, covariance matrix analysis, and statistical methodology for complex data. Collaborations span disciplines including machine learning, economics, and biomedical research. Scientific Awards Fellow of the Institute of Mathematical Statistics (2023) EPSRC Early Career Research Fellowship (2020-2026) EPSRC Postdoctoral Research Fellowship (2017-2020) Advising and Grants Battey supervises PhD students Charlotte Edgar, Jakub Rybak, and Rebecca Lewis, with informal guidance to Henrique Hoeltgebaum. Over 15 pre-doctoral researchers have been mentored in topics ranging from support vector machines to spatial point processes. Current funding includes an EPSRC grant for theoretical foundations of inference with nuisance parameters and prior support for covariance matrix inference.

• Statistical Theory
• High-Dimensional Inference
• Sparsity in Statistics
• +۳ مورد دیگر

François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.

• Machine Learning
• Statistical Inference
• Computational Statistics
• +۴ 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.

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

Dr. Hong Ge serves as a Senior Research Fellow in the Department of Engineering at the University of Cambridge and holds a Fellowship at Darwin College. He maintains a dual affiliation with The Alan Turing Institute where his research centers on probabilistic programming. His work develops foundational methodologies for machine learning and intelligence with emphasis on Bayesian inference and decision-making under uncertainty. His academic background includes: MSc at the University of Edinburgh supervised by Chris Williams PhD at the University of Cambridge supervised by Zoubin Ghahramani Dr. Ge specializes in Bayesian nonparametrics, probabilistic programming, and neural networks. His research advances computational frameworks for uncertainty quantification and large-scale probabilistic modeling, with applications spanning decision theory and Gaussian processes. He bridges theoretical machine learning with practical implementations through open-source software development. He has mentored 12 researchers including current advisees Neel Alex (co-supervised with David Krueger) and Wenlin Chen (co-supervised with Miguel Hernández-Lobato and Bernhard Schölkopf), and former members such as Alexander Terenin (now at Cornell University) and Yongchao Huang (now Lecturer at Aberdeen University). His group contributed to UK government pandemic response through the Turing-RSS Health Data Lab. Dr. Ge leads the Turing.jl team developing probabilistic programming tools including AdvancedHMC.jl for Hamiltonian Monte Carlo, NestedSamplers.jl, and AbstractGPs.jl for Gaussian processes. He co-organized the CAPP Workshop on Probabilistic Programming and ACMLL Workshop on Machine Learning Languages.

• Machine Learning
• Bayesian Inference
• Probabilistic Programming
• +۳ مورد دیگر

Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.

• Nuclear Magnetic Resonance (NMR)
• In vivo imaging
• MRI pulse sequence development
• +۷ مورد دیگر

Professor Paul Fearnhead is a leading academic in Statistics at Lancaster University 's School of Mathematical Sciences . His research focuses on Bayesian and Computational Statistics , with applications in Anomaly Detection , Continuous-time Markov Processes , and Changepoint Analysis . Department: Mathematics and Statistics Academic Rank: Professor Email: p.fearnhead@lancaster.ac.uk His work bridges theoretical statistics and computational efficiency, notably through pruning techniques for change detection and novel Monte Carlo methods. Current projects include AI Hub initiatives, probabilistic AI foundations, and real-time anomaly detection in streaming data. Research outputs span Bayesian Analysis , Time Series Modeling , and Scalable Statistical Algorithms , with applications in fields like astronomy and epidemiology. Recent publications emphasize simulation-based composite likelihoods and efficient distributed changepoint detection. Scientific contributions include leadership roles in the STOR-i Centre for Doctoral Training and Data Science Institute (DSI) projects such as CoSInES and Statscale. He supervises PhD students including Dylan Bahia, Yuntang Fan, and Ziyang Yang.

• Bayesian Statistics
• Computational Statistics
• Change Point Detection
• +۵ 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

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

Professor Carl Edward Rasmussen is affiliated with the University of Cambridge , where he focuses on Machine Learning , Probabilistic Inference , Decision Making , and Reasoning Under Uncertainty . His work bridges theoretical advancements with practical applications in robotics, control systems, and computational biology. Academic Affiliation: University of Cambridge Academic Role: Professor His research emphasizes scalable Gaussian process methods, Bayesian system identification, and reinforcement learning. Recent projects include transfer learning for antibacterial discovery , graph neural processes for molecular functions , and efficient variational inference techniques . Key themes in his publications highlight uncertainty quantification , model generalization , and nonparametric approaches . Notable Scientific Contributions Advancements in sparse Gaussian process hyperparameter estimation Framework for Bayesian system identification in dynamic systems Hybrid models combining transformers and Gaussian processes

• Machine Learning
• Probabilistic Inference
• Decision Making
• +۱ مورد دیگر

Colin J Akerman is Professor of Neuroscience and Group Leader in the Department of Pharmacology at the University of Oxford, concurrently serving as Corange Fellow and Medical Tutor at Corpus Christi College. His research investigates fundamental mechanisms of synaptic circuit formation and plasticity, with direct implications for epilepsy, dementia, and schizophrenia through multidisciplinary approaches integrating electrophysiology, optical imaging, and computational modeling. His primary research interests encompass Synaptic Plasticity, Neural Circuit Formation, and Excitatory-Inhibitory Balance, with specific focus on neuronal progenitor influences on connectivity, chloride dynamics in inhibitory transmission, and learning mechanisms in disease contexts. The lab employs custom-built equipment and molecular tools to probe synaptic function across in vivo , in vitro , and in silico platforms, emphasizing how activity-dependent processes shape neural networks during development and disease. Recent publications (2023-2025) reveal strong thematic convergence on intracellular chloride regulation in sleep-wake cycles, cortical circuit assembly from embryonic progenitors, and innovative optical tools for neural monitoring. This work bridges molecular neuroscience with systems-level understanding of synaptic plasticity, particularly regarding ionic mechanisms in epilepsy and sleep homeostasis. No scientific awards or fellowships are explicitly documented in the source materials. Professor Akerman currently mentors four PhD students (Vourvoukelis, Selfe, Wang, Gemayel) and multiple postdoctoral researchers, having previously trained scientists now leading independent groups in Toronto, Edinburgh, Cape Town, Oxford, and London. His research is funded by the European Research Council, Innovative Medicines Initiative, and Wellcome Trust, supporting investigations into synaptic mechanisms underlying neurological disorders. The Akerman Group, established in 2008, operates as an integrative neuroscience hub within Oxford's Pharmacology Department. The 10-member team combines expertise in patch-clamp electrophysiology, optogenetics, multiphoton imaging, and computational modeling, with current projects spanning neuronal progenitor biology, inhibitory synaptic plasticity, and learning rule implementation in neural networks. The lab emphasizes technical innovation, regularly developing custom instrumentation and molecular tools for neural observation and manipulation.

• Neuroscience
• Synaptic Plasticity
• Neural Circuit Formation
• +۴ مورد دیگر

Steve Hailes is a Professor of Wireless Systems at the Department of Computer Science, University College London. He has served as Head of Department since 2019 and Deputy Head from 2005. His research spans wireless networks, computational trust, AI, and sensor systems for health, ecological, and environmental applications. Education: PhD and undergraduate degree from Cambridge University Appointment: Joined UCL in 1991 (postdoc), Lecturer in 1992 Research interests include: Trust and security in networked systems (co-founder of computational trust) Security of industrial control systems AI/ML applications in security and causal discovery Multi-agent reinforcement learning and moral behavior modeling Gas sensor fabrication and deployment for diverse applications Recent publications focus on feature selection for cybersecurity , moral alignment in LLM agents , trust-based consensus algorithms , and causal discovery using reinforcement learning . Collaborations include co-authors like Westphal, Musolesi, and Tennant. Applications span healthcare (dementia, JIA), ecology (endangered species in Botswana), and environmental monitoring (CO distribution, meth lab detection).

• Wireless Systems
• Network Security
• Computational Trust
• +۸ 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.

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

Axel Gandy is a Professor of Statistics at the Department of Mathematics, Imperial College London. He serves as Director of the EPSRC CDT in Modern Statistics and Statistical Machine Learning , overseeing PhD supervision and advanced statistical training.

• Computational Statistics
• Statistics in Finance
• Survival Analysis
• +۳ مورد دیگر

Reiko Heckel is a Professor of Software Engineering at the University of Leicester, serving as Director of Postgraduate Teaching for Computing degrees and Data Analytics Lead at the Leicester Innovation Hub. She previously held academic roles at the Technical Universities of Dresden and Berlin before joining Leicester in 2004. Her research focuses on graph transformation systems, model-based development, stochastic modeling, and formal methods in software engineering. She earned her PhD (Dr.-Ing.) in Computer Science from TU Berlin in 1998. Her research interests span software engineering pedagogy, formal specification techniques, and applications of graph grammars in system modeling. Recent work explores stochastic graph transformations for social networks, transparency engineering in AI systems, and blockchain-based smart contract frameworks. Her contributions bridge theoretical foundations with practical applications in cybersecurity, data integration, and human-centric systems design. Key contributions include advancements in automated test case generation via graph transformations, visual contracts for software reverse engineering, and formal methods for complex system analysis. Her work frequently intersects with industry through collaborations via the Leicester Innovation Hub, emphasizing data analytics and technology transfer. Education: MSc Computer Science, Technical University of Dresden PhD (Dr.-Ing.), Computer Science, TU Berlin (1998) Leadership Roles: Head of Department (2014-2018) Director of Postgraduate Teaching (Ongoing) Research Themes: Model-Based Development Stochastic Systems Analysis Graph Neural Networks Trustworthy AI Her publications reflect a focus on formal methods, with recent trends in applying graph transformation techniques to social network modeling, blockchain smart contracts, and educational pedagogy.

• Software Engineering
• Graph Transformation Systems
• Model-Based Development
• +۵ 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.

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

Dr. George Cantwell is an Assistant Professor in the Department of Engineering at the University of Cambridge, affiliated with Cambridge Infectious Diseases. He specializes in computational methods for inference problems, particularly in disease spreading across networks. Education: PhD in Physics from the University of Michigan; postdoctoral fellowship at the Santa Fe Institute His research focuses on network science , complex systems , and statistical inference , with an emphasis on computational approaches. His work spans theoretical and applied domains, including: Message passing algorithms for heterogeneous networks Bias correction in social network analysis (friendship paradox) Statistical inference of network structure from noisy data Modeling judicial voting behavior through network interactions Computational cognitive neuroscience of category learning Recent publications highlight interdisciplinary applications in epidemiology, physics, and cognitive science. He actively mentors students in networks, complex systems, and statistical inference.

• Network Science
• Complex Systems
• Statistical Inference
• +۴ مورد دیگر