جستجوی استاد راهنمای دانشگاهی

Keir K. Rogers is an STFC Ernest Rutherford Fellow at Imperial College London (starting 2025). Previously, he held the Dunlap Fellowship at the University of Toronto and a postdoctoral position at the Oskar Klein Centre for Cosmoparticle Physics, Stockholm University. He earned his PhD from University College London and a Master’s from the University of Oxford. His research focuses on cosmological model physics, leveraging statistical and machine learning techniques to analyze cosmological data. Key areas include testing ultra-light axion models of dark matter, analyzing cosmic microwave background (CMB) and large-scale structure data, and developing computational frameworks like cosmological emulators and Bayesian optimization. He contributed to bounds on dark matter models using Lyman-alpha forest data and pioneered methods for CMB component separation using wavelets. Scientific awards include the STFC Ernest Rutherford Fellowship and Dunlap Fellowship. His work involves collaborations on next-generation surveys like the Dark Energy Spectroscopic Instrument (DESI) and CMB-S4. He leads efforts to improve parameter inference through simulation-based methods and has developed tools for mitigating high-density absorber contamination in Lyman-alpha forest analyses.

• Cosmology
• Dark Matter
• Statistical Methods
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Professor David Bacon is a leading academic in cosmology and astrophysics at the University of Portsmouth's Faculty of Technology, serving as Director of the Institute of Cosmology & Gravitation (ICG). His research focuses on cosmology at radio wavelengths, gravitational lensing, and theories of gravity, with significant contributions to dark matter and dark energy studies. Education : PhD in Natural Sciences from the University of Cambridge (2002) Appointments : PPARC/STFC Fellow (2002–2004), University of Edinburgh; Senior Research Fellow/RCUK Academic Fellow (2007), University of Portsmouth; Professor (2018–present) His work leverages data from major surveys like the Dark Energy Survey (DES), Atacama Cosmology Telescope (ACT), and Euclid mission, employing techniques such as weak lensing , galaxy clustering , and machine learning . Recent publications analyze dark energy constraints , galaxy cluster profiles , and cosmological parameter estimation using FRBs and supernovae. Scientific Awards : PPARC Research Fellowship (2002) STFC Advanced Fellowship (2004)

• Cosmology at Radio Wavelengths
• Gravitational Lensing
• Theories of Gravity
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Michael Bronstein is the DeepMind Professor of Artificial Intelligence at the University of Oxford and Founding Scientific Director at the Aithyra Institute. He holds affiliations with Imperial College London (previous) and institutions like Stanford, MIT, and Harvard. His research focuses on geometric deep learning, graph neural networks, protein design, and non-human species communication. Bronstein received his PhD from the Technion in 2007 and has been awarded multiple fellowships and grants, including ERC, Google, and Amazon awards. Education: PhD in Computer Science, Technion, 2007 Research Interests: His work spans geometric deep learning, graph neural networks, 3D shape analysis, and applications in protein design. Notable projects include protein interaction design using surface fingerprints and advancing graph neural network architectures. He also explores AI in non-human communication, combining machine learning with biological systems. Publications: Recent work emphasizes knowledge graph foundation models, graph homomorphism analysis, and generative models for discrete data. His research bridges theoretical foundations (e.g., graph expressivity) with practical applications in biomedicine and AI. Awards: EPSRC Turing AI Fellowship Royal Society Wolfson Research Merit Award Academia Europaea Membership IEEE/IAPR/ELLIS Fellowships Advising & Grants: Supervises students in AI and graph learning. Active in securing ERC, Google, and industry grants. His entrepreneurial ventures include founding companies like Fabula AI (acquired by Twitter). Labs/Teams: Leads Graph Learning Research at DeepMind and collaborates with interdisciplinary teams in AI, biology, and quantum systems.

• Geometric Deep Learning
• Graph Neural Networks
• 3D Shape Analysis
• +۴ مورد دیگر

Euan McGonigle is a Lecturer in Statistics at the University of Southampton, affiliated with the Department of Mathematical Sciences and the Statistical Sciences Research Institute (S3RI). He holds a PhD in Statistics and Operational Research from Lancaster University (2020), an MRes in the same field (2017), and an MSci in Mathematics from the University of Glasgow (2016). His research focuses on nonstationary time series analysis, change point detection, and multiscale statistical methods, particularly involving wavelet-based techniques for analyzing complex temporal data. His work emphasizes developing robust methodologies for segmenting time series data, analyzing locally stationary processes, and addressing nonstationarity in statistical models. He has contributed to software packages like TrendLSW and CptNonPar, which implement his research into practical tools for data analysis. McGonigle currently supervises PhD students in Mathematical Sciences and is accepting new PhD applications. Key areas of application include signal processing, financial time series, and environmental data analysis. His recent publications highlight advancements in wavelet-based trend estimation, multiscale variance analysis, and nonparametric change point detection techniques for multivariate settings.

• Nonstationary time series analysis
• Change point detection
• Multiscale methods in statistics
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Dr Stuart Barber is a Senior Lecturer in Statistics at the School of Mathematics, University of Leeds, affiliated with the Faculty of Engineering and Physical Sciences. His expertise spans wavelet methods, statistical bioinformatics, and applied statistics, with interdisciplinary applications in fields such as industrial tomography, spatial data analysis, and genomics. His research focuses on wavelet-based statistical techniques, Bayesian inference, and computational methods. Recent applications include clustering, phylogenetics, anaesthesiology, and copy-number genomic data segmentation. Research trends from his publications highlight advanced methodologies like Gaussian process modeling, Voronoi tessellations, and Approximate Bayesian Computation (ABC), alongside bioinformatics-driven studies on protein alignment and structure analysis. Responsibilities include serving as Head of the Department of Statistics. He actively supervises postgraduate researchers, including Fatih Gezer, and acknowledges support from the Simons Foundation. His work is associated with the Statistics research group, fostering collaborations and PhD opportunities in diverse statistical domains.

• Wavelet Methods in Statistics
• Statistical Bioinformatics
• Applied Statistics
• +۵ مورد دیگر

Dr Alex Gibberd is a Senior Lecturer (equivalent to Associate Professor) in Statistics within the School of Mathematical Sciences at Lancaster University. He has been a faculty member since 2018, following postdoctoral research at Imperial College London and a PhD in Statistics from University College London (UCL) in 2017. He also holds an MPhys in Astrophysics from the University of St Andrews (2012). Education: PhD in Statistics, University College London (2017) MPhys in Astrophysics, University of St Andrews (2012) Research Interests: Dr Gibberd's research focuses on high-dimensional time-series analysis, with methodological contributions in statistical modeling under non-stationarity and high-dimensionality. His work spans both theoretical developments and practical applications, particularly in neuroscience and finance. Key areas include: Sparse dynamic factor models and regularized estimation techniques Spectral analysis and locally-stationary wavelet models Optimization algorithms for model selection in high-dimensional settings Applications in brain connectivity analysis and economic forecasting Research Themes: His recent publications demonstrate a strong focus on developing interpretable statistical methods for complex systems. These include advances in principal component analysis with joint rank and covariance estimation, sparse dynamic factor models, and regularized spectral estimation for high-dimensional point processes. The applications range from neural connectivity modeling to energy efficiency policy analysis. Grants & Funding: Research in Paris Grant (2024) Support of Collaborative Research with Dr S. Roy at University of Bath (2021) Model Selection for High-Dimensional Temporal Disaggregation in Official Statistics (2021-2023) Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation (2021-2025) Wavelet Methods for Dependency Analysis in Multivariate Time Series (2020) STOR-i: Information Fusion for Non-homogeneous Panel and Time-series Data (2019-2025) PhD Supervision & Students: Dr Gibberd supervises PhD students at the intersection of high-dimensional statistics and time-series analysis. Current students include Carla Pinkney (STOR-i CDT), Ziyan Zhao, and Kai Zheng (Centre for Marketing Analytics & Forecasting). Research Affiliations: STOR-i Centre for Doctoral Training Centre for Marketing Analytics & Forecasting Changepoints and Time Series Research Group Data Science Institute - Foundations Social and Economic Statistics Group

• High-dimensional time-series analysis
• Statistical modeling under non-stationarity
• Sparse dynamic factor models
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Haotian Xu is a Harrison Early Career Assistant Professor in the Department of Statistics at the University of Warwick. His research focuses on nonparametric estimation in high-dimensional settings, change point detection, and inference under temporal dependence and contamination. He holds a Ph.D. from the University of Geneva (2021) and an MSc from the University of Illinois at Urbana-Champaign (2015). His postdoctoral work involved collaborations at institutions like Pennsylvania State University and Université Catholique de Louvain. Key research areas include robust statistical methods, wavelet-based inference, and applications in sensor calibration and functional data analysis. He has developed R packages such as 'changepoints' and 'FragmentCP' for change point detection. Teaching responsibilities include Bayesian Forecasting and Intervention at Warwick and courses in statistical modeling at Penn State and the University of Geneva. His publications span high-impact journals like the Annals of Statistics and IEEE Transactions, addressing challenges in time series analysis, multivariate modeling, and computational statistics. Current projects emphasize scalable inference for large datasets and fragmented functional data analysis.

• Nonparametric Estimation
• High-dimensional Inference
• Change Point Detection
• +۵ مورد دیگر

Barbara Bravi is an Assistant Professor in Biomathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. She holds affiliations with multiple research groups including the Biomathematics Group, Applied Mathematics and Mathematical Physics, and the Convergence Science Center. Her research focuses on statistical inference methods for biological systems, particularly modeling stochastic biochemical reactions, protein function, and immunological processes. She has expertise in machine learning applications for biomedical problems and protein sequence-function mapping. Barbara completed her PhD in Applied Mathematics at King’s College London (supervised by Peter Sollich), followed by postdoctoral research at École Polytechnique Fédérale de Lausanne (Matthieu Wyart) and École Normale Supérieure de Paris (Simona Cocco, Rémi Monasson, Thierry Mora, Aleksandra Walczak). Her work investigates protein allostery and immune response mechanisms at molecular levels. She joined Imperial College London in 2021 as a Lecturer in Biomathematics. Her research directions include mathematical modeling of biochemical networks, machine learning for biomedical data, and stochastic analysis of biological systems. Current interests emphasize immunology, protein dynamics, and integration of structural biology with artificial intelligence. She actively supervises students in the Chemical Biology CDT program and collaborates across disciplines at Imperial’s Institute of Infection and Institute of Chemical Biology.

• Statistical Inference in Biology
• Mathematical Modeling of Biochemical Systems
• Machine Learning in Biomedicine
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Dr. Anthea Monod is an Associate Professor in Mathematics and Machine Learning at the Department of Mathematics, Imperial College London, within the Faculty of Natural Sciences. She is Co-Director of the £10M EPSRC-funded Mathematical and Computational Foundations of Artificial Intelligence Hub (Erlangen AI Hub) and leads a research group of over 20 members including postdocs, PhD students, and undergraduates. Her research integrates algebraic topology, algebraic geometry, and computational methods to address challenges in machine learning, mathematical biology, and data analysis. Dr. Monod earned her PhD from the Swiss Federal Institute of Technology in Lausanne (EPFL). She previously held postdoctoral and visiting roles at institutions including the Technion, Duke University, Columbia University, and Tel Aviv University. Her work spans applied topology (e.g., topological data analysis), algebraic statistics, geometric machine learning, and computational biology. Her current research priorities include developing topological tools for analyzing neural network activations, advancing geometric approaches to AI, and applying algebraic methods to biological data. Key projects include cycle matching for high-dimensional neural activation patterns and tropical geometry-based graph kernels. Grants: £10M EPSRC Hub for AI Foundations (EP/Y028872/1) Awards: Turing Global Fellows Fund (2024) for student Yueqi Cao Labs/Teams: Co-Director of Erlangen AI Hub, member of Imperial's Biomathematics Group and AI Network

• Applied and Computational Topology
• Algebraic Geometry
• Geometric Machine Learning
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Dr. Wenxing Guo is a Lecturer in the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex. Their research focuses on Statistical Machine Learning, including Online Learning and Federated Learning, High-dimensional Inference, and Multivariate Regression and Classification models. Dr. Guo's work emphasizes innovative approaches to data analysis and statistical modeling, with recent contributions in federated learning frameworks and methodologies for handling high-dimensional datasets. Their publications span topics from wavelet-based Bayesian methods to tensor response regression, reflecting a strong foundation in both theoretical and applied statistics. They are actively open to supervising doctoral students in areas such as statistical inference and model development. Contact information includes wg22745@essex.ac.uk and their office at 3A.528 on the Colchester Campus.

• Statistical Machine Learning
• High-dimensional inference
• Multivariate Regression
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Dimitrios Adamos is an Honorary Senior Research Fellow in the Department of Computing at Imperial College London, Faculty of Engineering. He specializes in brain wave decoding for virtual environments and clinical rehabilitation, with a focus on translating neurotechnology into practical applications through AI-driven solutions. His academic work bridges theoretical advancements and real-world applications in brain-computer interfaces (BCI) and healthcare technologies. Dr. Adamos holds a Ph.D. in Neuroinformatics and co-founded Cogitat, an Imperial spinout company where he serves as CTO. At Cogitat, he develops ML/AI technologies for BCI applications, securing patents and pre-seed funding. His research emphasizes ethical challenges in GenAI-powered BCIs and causal perspectives in brainwave modeling. Key achievements include leading a team to first place in the 2021 NeurIPS EEG Transfer Learning Competition and partnering with NHS for neurotechnology in stroke rehabilitation. His research interests span biomedical engineering, artificial intelligence, cognitive sciences, and clinical applications of EEG signal processing. Dr. Adamos collaborates with institutions like the Science Museum London and media outlets such as BBC News and The New Statesman, highlighting his work on mind-controlled VR for healthcare and BCI ethics. His publications focus on EEG decoding, graph theory in brain signal analysis, and causal inference in neurotechnology.

• Brain-Computer Interfaces (BCI)
• Neurotechnology
• Artificial Intelligence
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Georgios Exarchakis is a Lecturer at the University of Bath, specializing in Machine Learning, Theoretical Neuroscience, and Data Science. His research emphasizes transparent and interpretable modeling, with applications in Neuromorphic Engineering, Health Science, and Quantum Chemistry. He has held research positions at IHU Strasbourg, Institut de la Vision, and École Normale Supérieure, and earned his PhD from the University of Oldenburg. Education: Dr. rer. nat. in Machine Learning, 2016, Carl von Ossietzky University of Oldenburg M.Sc. in Computational Science, 2012, Goethe University Frankfurt Diploma in Mathematics, 2008, Aristotle University of Thessaloniki His research interests span Interpretable Machine Learning, Invariant Representations, Sparse Coding, Wavelet Scattering, Probabilistic Models, and Deep Learning . He investigates how models can extract meaningful, stable features from complex data, drawing inspiration from biological systems and theoretical neuroscience. His work often bridges theory and application, particularly in quantum chemistry and neurotechnology. His recent publications highlight a strong focus on efficient and interpretable models , including wavelet scattering for molecular property prediction, discrete sparse coding, and clustering algorithms for event-based vision. The articles show a consistent theme of developing mathematically grounded, invariant, and scalable methods for data analysis. He is the developer or a key contributor to open-source libraries such as Kymatio (Scattering Transforms in Python) and ProSper (Probabilistic Sparse Coding), which facilitate the use of advanced signal processing and learning techniques in the research community. Georgios has taught Machine Learning courses at the University of Strasbourg, École Polytechnique, and Oldenburg. He has collaborated with leading researchers like Stéphane Mallat and Jörg Lücke. His work is published in top-tier venues including CVPR, NIPS, JMLR, and JCP.

• Machine Learning
• Theoretical Neuroscience
• Data Science
• +۱۶ مورد دیگر

Dr. Cristinel Mares is a Reader and Director of Teaching and Learning at Brunel University London, affiliated with the Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences . He has maintained a consistent research trajectory in structural dynamics, vibration analysis, and damage detection methodologies since the late 1990s. Role: Reader (equivalent to Professor rank), Director of Teaching and Learning Contact: cristinel.mares@brunel.ac.uk | Howell Building, Room 111 Research Interests span structural health monitoring (SHM) through acoustic emission and guided wave propagation , wavelet-based finite element analysis for dynamic systems, squeeze-film levitation mechanisms, and Bayesian estimation for crack monitoring. His work bridges theoretical modeling with industrial applications in aerospace, wind energy, and marine engineering. Scientific Contributions include over 104 publications in journals like Mechanical Systems and Signal Processing , Advances in Mechanical Engineering , and Sensors . Recent articles focus on passive compliance design for robotics , Poisson contraction in levitation systems , and fatigue crack diagnostics using high-frequency parametric analysis . Collaborative efforts include co-authoring with T-H Gan, M. Wang, and T. Stolarski. Lab & Team Involvement includes membership in Brunel's structural dynamics research group, working on multi-scale finite element models , acoustic emission monitoring , and vibration-based damage detection . His team applies these techniques to complex systems ranging from helicopter stub wings to fast-moving train beam-soil interactions .

• Structural Dynamics
• Vibration Analysis
• Damage Detection
• +۵ مورد دیگر

Haslifah Hasim is an Associate Professor at the School of Mathematical and Computer Sciences, Heriot-Watt University. Her research focuses on financial risk management, Islamic finance integration into quantitative models, and Bayesian econometric methodologies. She has contributed to the development of asset pricing frameworks compliant with Shariah principles, as well as advanced risk assessment techniques for banking and commodity markets. Her work combines theoretical econometrics with practical applications in global trade dynamics and financial stability analysis. Research interests include Value at Risk (VaR) modeling, copula-based volatility analysis, and the application of Markov switching models to study economic contagion effects. She has explored the interplay between oil prices, precious metals, and financial stress indicators, with recent studies examining the impact of the COVID-19 pandemic on oil markets. Her publications analyze portfolio risk in Malaysia's foreign exchange markets, Oman's trade potential with Indian Ocean Rim countries, and the efficacy of multivariate GARCH models in asset allocation. Methodologically, she employs Bayesian estimation, extreme value theory, and network analysis to uncover dependencies in financial systems.

• Financial Risk Management
• Islamic Finance
• Bayesian Econometrics
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Marina Iuliana Knight is a Professor of Statistics in the Department of Mathematics at the University of York. She holds a BSc in Mathematics from Bucharest, an MSc in Stochastic Processes and Theoretical Statistics from Bucharest, and a PhD in Statistics from the University of Bristol. Her research focuses on multiscale methods for nonstationary time series, with applications in energy, biology, and neuroscience. She has led projects such as the EPSRC-funded NeST programme grant and collaborates widely across disciplines, including with Prof Seth Davis (Biology), Dr Elena Geangu (Psychology), and Prof Guy Nason (Imperial College London). Current roles include Head of the Statistics Section and membership in the University Impact Leads for Science Departments group. Her work involves developing wavelet-based techniques for complex network data, with R packages like 'GNAR' and 'lpacf' available on CRAN. She has supervised numerous PhD students, including Maddie Shelley (current), Dingjia Cao (2024), and Jonathan Embleton (2021). Her research has addressed topics such as circadian rhythms, EEG/LFP data analysis, and wearable sensor integration for infant studies. She actively participates in international conferences and has authored 33+ peer-reviewed publications. Key achievements include advancing methods for locally stationary time series forecasting and network time series modelling. Her work bridges statistical theory with practical applications in medicine and environmental science, reflecting a commitment to interdisciplinary impact.

• Wavelet Multiscale Methods
• Nonstationary Time Series Analysis
• Long Memory Processes
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