جستجوی استاد راهنمای دانشگاهی

Dr. David Garcia Cava is a Senior Lecturer in Mechanical Engineering at the University of Edinburgh's School of Engineering. His research specializes in structural dynamics, engineering vibrations, and structural health monitoring (SHM) systems. He leads research developing vibration-based monitoring techniques for engineering structures including wind turbines, aerospace components, and composite materials. His work focuses on: Developing adaptive methodologies for structural health monitoring Damage detection under environmental variability Experimental characterization of dynamic responses Machine learning applications in structural diagnostics Recent publications demonstrate advancements in mitigating environmental variability in SHM, fatigue testing of renewable energy structures, physics-informed machine learning methods, and vibration analysis of composite materials. His research has applications across wind energy, tidal power, aerospace, and mechanical systems. Dr. Garcia Cava teaches courses in Structural Mechanics and Dynamics, Advanced Dynamics, and Research Methods. He is actively recruiting PhD students for projects on structural health monitoring and vibration analysis.

• Structural Dynamics
• Engineering Vibrations
• Structural Health Monitoring
• +۲ مورد دیگر

Dr. Carl Whitfield is a Dame Kathleen Ollerenshaw Fellow in Applied Mathematics at the University of Manchester's Department of Mathematics. He specializes in computational modeling of lung structure and function, particularly focusing on cystic fibrosis. His research integrates multi-scale modeling of respiratory biomechanics and fluid dynamics with patient-specific simulations to improve disease detection and treatment optimization. Whitfield holds a PhD in Modelling Spontaneous Motion and Deformation of Active Droplets from the University of Sheffield (2015) and a Master of Physics from the same institution (2012). Current Role: Dame Kathleen Ollerenshaw Fellow, Applied Mathematics Research Themes: Respiratory Physiology, Biomechanics, Fluid Mechanics, Computational Modelling Collaborations: Prof. Oliver Jensen (Manchester), Prof. Jim Wild (Sheffield), Prof. Ian Hall (Epidemiology) His research interests span computational modeling, biological fluid mechanics, and inverse problems, with applications to respiratory medicine and soft matter physics. Whitfield has contributed to pandemic response through modeling asymptomatic testing and workplace transmission dynamics during the SARS-CoV-2 pandemic. He leads the Mathematical Epidemiology Group and contributes to multi-modal studies on pregnancy and stillbirth prevention. Whitfield's recent work emphasizes the non-local effects of airway constrictions on inhaled particle deposition and the role of surfactant in viscoplastic fluid instabilities. His publications explore both fundamental fluid dynamics and translational medicine, reflecting his interdisciplinary approach to solving complex biomedical problems. Key Achievements: Developed patient-specific lung models for CF drug delivery optimization Funding: Active roles in projects including Digital Futures and Christabel Pankhurst Institute initiatives

• Computational Modelling
• Respiratory Physiology
• Biomechanics
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Claire Heaney is an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow affiliated with both Imperial-X and the Department of Earth Science & Engineering within the Faculty of Engineering at Imperial College London . Her research focuses on applying advanced machine learning techniques to complex scientific problems in environmental and computational physics. Research Interests: Reduced-Order Modelling Machine Learning for Scientific Applications Urban and Environmental Flows Scientific Contributions: Claire's work demonstrates expertise in: Neural network-based solutions for partial differential equations Hybrid physics-ML models for environmental systems Uncertainty quantification frameworks Mesh adaptation techniques for improved computational efficiency Her research spans multiple disciplines including applied mathematics, computational fluid dynamics, and nuclear engineering. Scientific Awards: Co-author of one of Wiley's top cited articles (Jan 2021 - Dec 2022) Recipient of the prestigious Schmidt AI in Science Postdoctoral Fellowship Collaborative Research: Claire works at the intersection of earth science and engineering, collaborating across disciplines to develop AI solutions for environmental challenges. Her current research investigates the use of green infrastructure to mitigate air pollution in urban environments.

• Reduced-Order Modelling
• Machine Learning for Scientific Applications
• Urban and Environmental Flows
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Dr John Williamson is a Lecturer in Music at the University of Glasgow's School of Culture & Creative Arts. His work bridges music technology, human-computer interaction (HCI), and Bayesian methodologies. He focuses on designing interactive systems for virtual environments, cybersecurity, and rehabilitative gaming. His research includes tactile feedback systems, thermal attack analysis on keyboards, and probabilistic models for user comprehension. Williamson has contributed to projects like ThermoSecure and has explored cultural heritage mapping through studies of Glasgow's Sauchiehall Street. He holds an ORCID identifier (0000-0002-1010-6998) and is based at Room 402, 14 University Gardens. Research interests span HCI innovation, Bayesian approaches to design, and the integration of music technology into interactive platforms. Notable trends in his publications include advancing secure human-computer interfaces, optimizing user interaction through probabilistic models, and applying computational methods to cultural studies. His work often emphasizes practical applications, such as improving accessibility in VR and enhancing security in everyday devices. Williamson has secured grants for projects like the MoreGrasp Initiative (2017-2021), focusing on neuroprosthetics for spinal injury patients. He collaborates on interdisciplinary teams involving computer science, engineering, and arts disciplines. His teaching and supervision activities are integral to the School's creative arts and technology programs, though specific student names are not listed here. Current research directions include expanding Bayesian methods for interactive systems and exploring the intersection of music with immersive technologies.

• Human-Computer Interaction
• Bayesian Methods
• Virtual Reality
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Marcelo A. Pereyra is a Professor at the School of Mathematical & Computer Sciences, specifically within the Department of Actuarial Mathematics & Statistics at Heriot-Watt University. His research focuses on advanced computational methods including Bayesian inference, optimization algorithms, and their applications in imaging and inverse problems. He has maintained consistent research output from 2010 to 2025, with notable contributions in machine learning, signal processing, and statistical methodologies. His work aligns with UN Sustainable Development Goals, particularly in advancing innovation and infrastructure through mathematical and computational advancements. Collaborations span global institutions, addressing challenges in computational imaging, data analysis, and engineering systems. Key methodologies include diffusion models, Markov chain Monte Carlo methods, and proximal algorithms. Pereyra’s publications emphasize practical applications of theoretical frameworks, such as defect detection in subsea pipelines and photon-starved image restoration. His research bridges statistical theory with computational efficiency, often focusing on Bayesian approaches for complex problem-solving. Grants and advisory roles are not explicitly detailed in the provided text. He leads interdisciplinary efforts in generative models and empirical Bayesian methods, contributing to both foundational and applied aspects of modern data science. His work frequently involves international collaborations, reflecting a commitment to advancing global research networks in mathematical sciences.

• Machine Learning
• Bayesian Methods
• Optimization Algorithms
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Sarat Dass is a Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. He is actively engaged in research, teaching, and supervising PhD students. His primary research interests span Statistics, Bayesian Statistics, Data Science, and their applications in Epidemiology and Spatio-temporal processes. He has published extensively, with 136+ research outputs since 2006, focusing on topics like ionospheric TEC forecasting, disease modeling (e.g., chikungunya, COVID-19), and statistical computing methods. Education & Teaching: Teaches courses such as F79BI: Bayesian Inference and Computational Methods, emphasizing advanced statistical methodologies and computational techniques. Research Interests: His work integrates machine learning (e.g., LSTM, neural networks) with statistical modeling for environmental and health applications. Key areas include predicting ionospheric disturbances during solar flares/earthquakes, analyzing disease spread dynamics, and optimizing hydrocarbon exploration using Gaussian processes. Collaborations: Collaborates internationally, particularly in Malaysia, Indonesia, and Pakistan, on projects involving epidemiology, space weather, and geophysical modeling. His research contributes to Sustainable Development Goals related to health, climate action, and innovation. Advising & Grants: Accepts PhD students and has secured grants for projects on disease modeling and environmental data analysis. His work bridges theoretical statistics with practical applications in public health and geophysics.

• Statistics
• Bayesian Statistics
• Data Science
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William Weimin Yoo is an Assistant Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. His research focuses on Bayesian nonparametrics, uncertainty quantification, statistical computing, deep learning, and dark matter physics. He has worked internationally across the UK, Netherlands, France, US, and Canada. He teaches courses including F21BD: Big Data Management, F70TS: Time Series and Machine Learning, and F90AM: Advanced Machine Learning. Yoo is actively supervising PhD students in topics such as uncertainty quantification for clustering and accelerated posterior contraction rates. He emphasizes interdisciplinary research, combining statistical theory with applications in physics and sports analytics. His work bridges computational methods and theoretical guarantees in Bayesian frameworks. His academic contributions span methodological advancements in posterior contraction, credible sets, and spatio-temporal modeling. He frequently engages in collaborative research and peer review across top journals and conferences in statistics and machine learning.

• Uncertainty Quantification
• Statistical Computing
• Deep Learning
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Professor Roderick Murray-Smith is a Professor of Computing Science at the University of Glasgow, affiliated with the School of Computing Science. He leads the Inference, Dynamics and Interaction Research Group and the Information, Data & Analysis Section . Previously, he held positions at institutions including MIT, Technical University of Denmark, and Daimler-Benz Research. His research focuses on Closed-loop Data Science , Machine Learning in Science , and Mobile Human-Computer Interaction . Notable projects include quantum imaging, music recommender systems, and collaborations with industry leaders like Nokia and Samsung. He served as Director of the SICSA research pool and founding Head of the Information, Data & Analysis Section. Key technical contributions span medical image segmentation , neural network architectures , and biomechanical VR simulations . His work bridges control theory, machine learning, and interactive systems, emphasizing real-world applications in healthcare, robotics, and mobile devices.

• Machine Learning
• Human-Computer Interaction
• Dynamic Systems
• +۵ مورد دیگر

Jing Tang is an Assistant Professor at Northumbria University's Newcastle Business School, specializing in Business Analytics and Artificial Intelligence. She teaches undergraduate and graduate level courses in Business Analytics, Machine Learning, Data Management and Visualization, and Decision Making. Her research focuses on the intersection of business applications and artificial intelligence, with particular expertise in: Business Analytics and Data-driven Decision Making Artificial Intelligence and Machine Learning for business applications Optimization and Heuristic Algorithms Social Networks Analysis and Recommender Systems Dr. Tang's recent publications demonstrate a strong focus on applying AI techniques to business problems, particularly in consumer behavior analysis, recommendation systems, and cyber security for online retail. Her work often combines theoretical AI approaches with practical business applications. Among her notable achievements: Supervising PhD student Yijing Li on "Understanding Online Consumer Behaviour through Implicit Feedback: Intention Recognition and Modelling for Business Strategy" since October 2022 Publishing in top-tier journals including IEEE Transactions, ACM Transactions, and Information Sciences Dr. Tang is actively contributing to the advancement of Business Analytics through her research and teaching, bridging the gap between theoretical AI and practical business applications.

• Business Analytics
• Artificial Intelligence
• Data Analytics
• +۴ مورد دیگر

Lidija Zdravkovic is a Professor of Computational Geomechanics in the Department of Civil and Environmental Engineering at Imperial College London, Faculty of Engineering. She is also the Head of Geotechnics, leading research and academic activities in geotechnical engineering. Her work is deeply integrated with the Imperial College Finite Element Program (ICFEP) and the Imperial Centre for Geohazards, reflecting her strong commitment to advancing numerical methods in geotechnical applications. Her research interests are centered on computational geomechanics, with a focus on: Numerical analysis in geotechnical engineering Soil constitutive modeling and boundary conditions Advanced soil behavior, including unsaturated and thermo-hydro-mechanical (THM) coupling Offshore wind foundation systems Soil-structure interaction (SSI) and dynamic soil behavior She has pioneered developments in finite element solution algorithms and constitutive models for complex geotechnical problems. The recent publications (2023–2025) demonstrate a consistent trend in advancing numerical methodologies for geotechnical challenges, particularly in large deformation analysis (e.g., material point methods), uncertainty quantification, THM coupling, and offshore wind foundations. Her work bridges theoretical development with practical applications in infrastructure, energy, and environmental geotechnics. Scientific honors and professional contributions include: Delivered the Geotechnique Lecture in 2013 Editorial Board Member, Computers and Geotechnics (since 2010) Member, Geotechnique Advisory Panel (2003–2006) UK Representative, ISSMGE TC103 on Numerical Analysis Executive Committee Member, British Geotechnical Association (2010–2013) Lidija Zdravkovic has supervised numerous PhD and postdoctoral researchers, though specific names are not listed. She has secured research funding through major infrastructure projects such as Crossrail, Shard of Glass, Heathrow Terminal 5, and Rome Metro, where her numerical modeling expertise was applied. She co-authored two books on finite element analysis in geotechnical engineering and has over 100 academic publications. She is actively involved in the Geotechnics research group at Imperial, contributing to the development of the ICFEP code and mentoring the next generation of computational geomechanics researchers. Her lab focuses on high-fidelity numerical simulation of geotechnical systems under complex loading and environmental conditions.

• Computational Geomechanics
• Numerical Analysis in Geotechnical Engineering
• Soil Constitutive Modelling
• +۷ مورد دیگر

Andrew B. Duncan is a Senior Lecturer in Statistics and Data-Centric Engineering at Imperial College London's Department of Mathematics, part of the Faculty of Natural Sciences. He also leads the Data Centric Engineering Programme at the Alan Turing Institute. His research integrates applied probability, computational statistics, and machine learning to solve industrial challenges in areas like cellular biology, aerospace, and energy systems. He holds a PhD from the University of Warwick and previously lectured at the University of Sussex. Education: PhD in Mathematics, University of Warwick (2013), supervised by Andrew Stuart and Charlie Elliott Postdoctoral research at Imperial College London and the University of Oxford Research Interests: Focuses on developing statistical methodologies for complex engineering systems, including predictive health monitoring, computational statistics, and interdisciplinary applications. Key areas include uncertainty quantification, Bayesian inference, and energy-based models. Advising & Grants: Supervised multiple PhD students and postdocs, many now in academic and industry roles. Active in securing funding for projects on sensor optimization, digital twins, and data-centric engineering. Labs/Teams: Leads the Data Centric Engineering group at the Alan Turing Institute and collaborates with the Statistics section at Imperial College. His work bridges academia and industry, emphasizing practical applications of statistical methods.

• Applied Probability
• Computational Statistics
• Machine Learning
• +۷ مورد دیگر

Nelson Trujillo-Barreto is a Lecturer in Computational Neuroscience at the School of Health Sciences, University of Manchester since January 2023. With over 25 years of experience in brain dynamics analysis, he specializes in developing probabilistic and biophysical generative models of neuroimaging data. His academic journey includes a PhD in Physical Sciences from Havana University (2006) and prior work as Head of the Department for Brain Dynamics at the Cuban Neuroscience Centre (1995-2014). His research focuses on bridging the gap between recorded neuroimaging data and underlying neuronal activity through solving forward and inverse problems. Key areas include Brain Electromagnetic Tomography (BET), Bayesian models for estimating time-varying dynamical connectivity, and multimodal integration of EEG/MEG and fMRI data. His work has significant clinical applications in neurofeedback, brain-computer interfaces, and non-invasive brain stimulation for conditions like Neurofibromatosis Type 1 and Multiple Sclerosis. His recent publications demonstrate strong trends in applying computational neuroscience to neurological disorders, particularly focusing on working memory, brain connectivity, and therapeutic interventions. His research shows increasing emphasis on multimodal integration and clinical translation of advanced neuroimaging techniques. UoM PCHN Best Poster Award (2025) Dr. Trujillo-Barreto maintains active research collaborations with leading institutions including University College London, Cambridge University, McGill University, and various European neuroscience centers. His editorial roles include Associate Editor for Frontiers in Brain Imaging Methods and Frontiers in Computational Neuroimaging. He serves on prestigious review panels including the EPSRC Peer Review College and European Commission College of Experts.

• Brain Dynamics
• Computational Neuroscience
• Neuroimaging Analysis
• +۷ مورد دیگر

Martin King is a Research Fellow in the Department of Physics at the University of Strathclyde, Faculty of Science. He is actively engaged in cutting-edge research in laser-plasma physics, with a focus on ion acceleration, relativistic plasmas, and computational modeling. His work is supported by major grants from EPSRC, and he collaborates extensively with leading researchers in the field. His research interests lie at the intersection of high-intensity laser physics and plasma dynamics. He investigates laser-driven ion acceleration , relativistic transparency , and nonlinear optical effects in plasmas . He also pioneers the use of machine learning for optimizing electron temperature and developing synthetic diagnostics for proton spectra. His work enables advances in compact particle accelerators and fusion research. The recent publications show a strong trend toward integrating artificial intelligence with plasma simulations and experimental diagnostics . His work spans from fundamental plasma instabilities to applied beam engineering, with emphasis on proton beam stability , structured light generation , and energy-selective focusing . He has contributed to multiple EPSRC-funded projects, including Relativistic plasma apertures and Nonlinear Optics in Relativistically Transparent Plasmas . While no formal students are listed, he has collaborated with numerous early-career researchers and contributed to doctoral training initiatives. His datasets and software tools are publicly archived, supporting open science. Dr. King is involved in key research labs and teams focused on laser-plasma interaction experiments, often collaborating with the wider Scottish Universities Physics Alliance (SUPA). His work is conducted within advanced laser laboratories, utilizing high-power laser systems for probing relativistic plasma phenomena.

• Laser-Plasma Physics
• Relativistic Plasma Physics
• Laser-Driven Ion Acceleration
• +۷ مورد دیگر

Dr Eduard Campillo-Funollet is a Visiting Professor in the Department of Mathematics at the University of Sussex, affiliated with the School of Mathematical and Physical Sciences. His work bridges mathematical biology, computational modeling, and public health, with a focus on pandemic response systems and cellular dynamics. Specializes in epidemiological modeling and Bayesian inference for biological systems Active in computational oncology and cell contraction dynamics Developed frameworks for predicting healthcare demand during disease outbreaks Collaborates across disciplines including genetics, physics, and social sciences His research spans mathematical formulations of biological processes and data-driven modeling approaches. Recent work has focused on the mathematical underpinnings of disease transmission dynamics and cellular mechanics, with applications in both public health and molecular biology. Current affiliations include the University of Sussex's Centre for Computational Neuroscience and Mathematical Biology. He maintains active collaborations with institutions across the UK and international research networks.

• Mathematical Biology
• Epidemiological Modeling
• Bayesian Inference
• +۷ مورد دیگر

Malcolm Sambridge is a researcher at the Research School of Earth Sciences, Australian National University . His work focuses on advancing Bayesian inference methods for geophysical problems, particularly through the development of trans-conceptual sampling frameworks. In the article "Trans-Conceptual Sampling: Bayesian Inference With Competing Assumptions" , Sambridge and colleagues propose a novel approach to Bayesian sampling that generalizes trans-dimensional methods, enabling exploration across models with varying conceptual assumptions, basis functions, and forward physics. This framework eliminates the need for bespoke parameter transformations, allowing automated sampling with modern algorithms like Hamiltonian MCMC. The research demonstrates that trans-conceptual sampling achieves results comparable to reversible-jump MCMC in standard cases but extends applicability to scenarios where traditional trans-dimensional methods would fail, such as models with non-local basis functions or heterogeneous parameterizations. Applications range from airborne electromagnetic inversion to synthetic seismic tomography, showcasing the method's versatility in handling complex geophysical problems.

• Geophysics
• Bayesian Inference
• Inverse Problems
• +۲ مورد دیگر