جستجوی استاد راهنمای دانشگاهی

Simon Kingمشاهده پروفایل

استاد

• Speech Synthesis
• Speech Processing
• Acoustic Modeling
• +۴ مورد دیگر

Simon King is a Professor of Speech Processing at the University of Edinburgh , affiliated with the School of Philosophy, Psychology and Language Sciences . He serves as Director of the Centre for Speech Technology Research (CSTR) and teaches courses like Speech Processing and Speech Synthesis , while directing the MSc in Speech and Language Processing . Research Interests His research focuses on: Developing new acoustic models (e.g., Linear Dynamical Models, factorial-HMMs) for speech recognition Advancing unit selection and HMM-based speech synthesis Integrating articulatory measurement data for enhanced modeling Exploring perceptual measures in synthesis criteria Building multilingual speech systems to identify universal speech building blocks Publication Trends Simon's recent work emphasizes deep learning (DNNs, LSTMs) in speech synthesis, multilingual frameworks , and articulatory-acoustic feature integration . His studies often bridge grapheme-based modeling , perceptual error reduction , and noise-robust synthesis . Scientific Awards EPSRC Advanced Research Fellowship (2005-2009) Students & Collaborations He has supervised numerous PhD students including Rasmus Dall, Tom Merritt, and Srikanth Ronanki. Current research fellows like Mirjam Wester and Zhizheng Wu contribute to projects such as Natural Speech Technology (NST) and Simple4All .

Henry Mossمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bayesian Optimization
• Theoretical Physics
• +۳ مورد دیگر

Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk

• Civil Engineering
• Structural Engineering
• Computational Engineering
• +۳ مورد دیگر

Professor Haijiang Li is a Chair in BIM for Smart Engineering at Cardiff University's School of Engineering. His roles include leading the Computational Mechanics and Engineering AI Research Group, directing the BIM for Smart Engineering Centre, and overseeing the BIM MSc programme. He holds editorial roles for journals like Construction Innovation and Automation in Construction , and chairs the European Group of Intelligent Computing in Engineering (EG-ICE). Research focuses on smart computational engineering platforms integrating BIM, AI, and big data for sustainable infrastructure. Key areas include digital twins, disaster management, and resilient urban systems. He has secured £40M in research funding, including £9M as PI, and led over 70 research staff and students. Prof. Li is a Standards Committee Technical Executive at buildingSMART, driving international BIM standards. His work includes co-authoring a book on BIM standards across China, the US, and the UK. Awards include Fellowships from the British Computer Society (FBCS) and the Higher Education Academy (FHEA). His research outputs span over 250 publications, covering topics like AI-driven bridge maintenance, ontology-based decision-making, and energy-efficient urban systems. Collaborations with industry and global partners emphasize practical applications of BIM and smart technologies.

Richard E. Turnerمشاهده پروفایل

استاد

• Probabilistic Machine Learning
• Environmental Prediction
• Spatio-temporal Modelling
• +۴ مورد دیگر

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.

• Neuroscience
• Cognitive Science
• Decision-Making
• +۳ مورد دیگر

Laurence Hunt is an Associate Professor of Experimental Psychology at the University of Oxford and a Tutorial Fellow in Psychology at St John's College. He leads the Laboratory of Decision Dynamics, focusing on neural mechanisms underlying decision-making. His work integrates mathematical models with electrophysiological techniques in humans and animal models to study behavioral and neural data. Key roles include the Wellcome/Royal Society Sir Henry Dale Fellowship and prior postdoctoral positions at UCL and Oxford's Department of Psychiatry. Education: D.Phil in Neuroscience from the University of Oxford (2007-2011), pre-clinical Medicine at Cambridge University. Research emphasizes cognitive computational neuroscience, particularly how neural systems process value, uncertainty, and learning. Collaborators include notable figures like Matthew Rushworth and Christopher Summerfield. Scientific Awards: Wellcome/Royal Society Sir Henry Dale Fellow Advising & Grants: His research is supported by fellowships, though specific grants or advisees are not detailed. The Hunt Lab actively explores topics such as reward processing, decision dynamics, and neural geometry using advanced analytical methods. Labs/Teams: Director of the Laboratory of Decision Dynamics, part of the Department of Experimental Psychology. Engaged in interdisciplinary collaborations across Oxford and international institutions.

Aidan J Hornerمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Memory Research
• Neuroimaging
• +۳ مورد دیگر

Aidan J Horner is a Professor in the Department of Psychology at the University of York. He holds a BSc in Psychology (2005) and MSc in Cognitive Neuroscience (2006) from the University of York, followed by a PhD in Cognitive Neuroscience from the University of Cambridge (2010). His career includes postdoctoral research at Otto-von-Guericke University (2010–2011) and University College London (2011–2016), and a visiting scholar position at Stanford University (2008). He returned to York as a Lecturer in 2016, advancing to Senior Lecturer and his current Professorship. Research Focus: Horner’s work examines how the brain encodes and retrieves long-term memories, particularly spatial and event-based information. He employs experimental psychology, virtual reality, neuroimaging (e.g., fMRI, MEG), and computational modeling to study hippocampal and cortical mechanisms underlying memory formation, consolidation, and forgetting. His recent studies explore the role of theta oscillations in memory binding, the impact of emotion on memory coherence, and forgetting dynamics. Publications & Awards: Over 50 peer-reviewed articles, including high-impact work in Current Biology , Nature Communications , and Cognition . Recognized with the Annual Cognitive Paper Prize Award (2022) for groundbreaking contributions to memory research. Grants & Projects: Lead investigator on ESRC-funded projects (e.g., "Promoting rapid and sustained learning of novel information" , 2018–2022). Collaborates with institutions like the York Neuroimaging Centre (YNiC) to advance neuroimaging techniques in cognitive studies. Labs & Teams: Affiliated with the York Neuroimaging Centre (YNiC), integrating neuroimaging with behavioral and computational approaches to memory systems. Active in interdisciplinary teams studying memory plasticity and cognitive neuroscience.

• Cognitive Psychology
• Social Cognition
• Neuroscience
• +۷ مورد دیگر

Professor Heather Ferguson is a distinguished cognitive psychologist at the University of Kent's School of Psychology, where she has built an impressive academic career since her appointment as Lecturer in 2009. Promoted to Professor in 2018, she leads groundbreaking research examining the cognitive basis of social communication across the lifespan. Her work bridges cognitive psychology, neuroscience, and social cognition, with particular expertise in perspective-taking, theory of mind, and the interface between cognitive processes and social interaction. Her research primarily investigates how we access and represent other people's perspectives during communication, using methodologies including eye-tracking, EEG, and reaction time measurements. Key questions driving her work include: How do people understand and predict events based on others' mental states? What happens when these conflict with our own knowledge? How do social abilities relate to cognitive skills like memory and inhibitory control? How does aging affect social cognition? Her work has significant implications for understanding autism spectrum disorder, social development, and cognitive aging. Professor Ferguson's recent publications reveal a strong focus on perspective-taking across the lifespan, social cognition in autism, the cognitive effects of fiction reading, and the neural mechanisms underlying empathy. Her research demonstrates consistent methodological rigor through pre-registered studies and meta-analyses, with particular attention to developmental trajectories from adolescence through older adulthood. Psychonomic Society Early Career Award (2019) Open Science Framework Pre-registration Challenge award (2018) Cognitive Neuroscience Society Postdoctoral Fellow Award (2018) University of Kent Prize for Research (2016) Kent Union Teaching Award for 'Best Teacher' (2012) Professor Ferguson actively supervises multiple PhD students and has secured substantial research funding, including a €1.5 million ERC Starting Grant and multiple Leverhulme Trust grants. Her work with the Cognitive basis of Social Communication and Ageing (COGSOCOAGE) research group has produced influential findings on social cognition across the lifespan. She serves as Associate Editor for the Journal of Experimental Psychology: Learning, Memory & Cognition and on the editorial board of Cognition, while also mentoring early-career researchers through the Eastern Arc Mentoring Scheme.

• Neuroscience
• Synaptic Plasticity
• Neural Circuit Formation
• +۴ 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.

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

Matthias Gruberمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Memory
• Motivation
• +۳ مورد دیگر

Dr. Matthias Gruber is a Reader (Associate Professor) in Cognitive Neuroscience at Cardiff University's School of Psychology. He leads the Cardiff University Motivation and Memory Lab at CUBRIC, where his research investigates the neuroscience of motivation and its effects on memory using multimodal neuroimaging techniques (structural/functional MRI, M/EEG). His work focuses on how intrinsic motivational states like curiosity enhance learning and memory consolidation. Education includes: PhD in Cognitive Neuroscience, University College London (2007-2011) Research Assistant position at University of Regensburg (2003-2006) Research explores: Neural mechanisms of curiosity and motivation Effects of reward anticipation on memory encoding Hippocampal-prefrontal interactions during learning Developmental aspects of curiosity from childhood to adulthood Real-world applications of motivation research His publications consistently demonstrate how motivational states modulate memory systems, with recent work focusing on spatial exploration dynamics and educational applications. Awards and honors: Sir Henry Dale Fellowship (Wellcome/Royal Society, 2019-2024) Laird Cermak Award (2016) Michael S. Gazzaniga Prize for Cognitive Neuroscience (2015) Elected to Memory Disorders Research Society (2017) Research funding includes major grants from Wellcome Trust, Royal Society, and European Commission. He leads the Motivation and Memory Lab at CUBRIC and is currently accepting expressions of interest from potential research trainees.

Andrea Greveمشاهده پروفایل

مدرس

• Cognitive Psychology
• Memory Studies
• Neuroscience
• +۳ مورد دیگر

Dr Andrea Greve is a Lecturer in the Department of Psychology at the University of Cambridge . Her research focuses on cognitive processes related to memory, prediction error, and learning mechanisms. Key areas of interest include declarative memory formation, semantic predictions, and the influence of novelty on memory retention. She has explored topics such as word learning in variable-choice paradigms, the role of hippocampal lesions in memory binding, and predictive coding in neuroimaging contexts. Her work integrates experimental psychology with neuroscience methodologies, particularly leveraging neuroimaging techniques to investigate memory systems. Notable contributions include studies on false memory effects, the nonmonotonic relationship between object-location memory and expectedness, and the impact of prior knowledge on memory encoding. Dr. Greve has also contributed to methodological advancements, such as improved MRI anonymization for MEG coregistration. While her research spans multiple decades, recent efforts (2023–2025) emphasize predictive frameworks and their applications in understanding cognitive phenomena like semantic surprise and episodic memory formation. Her findings challenge traditional assumptions about fast mapping in adults and highlight the importance of integrating computational models with empirical data. Dr. Greve collaborates extensively with neuroimaging and cognitive science teams, contributing to interdisciplinary projects that bridge theoretical and applied research in memory systems. Her work maintains a strong focus on methodological rigor, particularly in experimental design and data interpretation.

• Neuroscience
• Cognitive Neuroscience
• Memory Processes
• +۴ مورد دیگر

Simon Hanslmayr is a Professor in the School of Psychology & Neuroscience at the University of Glasgow. His research investigates neural oscillations' role in attention and memory processes, employing EEG, fMRI, and transcranial stimulation techniques. He focuses on healthy populations and clinical conditions like Schizophrenia and PTSD. His lab develops tools like the Brain Time Toolbox for electrophysiological data analysis. Education: Ph.D. in Cognitive Neuroscience (not explicitly detailed in text) Research interests include understanding how precise neural timing via oscillations underpins cognitive functions. Key areas: hippocampal memory coding, theta phase synchronization in associative memory, and causal effects of rhythmic stimulation on memory plasticity. Recent articles highlight mechanisms linking theta oscillations to memory formation, thalamocortical interactions in perception, and hippocampal-neocortical coupling. His work bridges experimental and computational approaches to model memory dynamics. Grants: Sensory stimulation for memory impairment (BIAL Foundation, 2025-2026) EU-funded studies on neural oscillations and memory (2020-2021) Awards: None explicitly listed, but active grant recipient. Supervised students include Kiera Capstick, Eleonora Marcantoni, and others. Collaborates with researchers worldwide through lab affiliates and visiting scholars. Current work emphasizes scalable neurotechnologies for cognitive enhancement and memory rehabilitation. Labs/Teams: Leads the Memory & Oscillations Lab at the University of Glasgow, collaborating with institutions like the University of Zurich and Maastricht University on neuroimaging and clinical studies.

• Neuroimaging
• Neuroscience
• Functional Magnetic Resonance Imaging (fMRI)
• +۳ مورد دیگر

Prof. Stephen Mayhew is a Professor at Aston University's School of Life & Health Sciences, affiliated with the College of Health and Life Sciences. He leads the Cognition & Neuroscience Research Group (CNRG) and contributes to the Aston Research Centre for Health in Ageing. His research focuses on multimodal neuroimaging (EEG/fMRI/MRI/MEG) to study brain activity in health and cognition, particularly negative BOLD responses and their role in neural inhibition and behavior. Recent work includes investigating task-demand effects on BOLD responses, brain state dynamics during rest, and neurovascular coupling methods. Education & Affiliations: Affiliated with Aston University's School of Life & Health Sciences Member of CNRG and Health in Ageing Centre Research Interests: His work explores collaborative/antagonistic brain networks, lifespan changes in brain function, and the functional significance of negative BOLD responses. Techniques include fMRI, EEG, and advanced neuroimaging analysis methods like fractal dimension and laminar 7T MRI. Key Contributions: Published over 30 peer-reviewed articles on neuroimaging and brain networks Systematic reviews on neurovascular coupling and transcranial Doppler methods Labs & Teams: Active in CNRG and collaborates across disciplines in health sciences and bioengineering.

Lin Wangمشاهده پروفایل

مدرس

• Machine Learning
• Audio-Visual Signal Processing
• Robotic Perception
• +۳ مورد دیگر

Dr. Lin Wang is a Lecturer in Applied Data Science and Signal Processing at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He leads the Machine Listening Lab and is a member of the Centre for Multimodal AI, Centre for Intelligent Sensing (CIS), and Institute of Coding (IoC). His research focuses on audio-visual signal processing, robotic perception, and machine learning, with applications in healthcare, drone-based sensing, and human activity recognition. Dr. Wang holds a PhD from Dalian University of Technology and has held postdoctoral positions at QMUL, the University of Sussex, and the Alexander von Humboldt Foundation in Germany. Education and Roles: PhD in Signal Processing, Dalian University of Technology (2010) Postdoc at Queen Mary University of London (2014–2017) Postdoc at University of Sussex (2017–2018) Alexander von Humboldt Fellow at University of Oldenburg (2011–2013) Fellow of the Higher Education Academy (UK) Research Interests: Audio-visual signal processing for drones and wearable devices Machine listening and robotic perception Machine learning for healthcare and environmental monitoring Human activity recognition using multimodal sensors Awards and Grants: Early Career Champion on AI&Data, UK Acoustics Network Outstanding Article Award, Frontiers in Computer Science (2022) EPSRC grant: Bioacoustic Monitoring Using Drones (£46,821, 2022–2023) Innovate UK grant: Music Source Separation (£48,144, 2024–2025) Teaching and Students: Dr. Wang teaches Applied Statistics , Website Design and Authoring , and Machine Learning for Visual Data Analysis . He supervises PhD students including Ashish Alex (speech separation), Michael Clayton (drone audition), and Dmitrii Mukhutdinov (audio-visual processing). Labs and Teams: He co-leads the Machine Listening Lab and is part of the Centre for Multimodal AI, focusing on interdisciplinary projects in robotics, acoustics, and AI.

• Neural dynamics of memory circuits
• Memory-guided behavior
• In vivo multichannel recordings
• +۵ مورد دیگر

Professor David Dupret is a Professor of Neuroscience and MRC Investigator at the University of Oxford, where he also serves as a Tutorial Fellow in Biomedical Sciences at St Edmund Hall. His work takes place within the MRC Brain Network Dynamics Unit, part of the Nuffield Department of Clinical Neurosciences, and he is affiliated with the Department of Physiology, Anatomy and Genetics. David completed his Ph.D. in Neuroscience at the Institute François Magendie (INSERM, University of Bordeaux, France), receiving the French Neuroscience Association's 2007 Ph.D. Year Prize. He joined the MRC Anatomical Neuropharmacology Unit in 2007 as a Visiting Fellow, funded by the Institute of France and the International Brain Research Organisation. In 2009, he became an MRC postdoctoral scientist and Junior Research Fellow at St Edmund Hall, progressing to MRC Programme Leader Track scientist in 2011 and tenured MRC Programme Leader in 2014. Professor Dupret's research focuses on the circuit-level mechanisms of memory-guided behavior, with particular emphasis on neural dynamics of memory circuits during active waking behavior and sleep. His laboratory employs in vivo multichannel recordings and optogenetic manipulation of neuronal ensembles to investigate how hippocampal networks organize memory processes. His work has revealed fundamental insights into how memory circuits operate during both waking behavior and sleep states, particularly regarding hippocampal ripple activity, dentate spikes, and offline reactivation processes. Analysis of Professor Dupret's recent publications reveals a consistent focus on hippocampal network dynamics and memory processes. His work spans from basic neural circuit mechanisms to applications in neurodegenerative conditions like Alzheimer's disease. A notable trend is the integration of computational approaches with experimental neuroscience to understand how neural assemblies encode and retrieve memories. His team has made significant contributions to understanding how dentate spikes support memory flexibility and how hippocampal ripple diversity organizes neuronal reactivation during offline states. French Neuroscience Association's 2007 Ph.D. Year Prize Foundation Louis D. Research Fellowship (2007) International Brain Research Organisation Fellowship (2008) FENS-Kavli Network of Excellence Scholar (2016) Boehringer Ingelheim-FENS Research Award (2018) Elected to membership of Academia Europaea (2024) Professor Dupret has secured substantial research funding through his MRC Programme Leader position and has mentored numerous researchers who appear as co-authors on his publications. His laboratory, the Dupret Group, operates within the MRC Brain Network Dynamics Unit, collaborating extensively with other research groups including the Sharott Group, Magill Group, and Denison Group. Current research directions include investigating how memory circuits maintain flexibility while resisting extinction, exploring the relationship between neural coactivity patterns and memory organization, and developing computational models of hippocampal function. His team is actively pursuing future work on the mechanisms underlying memory persistence and the neural basis of flexible memory recall.