جستجوی استاد راهنمای دانشگاهی

Simon Stringer is a Departmental Lecturer and Director of the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence at the University of Oxford's Department of Experimental Psychology. His research focuses on developing computational models of brain function, including vision, spatial processing, motor control, language, and consciousness. He collaborates with experimental groups across the UK to bridge theoretical and empirical neuroscience. His work aims to advance understanding of neurological disorders and inspire AI/robotics innovations. Education: Holds a BSc and PhD (specific institutions unlisted). Professional roles include Course Director for the MSc in Psychological Research. His lab employs theoreticians to model neural dynamics, particularly exploring how primate visual systems interpret complex scenes and the mechanisms of feature binding in neural networks. Research highlights include computational studies on auditory category learning, head direction cell modeling, and spiking neural networks. While no awards are explicitly listed, his work has been featured in outlets like Nautilus Magazine discussing AI consciousness. Labs/Teams: Leads the Oxford Centre for Theoretical Neuroscience & AI, fostering interdisciplinary collaborations between neuroscience and computer science.

• Theoretical Neuroscience
• Artificial Intelligence
• Vision Science
• +۵ مورد دیگر

Professor Ian Craddock holds dual roles as a Professor at the School of Engineering Mathematics and Technology and Head of the School of Civil, Aerospace and Design Engineering at the University of Bristol. His research focuses on digital health technologies, IoT systems, and wearable sensors for healthcare applications. He leads the SPHERE initiative, a sensor platform for residential healthcare monitoring, and has pioneered work in activity recognition, indoor localization, and disease management (e.g., Parkinson’s, dementia). Education: B.Eng and Ph.D. in Engineering from the University of Bristol, Chartered Engineer (C.Eng). Key projects include the HEmiSPHERE study assessing post-surgery recovery and the CUBOId study on early dementia detection through behavioral analysis. His work integrates machine learning, computer vision, and wireless communication to address clinical challenges in real-world settings. Research interests span smart home technologies, assistive devices, and ethical AI deployment in healthcare. He collaborates with institutions like the Bristol Medical School and the School of Electrical, Electronic and Mechanical Engineering on interdisciplinary projects. Publications highlight advancements in multimodal data fusion, wearable sensor optimization, and pandemic response systems. His contributions bridge engineering innovation with clinical needs, emphasizing user-centric design and privacy-aware technologies.

• Digital Health
• Internet of Things (IoT)
• Biomedical Engineering
• +۳ مورد دیگر

Hongpeng Zhou is a Dame Kathleen Ollerenshaw Fellow at the Department of Computer Science, University of Manchester. His research focuses on interpretable machine learning for cancer research, Bayesian deep learning, and bioinformatics. Previously, he was a postdoctoral researcher at the same university, developing ML algorithms for single-cell transcriptomics data. Education: PhD in Bayesian deep learning, Delft University of Technology (2018–2022) MEng in Control Science, Harbin Institute of Technology (2015–2017) BEng in Control Science, Harbin Institute of Technology (2011–2015) Research Interests: Dr. Zhou specializes in creating interpretable ML models for biological and engineering systems. His work bridges deep learning, Bayesian methods, and domain-specific applications like materials science, geotechnical engineering, and cancer genomics. Core themes include neural network compression, physics-informed learning, and unsupervised exploration of complex datasets. Publication Trends: Recent articles demonstrate interdisciplinary collaboration across computer science, bioinformatics, and mechanical engineering. Dominant themes include interpretable ML architectures, physics-informed neural networks, and applications in biomedicine/materials science, with emphasis on uncertainty quantification and real-world system optimization. Awards: Dame Kathleen Ollerenshaw Fellowship Projects & Advising: Leads the TICM R&D project on efficient 3D Gaussian splatting and co-investigates breast cancer prevention initiatives. Accepts PhD students and collaborates with groups like Prof. Magnus Rattray's bioinformatics team.

• Machine Learning
• Deep Learning
• Interpretable Learning
• +۳ مورد دیگر

Murilo Marinho is a Lecturer in Robotics Engineering Systems at the EEE department, with past roles including Assistant Professor (University of Tokyo, 2019–2023) and Research Associate (University of Tokyo, 2018–2019). His research focuses on surgical robotics, space robotics, and nuclear robotics, with over 50 peer-reviewed publications in top journals like T-RO, RAM, RA-L, T-MRB, and IJMRCAS. He leads the development of the SmartArm surgical robotic system, recipient of the Surgical Robot Challenge 2019: Overall Winner Award , and won the IROS'23 Best Application Paper Award for shared autonomy in vitreoretinal surgery. Teaching responsibilities include EEEN62021 Software for Robotics and EEEN62012&42012 Robotic Manipulators . His work aligns with UN Sustainable Development Goals in healthcare and innovation. Notable contributions include autonomous control algorithms using dual quaternions and vector-field inequalities, validated through surgical simulators and physical prototypes. Research Highlights : Autonomous robotic systems for minimally invasive surgery Multi-arm robotic platforms for space exploration Surgical training simulators with haptic/visual realism Open-source robotic hardware/software (e.g., UMIRobot) Awards : IROS'23 Best Application Paper Award (2023) Surgical Robot Challenge 2019 Overall Winner (2019) University of Tokyo Dean's Commendation for Teaching Excellence (2021) Collaborations include the Centre for Robotic Autonomy in Demanding and Long Lasting Environments (CRADLE) , focusing on advanced robotic autonomy in harsh environments. Active in developing surgical simulators, teleoperation frameworks, and digital twin technologies for robotic validation.

• Robotics
• Surgical Robotics
• Medical Robotics
• +۵ مورد دیگر

Professor Henggui Zhang is a faculty member in the Biological Physics Group at the University of Manchester. His research focuses on computational modeling of cardiac electrophysiology, with emphasis on arrhythmia mechanisms, ion channel dysfunction, and spatiotemporal complexity of biological systems. He holds a Fellowship of the Royal Society of Biology and contributes to sustainable development goals in advanced medical materials and digital health. Research Interests Cardiac arrhythmia modeling at single-cell and tissue levels Chaotic time series analysis and control of cardiac dynamics Computational studies of genetic disorders like Short QT Syndrome Electrophysiological effects of drugs and pollutants Key Contributions His recent work explores circadian rhythm impacts on arrhythmia susceptibility, novel treatments for atrial fibrillation, and AI-driven ECG classification systems. Over 44 supervised students and 172 research outputs underscore his prolific academic leadership. Awards Fellow of Royal Society of Biology (2019) Society Fellowship (Biology) (2019) Grants & Projects Co-Investigator in the 'Manchester Bioelectronics Network' (2018), advancing interdisciplinary bioelectronics research. Labs & Teams Leads the Biological Physics Group, collaborating on projects like AI-based medical diagnostics and cardiac modeling.

• Cardiac Electrophysiology
• Computational Biology
• Biomedical Engineering
• +۳ مورد دیگر

Simon Davidson is a Research Fellow specializing in Advanced Processor Technology, with a focus on neural network hardware implementations and spiking neural network architectures. His work is closely associated with the SpiNNaker project, a massively parallel computing platform designed for large-scale neural simulations. Dr. Davidson's research spans multiple critical areas at the intersection of computer architecture and neuroscience: Spiking Neural Network Hardware Implementation Many-Core Processor Architectures Power Management for Neural Computing Systems Event-based Computation Paradigms FPGA-based Neural Hardware Digital Implementation of Biological Neural Models His recent publications reveal a progression from foundational tool development (SpiNNTools, spiNNlink) to comparative analysis of neural network approaches on hardware platforms. The research demonstrates increasing sophistication in handling million-core systems and addressing challenges in neural network deployment on digital hardware. Dr. Davidson has made substantial contributions to the SpiNNaker ecosystem, developing critical components including interconnect systems, execution engines, and software stacks that enable large-scale neural network simulations with biologically plausible timing constraints.

• Neural Networks
• Spiking Neural Networks
• Many-Core Processing
• +۷ مورد دیگر

Jeffrey Ting is the Interim Head of Computing and Mathematical Sciences and Senior Lecturer at the University of Wolverhampton's School of Mathematics and Computer Science (Faculty of Science and Engineering). His academic qualifications include an MSc in Control Systems from the University of Sheffield, a Postgraduate Diploma in Business Administration from the University of Birmingham, and a BSc Joint Honours in Computer Science and Electronic Engineering from the University of Birmingham. His research focuses on Machine Learning , Computer Vision , and Plenoptic Lightfield Imaging , with broader expertise spanning Mobile Technologies, Software Engineering, Cyber Security, and Web Technologies. Current investigations include developing a hybrid framework for light field scene reconstruction as part of his PhD research. Analysis of his 15 most recent publications reveals predominant themes in educational technology (mobile learning systems, interactive tools) and control systems (industrial automation, multivariable control), with later work expanding into medical informatics and imaging technologies. Publications demonstrate consistent interdisciplinary collaboration and practical applications in industrial and educational contexts. Professional affiliations include Chartered Membership in the British Computer Society and Fellowship in the Higher Education Academy. He currently supervises a PhD project on light field reconstruction and has extensive industry experience in software development and management prior to his academic career.

• Machine Learning
• Computer Vision
• Plenoptic Lightfield Imaging
• +۴ مورد دیگر

Dr. Franck Patrick Vidal is an Honorary Professor at Bangor University's School of Computing and Engineering, with additional affiliations at the Science and Technology Facilities Council. He holds a PhD in Computer Science and has extensive experience in medical imaging, visualization, and simulation. His research focuses on X-ray imaging, computed tomography (CT), and high-performance computing applications in medical physics. He has contributed to developing open-source tools like gVirtualXray for real-time X-ray simulations and has been involved in projects addressing large-scale emergency response visualization (RAMPVIS). Education includes a PhD from Bangor University (2008), a Master's from Teesside University (2002), and a Postgraduate Certificate in Higher Education (2016). He has held roles such as Senior Lecturer and Postdoctoral Research Fellow at institutions like Inria and CEA Saclay. His research interests span medical imaging technologies, optimization algorithms, and the application of artificial intelligence to healthcare. Notable awards include the 'Best Poster Presentation' and the 'David Duce Prize'.

• Medical Imaging
• Computed Tomography (CT)
• X-ray Simulation
• +۴ مورد دیگر

Dr James Knight is a Senior Research Fellow in Informatics at the School of Engineering and Informatics, University of Sussex. He is currently an EPSRC Research Software Engineering Fellow (2022–2026), leading research in neuromorphic computing and spiking neural networks. His work bridges computer science, neuroscience, and robotics, with a focus on bio-inspired artificial intelligence and computational modeling of insect cognition. Education: PhD in Computer Science, University of Manchester (2013–2016) MPhil in Advanced Computer Science, University of Cambridge (2012–2013) BEng in Electronic Engineering, University of Warwick (2003–2006) His research interests center on neuromorphic computing , spiking neural networks , and bio-inspired AI , particularly drawing insights from insect neuroscience to develop efficient and robust machine learning systems. He explores event-based vision, neural hardware acceleration, and computational models of insect navigation and perception. The 15 most recent publications (2024–2025) reflect a strong trend in developing and benchmarking neuromorphic algorithms, including training methods like Eventprop, hardware-software co-design (e.g., FeNN processor), and modeling biological systems such as insect central complex and mushroom bodies. These works span journals like Nature Communications , PLoS Computational Biology , and Neuromorphic Computing and Engineering , as well as IEEE and Springer conferences. Scientific Grants: Unlocking spiking neural networks for machine learning research (EPSRC, 2022–2026) – Principal Investigator ActiveAI – active learning and selective attention for robust, transparent and efficient AI (EPSRC, 2019–2022) – Research Team Member Dr Knight actively collaborates with leading researchers including Thomas Nowotny, Andrew Philippides, and Paul Graham. His work contributes to both theoretical advances in neural computation and practical applications in robotics and AI. He is involved in developing frameworks such as NeuroBench for standardized evaluation of neuromorphic systems. Laboratories and Research Teams: He is part of the neuromorphic computing and computational neuroscience research groups at the University of Sussex, contributing to interdisciplinary projects that integrate software engineering, AI, and biological modeling.

• Neuromorphic Computing
• Spiking Neural Networks
• Bio-inspired Artificial Intelligence
• +۸ مورد دیگر

Professor Andy Philippides is a faculty member in the School of Engineering and Informatics at the University of Sussex, where he holds the title of Professor of Biorobotics (Informatics). He has been continuously affiliated with the university since 1995, progressing from an MSc and PhD student to a permanent academic, achieving full professorship in 2017. His work is deeply interdisciplinary, bridging robotics, neuroscience, and artificial intelligence. His research interests focus on the mechanisms of intelligent behavior through the interaction of body, brain, and environment. Key areas include visual navigation in insects and robots, neuromodulation in neural networks (e.g., GasNets), evolutionary robotics, and computer vision. He leads and contributes to high-impact projects such as Brains-on-Board, INSIGHT, and be.AI, funded by EPSRC, BBSRC, MRC, and the European Union. His recent publications (2022–2025) reflect a strong trend in bio-inspired algorithms, particularly in insect navigation, spiking neural networks, and adaptive robotics. These works span journals like PLoS Computational Biology , Frontiers in Physiology , and Biomimetics , emphasizing visual route learning, memory networks, and neuromorphic computing. He has supervised over 50 MSc theses and numerous PhD students, many of whom now hold academic or industry positions. His collaborations include prominent researchers such as Paul Graham, Phil Husbands, and Tom Collett. He is actively involved in grant-funded research, with current projects extending into 2028. Action-based bio-inspired autonomous navigation (Universities UK) Emergent embodied cognition in shallow neural networks (BBSRC) be.AI - biomimetic embodied Artificial Intelligence (Leverhulme Trust) ActiveAI - active learning and selective attention (EPSRC) He teaches courses such as Intelligence in Animals and Machines and Research Methods in Neuroscience, and has previously taught in computational neuroscience and robotics. His lab, part of the Centre for Computational Neuroscience and Robotics (CCNR), fosters interdisciplinary research in biorobotics and neural modelling.

• Biorobotics
• Computational Neuroscience
• Artificial Intelligence
• +۱۲ مورد دیگر

Dr. Muhammad Hussain is a Lecturer in Computer Science at the University of Huddersfield's School of Computing and Engineering. He is affiliated with the Centre for Industrial Analytics and the Centre for Sustainable Computing, focusing on AI applications for industrial defect detection and sustainable technology. BEng, M.S., and Ph.D. from University of Huddersfield His research spans machine vision, lightweight neural network architectures, and explainable AI for healthcare. He has published extensively on YOLO variants, CNN optimization, and photovoltaic defect detection. Recent publications analyze trends in computer vision models and their deployment on edge devices for manufacturing. He serves on editorial boards (e.g., Machines journal) and chairs international conferences. Supervised work includes 1 PhD student and collaborative projects with industry partners.

• Machine Vision
• Artificial Intelligence
• Industrial Defect Detection
• +۳ مورد دیگر

Laura Sillars is the Director of MIMA (Middlesbrough Institute of Modern Art) and affiliated with Teesside University through the Institute for Collective Place Leadership. She holds a PhD from Durham University and is actively engaged in research, curation, and cultural leadership. Her work bridges academic inquiry and public-facing art practice, with a focus on the civic role of arts institutions. PhD, The Mineral Image: Contemporary Art and Digital Technology, Durham University (Awarded 2022) PGCE (Adult and Community Education), Institute of Education, University of London (2004) MA, Modern Art (1907–1945), Courtauld Institute of Art (2002) BA (Hons), History and History of Art, University of York (2000) Her research centers on the relationship between art and civic society, particularly how visual artists critique technological, economic, and social infrastructures. Drawing from Romantic, mystical, and techno-scientific traditions, her work explores the intersections of art, technology, and public space. She has curated major exhibitions like Liquid Crystal Display and has contributed essays to key publications on artists such as Sonia Boyce and Lynn Hershman Leeson. The recent publications reflect a consistent engagement with digital art, materiality, and cultural critique. Themes include biomaterials, light-based installations, and the role of art in addressing systemic social issues. Her work often involves collaboration with artists and institutions across the UK and internationally. She has held advisory and governance roles in prominent arts organizations including Engage, AHRC, FutureEverything, and Workplace Foundation. Her leadership extends to strategic cultural planning, as seen in her contributions to Middlesbrough’s cultural vision for 2030. Laura Sillars supervises PhD students and leads research projects such as Green Corridors NE , which explores natureculture and biodiversity through collaborative art and ecological practice. She is deeply embedded in both academic and public cultural networks, contributing to exhibitions, reports, and media commentary on the transformative power of art in urban regeneration. She is affiliated with MIMA and the Institute for Collective Place Leadership, where she fosters interdisciplinary collaboration between artists, communities, and academic researchers to reimagine public space and cultural infrastructure.

• Contemporary Art
• Curatorial Practice
• Art and Civic Life
• +۷ مورد دیگر

Dr. Asra Aslam is an incoming Assistant Professor of Data Science and AI at the University of Sheffield, UK, and currently serves as a Research Fellow at the University of Leeds, a Principal Investigator at the Alan Turing Institute, and a Visiting Researcher at Newcastle University. Her interdisciplinary work bridges artificial intelligence, computer vision, and healthcare data science. Ph.D. in Computer Vision and Machine Learning, University of Galway, Ireland (2021) M.Tech and B.Tech in Computer Science and Engineering, Aligarh Muslim University, India Former Assistant Professor at AMU, India Machine Learning Research Scientist at mindtrace.ai, Manchester, UK Her research focuses on Computer Vision , Deep Learning , Graph Neural Networks , and Health Data Sciences , with applications in multimorbidity, medical imaging, and smart cities. She develops AI tools for clinical decision support, including the DynAIRx project for medicines optimization in patients with multiple long-term conditions. Dr. Aslam's recent publications highlight trends in automated clinical codelist development , temporal graph neural networks for patient clustering , GAN-based medical data augmentation , and efficient fire detection models . Her work is published in top venues including CVPR, IEEE Access, Springer, and Elsevier journals. Women of the Future Award (Highly Commended), 2023 Education/Academic Leader of the Year, 2024 Rising Star of the Year, Women in Tech Excellence Awards, 2024 Hildegard Franke Early Career Award, 2024 Global Talent Award (Exceptional) by UKRI, 2023 IEEE/CVF, ICML, AISTATS Travel Awards She leads and co-leads major research grants including the NIHR Team Science Grant (£2.8M) , Alan Turing DSG (£80K) , and Horizon One: Global Academy Crucible . She supervises PhD and Master's students and mentors early-career researchers. Dr. Aslam is a strong advocate for diversity, serving as General Chair of Women in Computer Vision (WiCV) , AI Lead for Women in AI UK , and Early Career Researcher Representative across multiple institutions. She is actively involved in organizing workshops at CVPR, NeurIPS, and AIM-RSF, and serves on editorial boards of Discover Data (Springer Nature) and MDPI Journal of Imaging .

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Varun Ojha is an active researcher with a strong focus on machine learning, neural networks, and optimization techniques applied to diverse domains. His work spans environmental engineering (flood risk management via camera-based river monitoring), biotechnology (metabolic engineering), geotechnical engineering (rock strength prediction), and biomedical applications (ECG denoising). His research emphasizes hybrid methods combining evolutionary algorithms with neural architectures (e.g., neural trees, echo state networks) and multiobjective optimization frameworks. Key collaborations include institutions in Europe and Asia, with a recurring focus on environmental safety and sustainable systems. Ojha has published extensively in high-impact journals like Neural Networks and Biotechnology and Bioengineering , as well as conferences including IEEE CEC and ISDA. His recent work explores transfer learning for waste segmentation, adversarial robustness in deep learning, and surrogate modeling for complex systems.

• Machine Learning
• Neural Networks
• Evolutionary Computation
• +۶ مورد دیگر

Bernd Behr is a Senior Lecturer in Photography at Camberwell College of Arts, University of the Arts London. He has previously taught at the University of Gloucestershire and Goldsmiths, University of London. His practice combines moving image, photography, sculpture, and writing, with a focus on intersections between historical narratives, architectural sites, and visual culture. Education: San Jose State University, California; Goldsmiths, University of London Residencies: Akademie Schloss Solitude (Germany), Makan/Triangle Arts Trust (Jordan) Behr's research explores photographic indexicality, machine vision, and the built environment. His projects often use speculative methodologies to examine material and narrative provenance in imaging systems, particularly through collaborations with engineering institutions and archaeological practices. Recent work investigates autonomous vehicle vision systems, blockchain-based image economies, and taxidermic imaging techniques. His publications and presentations analyze visual cultures across historical and technological contexts. Awards: Beck's Futures 2003, Artsadmin deciBel Award 2006 Grants: Film London funding, Arts Council England Behr maintains active collaborations across art-science fields and contributes to editorial work at the Philosophy of Photography journal.

• Photography
• Moving Image Art
• Architectural Art Practice
• +۷ مورد دیگر