جستجوی استاد راهنمای دانشگاهی

Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.

• Natural Language Processing
• Machine Learning
• Dialogue Modelling
• +۱۰ مورد دیگر

Dr Mobarak Hoque serves as a Senior Lecturer in the Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester. He concurrently holds an external position as Honorary Senior Research Fellow at University College London (UCL) from July 2025 to June 2028. Academic Background: Doctor of Philosophy in Representation Learning in Multimodal Spatiotemporal Image-Guided Medical Procedures, National University of Singapore (NUS), awarded 2019 Research Expertise: Dr Hoque's work centers on Biomedical Data Science and Artificial Intelligence with specialization in Multimodal Large Language Models , Vision-Language Models , and Safe and Responsible AI . His research directly addresses UN Sustainable Development Goal 3 (Good Health and Well-being) through innovations in surgical robotics, medical imaging, and endoscopic procedures including polyp segmentation and low-light enhancement. Publication Trends: Recent publications (2023-2025) reveal a cohesive research trajectory focused on robust AI for surgical applications. Key methodologies include adversarial feature disentanglement, semi-supervised learning frameworks, and multimodal integration of vision-language models. His work consistently targets real-world clinical challenges like instrument segmentation, surgical workflow recognition, and low-light endoscopy enhancement using advanced techniques such as wavelet attention and diffusion models. Academic Supervision: Dr Hoque actively mentors PhD candidates in Biomedical Data Science and AI, particularly welcoming students interested in multimodal learning and responsible AI deployment for healthcare applications.

• Biomedical Data Science
• Artificial Intelligence
• Medical Imaging
• +۳ مورد دیگر

Professor Neil Telling is Research Director at Keele's School of Pharmacy and Bioengineering, specializing in biomedical nanophysics. His research develops magnetic nanostructures for medical applications and investigates biomineralized iron deposits in neurological tissue using advanced synchrotron techniques. Research pillars: Nanoscale magnetic materials for targeted therapies X-ray spectromicroscopy of biological tissues Iron speciation in neurodegenerative conditions He maintains extensive international collaborations and regularly conducts experiments at major synchrotron facilities including the Advanced Light Source and Diamond Light Source.

• Magnetic Nanostructures
• Synchrotron Imaging
• Biomedical Applications
• +۱ مورد دیگر

Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Professor Thomas Blumensath is a Professor of Signal and Image Processing at the University of Southampton and a Fellow at the Alan Turing Institute. He is the Academic Lead in Image Processing and Reconstruction at the University's μ-VIS X-ray Imaging Centre and Director of Research at the Institute of Sound and Vibration Research (ISVR). His research focuses on advanced algorithms for solving inverse problems in tomographic imaging, combining machine learning, optimization, and statistical methods. Key areas include X-ray tomography strategies, GPU-accelerated reconstruction, and multimodal imaging applications. Education: B.Sc. (Hons) Music Technology and Audio System Design, University of Derby (2002) PhD in Electronic Engineering (Bayesian Signal Processing), University of London (2006) Research Interests: Professor Blumensath's work spans theoretical and applied signal/image processing, with emphasis on tomographic imaging techniques. His current projects address efficient reconstruction methods, spectral X-ray CT, and applications in manufacturing and plant science. He collaborates with advanced imaging facilities like Diamond Light Source and ISIS neutron imaging beamline. Key Contributions: His research bridges computational methods (e.g., compressed sensing) with practical imaging challenges, including limited-angle tomography and stereo imaging strategies. He leads the National Research Facility for Lab X-ray CT and has developed the TIGRE reconstruction toolbox. Grants & Projects: Active funding includes EPSRC projects on tomographic sensitivity monitoring and CT-based manufacturing inspections. Completed projects cover constrained reconstruction, AM process verification, and industrial CT metrology. Awards: Alan Turing Institute Fellowship Teaching & Leadership: He teaches modules on machine learning, biomedical image processing, and robotics. Leads the BEng Control Engineering program at the Joint Education Institute with Harbin Engineering University. Labs/Teams: Active in the Signal Processing, Audio and Hearing research group (SPAH) and the Institute for Life Sciences. Oversees the μ-VIS X-ray Imaging Centre's research initiatives.

• Signal Processing
• Image Processing
• X-ray Tomography
• +۶ مورد دیگر

Andrew Markham is a Professor of Computer Science at the University of Oxford , affiliated with Kellogg College . He leads a research group focusing on Cyber Physical Systems (CPS) , specializing in sensors, signal processing, and machine learning to enable machines to better perceive the physical world. His work emphasizes cross-disciplinary collaboration, notably in wildlife tracking and indoor positioning systems. He has held roles as a Postdoctoral Fellow (2008-2012), Associate Professor (2013), and Full Professor (2021). Education : PhD in Electrical Engineering (University of Cape Town, 2008), BSc (Hons) in Electrical Engineering (2004). Research Interests : Tracking and localization in GPS-denied environments (e.g., underground, indoors), magneto-inductive systems, physics-informed machine learning, and data-driven approaches for noisy sensor data. His projects include wildlife monitoring via wireless sensor networks and mmWave radar for human motion capture. Key Projects : CARACAL acoustic monitoring system, mmPoint dense human tracking, and RandLA-Net for large-scale point cloud segmentation. His work spans robotics, environmental sensing, and biomedical applications. Advising & Grants : Supervises over 30 students and collaborates with industrial partners. Research teams include Cyber Physical Systems, Autonomous Ubiquitous Sensing, and Wildlife Monitoring initiatives. Labs/Teams : Leads the CPS research group, focusing on sensor networks, inertial navigation, and multimodal fusion systems. Collaborates with zoology and earth science disciplines on applied projects.

• Sensors and Sensor Networks
• Signal Processing
• Machine Learning
• +۶ مورد دیگر

Ximian Xu (preferred name: Simeon) is a Senior Research Associate in the Faculty of Divinity at the University of Cambridge , with a PhD in Systematic Theology from the University of Edinburgh (2020). His research bridges theology with technology, focusing on artificial intelligence (AI) ethics, Dutch Neo-Calvinism, Karl Barth, and contextual theology in mainland China. Xu’s work explores the intersection of AI and Christian ethics, the role of Reformed theology in contemporary Chinese Christianity, and the ontological implications of technology on human sustainability. Research Interests Theology and Technology (AI, Surveillance) Dutch Neo-Calvinism (Herman Bavinck) Karl Barth Contextual Theology (Reformed in China) Grants and Organisational Roles He received the CAHSS KE Grant for the workshop "Artificial Intelligence and the Christian Churches" and the Alan Turing Institute Postdoctoral Enrichment Award for research on AI in pastoral care. Xu co-founded the Studies in Dutch Neo-Calvinism Series (Chinese) and is a co-editor for its publications. Publications and Awards His recent works include studies on AI’s moral agency, technomoral futures, and Reformed theological frameworks. Xu’s book The Digitalised Image of God: Artificial Intelligence, Liturgy, and Ethics (2024) addresses AI’s theological challenges. He has contributed to journals like Perspectives on Science and Christian Faith and Modern Theology .

• Theology and Technology
• Systematic Theology
• Dutch Neo-Calvinism
• +۴ مورد دیگر

Dr. Andrea Lecchini Visintini is an Associate Professor at the School of Electronics and Computer Science , University of Southampton. He specializes in systems modelling and control with applications in aerospace engineering and biomedical domains, utilizing Monte Carlo methods for stochastic optimization. Cyber-Physical Systems Research Group Institute for Life Sciences Research Focus: His work bridges computational methods with practical applications in: Neurovascular coupling and brain tissue pulsation analysis Advanced control strategies for aerospace systems Stochastic optimization in machine learning and fault detection Medical imaging and diagnostic protocol development Publication Trends: Recent work emphasizes interdisciplinary approaches combining computational neuroscience with engineering, focusing on brain hemodynamics, MIMO system control, and data augmentation techniques for imbalanced datasets. Supervision: Currently supervising PhD student Xuankun Cai in Computer Science.

• Systems Modelling and Control
• Aerospace Engineering
• Biomedical Applications
• +۱ مورد دیگر

Dr. Hongye Zhang serves as a Lecturer in Superconducting and Cryogenic Electric Machines at the School of Engineering, University of Edinburgh, while maintaining a Visiting Research Fellow position at the University of Manchester. He actively contributes to the European Society for Applied Superconductivity (ESAS) as a Board Member and chairs the international HTS 2026 workshop. His educational foundation includes: BSc and MSc in Electrical Engineering from Xi’an Jiaotong University (2015, 2018) Diplôme d’ingénieur (MEng) from École Centrale de Lyon (2018) PhD in Applied Superconductivity from the University of Edinburgh (2021) Dr. Zhang’s research centers on decarbonizing transport through superconducting/cryogenic electric machines for hydrogen-powered aircraft, integrating artificial intelligence with superconductor technology and cryogenic techniques. His work targets net zero emissions by developing high-power-density propulsion systems that leverage hydrogen energy and advanced numerical modeling of superconductors. Analysis of his 2022-2025 publications reveals dominant themes in superconducting machine design for wind energy and electric aviation, with significant contributions to loss mitigation, flux pump technology, and trapped field magnet applications. His research bridges fundamental superconductor characterization with practical system integration for renewable energy. Recognized with the 2021 IEEE Council on Superconductivity Graduate Study Fellowship, his professional engagements include: Early Career Editorial Board Member for Elsevier’s Superconductivity journal Technical Editor for IEEE Transactions on Applied Superconductivity Program Committee Member for SMT 2023 He leads critical research within the £54-million H2GEAR project developing hydrogen-electric aircraft propulsion, while teaching Power Engineering 2 and Electrical Machines courses. His advisory roles span doctoral supervision and industry collaboration through Energy Systems research institute. Based at the University of Edinburgh’s Faraday Building, Dr. Zhang directs a research group focused on hydrogen energy applications and superconducting machine testing, with strong ties to the H2GEAR consortium and ESAS working groups.

• Superconductor Technology
• Cryogenic Techniques
• Electric Machines and Drives
• +۳ مورد دیگر

Professor David Clifton is the Royal Academy of Engineering Chair of Clinical Machine Learning at the University of Oxford’s Institute of Biomedical Engineering. He leads the Computational Health Informatics (CHI) Lab, focusing on AI-driven healthcare solutions with a strong emphasis on translational research in low- and middle-income countries (LMICs). His work spans digital health technologies, medical imaging analysis, and AI ethics. Clifton holds multiple fellowships, including from the Alan Turing Institute and Fudan University. Key affiliations include co-directorship of the Oxford-CityU Centre for Cardiovascular Engineering and involvement in the Wellcome Trust’s Flagship Centre in Vietnam. His research has been commercialized through spinouts like OBS Medical and Oxehealth. Notable projects include AI tools for non-invasive vital sign monitoring and pandemic response strategies using audio-based health data. Clifton’s awards include the IEEE Early Career Award (2022) and the Vice-Chancellor’s Innovation Prize. His lab’s Suzhou branch focuses on open-source digital health research using public datasets. Current research themes include multimodal data integration, generative AI in healthcare, and equitable AI deployment across global health systems.

• Clinical Machine Learning
• Digital Health
• AI in Healthcare
• +۴ مورد دیگر

Zaiqiao Meng is a Lecturer (Assistant Professor) at the University of Glasgow's School of Computing Science, affiliated with the Information Retrieval Group and IDA section. He also holds an Affiliated Lecturer position at the University of Cambridge's Language Technology Lab. His research focuses on the intersection of machine learning, knowledge graphs, and NLP, particularly in biomedical applications. Key areas include AI agents, large language models, and healthcare informatics. Current roles include co-leading the Glasgow AI4BioMed Lab, which develops AI solutions for biomedical knowledge extraction. He has extensive postdoctoral and visiting research experience, including at KAUST's MINE lab. Meng has published widely in top conferences like ACL and EMNLP, with over 40 publications since 2019. His work spans topics such as drug-target interaction prediction, clinical summarization, and knowledge graph construction. Teaching includes courses on Recommender Systems and Data Science at both undergraduate and graduate levels. He advises multiple PhD students on projects involving LLMs, biomedical entity representation, and conversational agents.

• Natural Language Processing
• Information Retrieval
• Large Language Models
• +۳ مورد دیگر

Dr. Alex S Clark is an Associate Professor in Quantum Technologies at the University of Bristol's School of Physics, where he serves as a Senior Lecturer and Royal Society University Research Fellow. He is a key member of the Quantum Engineering Technology Labs (QETLabs) and leads the Interfaces Work Package in the EPSRC Programme Grant 'Quantum Science with Ultracold Molecules (QSUM).' Additionally, he holds a Visiting Academic position at Imperial College London and serves as Honorary Secretary for the QQQ Group at the Institute of Physics. His research focuses on Solid State Quantum Nanophotonics, exploring the use of atoms, molecules, and solid state defects to develop quantum technologies. Dr. Clark's work spans quantum imaging, quantum sensing, and quantum information processing, with particular emphasis on creating on-demand photon sources, quantum memories, photonic quantum gates, and hybrid interfaces to link disparate quantum systems. His research integrates experimental and theoretical approaches across quantum photonics, nanophotonics, and quantum technology. Analysis of his recent publications reveals a strong trend toward practical quantum applications, particularly in quantum sensing and imaging using undetected light. His work demonstrates increasing focus on real-world applications including methane sensing, medical diagnostics, and environmental monitoring, while maintaining fundamental research in quantum optics and nanophotonics. The interdisciplinary nature of his research bridges physics, engineering, and materials science. Among his notable achievements is the prestigious Royal Society University Research Fellowship, recognizing his significant contributions to quantum technology research. His work has resulted in numerous publications and patents in quantum photonics and related fields. Dr. Clark leads multiple major research initiatives, including the Quantum Positioning, Navigation, and Timing Hub (2024-2029) and the Integrated Quantum Networks project. His research has secured substantial funding through EPSRC grants and other sources, supporting a vibrant research group focused on advancing quantum technologies from fundamental principles to practical applications. Within the Quantum Engineering Technology Labs (QETLabs), Dr. Clark's research group works at the intersection of quantum optics, nanophotonics, and quantum information science. His team develops novel photonic platforms for quantum applications, with particular expertise in quantum imaging with undetected photons, quantum sensing, and integrated quantum photonics.

• Quantum Photonics
• Nanophotonics
• Quantum Technology
• +۴ مورد دیگر

Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Isabella Guido is a Senior Lecturer in Experimental Soft Matter Physics at the University of Surrey's School of Mathematics and Physics. She holds a PhD from TU Berlin (2010) and has conducted postdoctoral research at Peking University and the Max Planck Institute for Dynamics and Self-Organization. Her research focuses on synthetic biology, active bioinspired systems, and microtubule-motor protein dynamics. Guido's work bridges active matter physics and synthetic biology, aiming to develop minimal systems mimicking natural cellular structures. Key projects include synthetic beating structures resembling cilia, 3D active nematics, and investigations into cellular symmetry breaking via biomimetic systems. Her education includes a PhD on dielectrophoretic effects in mammalian cells, followed by postdoctoral studies on cell mechanics, microfluidics, and electroporation. Guido's interdisciplinary approach combines experimental biophysics with synthetic biology to uncover principles governing living matter. She leads the Synthetic Active Systems group and collaborates internationally on projects such as light-powered artificial cells and motor-driven microtubule networks. Her work addresses sustainable development goals through bio-inspired material design and active matter applications. Publications highlight contributions to electrotaxis mechanisms, live-cell imaging techniques (e.g., MIET), and microtubule network dynamics under depletion forces. Guido's research has advanced understanding of ciliary beating patterns, synthetic axoneme models, and biopolymer self-organization under mechanical stress.

• Synthetic Biology
• Active Bioinspired Systems
• Active Matter
• +۳ مورد دیگر

Darryl Overby is a Professor of Mechanobiology in the Department of Bioengineering at Imperial College London's Faculty of Engineering. His academic affiliations include the CRUK Convergence Science Centre, Cancer Technology Network, and multiple interdisciplinary networks focused on ocular biomechanics, vascular science, and regenerative medicine. He holds an Orcid identifier (0000-0001-9894-7515) and was previously affiliated with Tulane University and Harvard Medical School. Education: Ph.D. in Mechanical Engineering (2002) from MIT under Prof. Roger Kamm, followed by postdoctoral research at Harvard Medical School under Prof. Donald Ingber. His work focuses on cellular biomechanics, mechanotransduction, and the role of mechanical forces in intraocular pressure regulation and glaucoma pathophysiology. Research interests span mechanobiology of Schlemm’s canal endothelial cells, nitric oxide signaling in ocular tissues, and development of organ-on-chip models for studying aqueous humor outflow dynamics. His lab explores how mechanical signals drive transcellular pore formation and how these mechanisms fail in glaucomatous conditions. Key contributions include consensus guidelines for Brillouin microscopy measurements, ex vivo perfusion studies, and the role of TRPV4 channels in mechanosensing. His work integrates engineering techniques with clinical ophthalmology to advance therapies targeting glaucoma and ocular hypertension. Professional activities include leadership roles in the Ocular Biomechanics Network and Wound Healing and Regeneration Network. His research has led to patents for devices enhancing aqueous humor drainage and RNAi-based therapies.

• Mechanobiology
• Ophthalmology
• Biomedical Engineering
• +۴ مورد دیگر