جست‌وجوی استاد و پژوهشگر

David Cliftonمشاهده پروفایل

استاد

• Clinical Machine Learning
• Digital Health
• AI in Healthcare
• +۴ 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.

Anandadeep Mandalمشاهده پروفایل

دانشیار

• Digital Finance
• Financial Technology
• Artificial Intelligence in Finance
• +۱۲ مورد دیگر

Dr Anandadeep Mandal is an Associate Professor in Finance and the Scotcoin Distinguished Chair of Digital Finance at the University of Birmingham , within the Birmingham Business School and the Department of Finance . He is the founding director of the MSc Financial Technology programme and the Programme Director for the MBA (Distance Learning), demonstrating significant leadership in academic program development. Education: PhD in Probability Distribution Fitting, Cranfield University (2016) MRes in Management Science, Cranfield University (2012) MSc in Finance and Investments, Durham University (2008) Bachelor’s in Electronics Engineering Research Interests: Dr Mandal’s interdisciplinary research lies at the intersection of mathematical modelling, artificial intelligence, finance, and digital innovation . His work focuses on AI-enabled investment strategies , blockchain for financial transparency , ESG performance measurement , and the development of the Sustainable Efficiency Index (SEI) . He also pioneers AI applications in digital education , including a patent-pending platform for automated grading of multi-modal student submissions using ensemble AI methods. Publication Trends: His recent scholarly output spans high-impact journals and conferences, reflecting a strong focus on digital finance , climate and social media analytics , cryptocurrency regulation , and AI in financial forecasting . His work combines advanced data science techniques with real-world policy and financial applications, particularly in sustainability and public health. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: Dr Mandal has secured over £2 million in research funding from sources including UKRI, UoB QR Funding, and industry partners. While specific students are not listed, his role as programme director and research leader suggests active mentorship. His research has direct policy impact through collaborations with the NHS Trusts , NIHR , and the UK Government . Labs, Teams, and Impact: Dr Mandal leads a research agenda that bridges academia and public policy. His work extends beyond the university through public engagement at science festivals, outreach for young learners, and expert contributions to UK Parliamentary consultations on AI, sustainability, and financial innovation. He is a key figure in advancing digital finance education and research at the University of Birmingham.

Ferenc Huszárمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning Theory
• Optimization
• +۶ مورد دیگر

Ferenc Huszár is an Associate Professor of Machine Learning at the University of Cambridge, affiliated with the Department of Computer Science and Technology. His research focuses on foundational aspects of deep learning, including optimization, generalization, representation learning, and causal reasoning. He co-founded Magic Pony Technology, where he contributed to super-resolution and compression techniques, later acquired by Twitter. Education: PhD in Bayesian Machine Learning from the University of Cambridge (supervised by Carl Rasmussen, Máté Lengyel, and Zoubin Ghahramani), followed by roles in tech/startups. Research Interests: Theoretical underpinnings of deep learning, neural network behavior analysis, LLM theory, causal inference, and AI safety. His lab explores algorithmic reasoning in neural networks and implicit Bayesian inference in LLMs. Selected Contributions: Co-authored influential papers on super-resolution (CVPR 2016) and GAN-based image enhancement (CVPR 2017). Active in advising 9 PhD students and mentoring research assistants. Grants & Collaborations: Collaborates with institutions like the Max Planck Institute and ELLIS. Supervises projects on causal representation learning, geometric deep learning, and federated learning.

Zheng Yuanمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Educational Technology
• +۳ مورد دیگر

Dr. Zheng Yuan is an Associate Professor (Senior Lecturer) in the School of Computer Science at the University of Sheffield. Previously, they held roles as an Assistant Professor at King's College London and a Research Associate at the University of Cambridge's Department of Computer Science and Technology. Their primary research focuses on machine learning and deep learning applications in natural language processing (NLP), particularly in educational technology, healthcare, creativity, and multilingual contexts. Key projects include computer-assisted language learning (CALL), human-centered NLP in education, computational code-switching, and creative AI. Education includes a PhD and MPhil in Natural Language Processing from the University of Cambridge, and a BSc(Eng) from Queen Mary University of London. They hold affiliated positions at the University of Cambridge, King's College London, and are a Fellow of Trinity College, Cambridge. They contribute to The Alan Turing Institute's Data-Centric Engineering Programme and hold FHEA status (2024-). Research interests span educational NLP, multilingual systems, transfer learning, and explainable AI. They actively organize workshops and serve on editorial boards (e.g., PeerJ Computer Science) and conference committees (ACL/EMNLP). Recent activities include co-organizing NLP workshops at ACL 2025 and NAACL 2024, alongside roles in professional societies like the ACL Professional Conduct Committee. Awards include Fellowship of the Higher Education Academy (2024-) and ASEFClassNet18 Faculty Collaboration (2025-). They welcome PhD applications in NLP and machine learning, emphasizing interdisciplinary applications.

Zaiqiao Mengمشاهده پروفایل

مدرس

• Natural Language Processing
• Information Retrieval
• Large Language Models
• +۳ 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.

Jim Smithمشاهده پروفایل

استاد

• Interactive Artificial Intelligence
• Statistical Disclosure Control
• Privacy Leakage from AI Models
• +۷ مورد دیگر

Jim Smith is a Professor in Interactive Artificial Intelligence at the University of the West of England (UWE), Bristol, affiliated with the School of Computing and Creative Technologies and the Department of Computer Science and Creative Technologies. He serves as Director of the Computer Science Research Centre and leads the AI@UWE theme. His research is supported by UKRI, Innovate UK, and partnerships with organizations including Health Data Research UK, Office for National Statistics, NHS Scotland, and DSTL. University: University of the West of England School: School of Computing and Creative Technologies Department: Department of Computer Science and Creative Technologies Role: Professor in Interactive Artificial Intelligence Leadership: Director, Computer Science Research Centre Research Interests : Jim Smith's work focuses on Interactive Artificial Intelligence, particularly at the intersection of AI and privacy preservation when using sensitive data for public good. His research includes statistical disclosure control, privacy leakage from AI models, evolutionary computation, machine learning, and systems that learn through human interaction or self-adaptation. He explores how AI can automate privacy checks in research outputs and assess vulnerabilities in trained models. Recent Publications : His recent work spans AI privacy in trusted research environments (e.g., SACRO, SDC-Reboot), dialogue act classification, human-robot interaction, and visualization of deep learning models. Themes include privacy-preserving AI, automated disclosure control, interactive machine learning, and neuromorphic computing. Machine Learning & Privacy Evolutionary Computation Interactive AI Systems Human-Computer Interaction Statistical Disclosure Control Federated Learning Security Scientific Awards : No specific awards are mentioned in the provided texts. Advising and Grants : He currently supervises PhD students on topics including spatio-temporal air quality modeling, federated learning privacy, and threat detection in mobile networks. He leads Innovate UK and UKRI-funded projects such as SACRO and SDC-Reboot, focusing on AI-driven solutions for data confidentiality in public sector research. Interactive Machine Learning for Claim Settlement (Innovate UK) SDC-Reboot (DARE UK/Health Data Research UK) Threat Identification in Mobile Networks (Ribbon Communications) Labs and Teams : He leads the AI@UWE initiative and the Computer Science Research Centre at UWE. His work involves collaboration through DARE UK and open-source development via the AI-SDC GitHub organization, which hosts tools from SACRO and GRAIMATTER projects.

Vishal Sharmaمشاهده پروفایل

مدرس ارشد

• Cyber-Physical Systems (CPS)
• 5G and Beyond Security
• Internet of Things (IoT)
• +۴ مورد دیگر

Dr. Vishal Sharma is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on Cyber-Physical Systems (CPS), 5G/6G Security, Unmanned Aerial Vehicles (UAVs), Blockchain, and Digital Twins. He has held roles at institutions like Singapore University of Technology and Design (SUTD) and Soonchunhyang University, South Korea. Notable achievements include Best Paper Awards at ICCMIT 2017, IEEE SITE 2024, and HUCAPP/VISIGRAPP 2025. He leads the Innovation-by-Design Lab and is a Fellow of the Higher Education Academy (FHEA). Research Interests: Cyber Defence, UAV Security, Secure Computing, Network Security, and Sustainable Edge Computing. He has collaborated on projects like RapidRANDefender (QRICSec) and Traceable Procurement for Net-Zero Processes. Awards include the Royal Society International Exchanges Committee appointment (2025) and QUB's Individual Performance Award (2024). Grants and Projects: Principal Investigator for projects such as Exploring Operational Capabilities of Arm Morello for UAV Security (2023) and TUDOR: Ubiquitous 3D Open Resilient Network (2023). Active in editorial roles for IEEE Communications Magazine and IET Networks. His work aligns with UN Sustainable Development Goals (SDGs) related to climate action and innovation. Publications span 150+ articles in top journals/conferences, with a focus on secure communication, edge computing, and UAV networks. Supervises PhD students in cyber defence, AI security, and distributed ledger technologies.

Aurora Maccaroneمشاهده پروفایل

پژوهشگر ارشد

• Single-Photon Imaging
• Underwater LiDAR
• Quantum Sensing
• +۴ مورد دیگر

Aurora Maccarone serves as an RAEng Research Fellow within the Institute of Photonics and Quantum Sciences at Heriot-Watt University's School of Engineering & Physical Sciences. Her work focuses on advanced photonics applications for challenging environments, particularly underwater and obscured conditions. Her research expertise spans: Single-photon LiDAR systems for underwater 3D imaging Photon-counting detector arrays for depth profiling Real-time reconstruction algorithms for obscurant-penetrating imaging Quantum sensing applications in marine environments Recent publications demonstrate consistent innovation in single-photon imaging techniques, with emphasis on underwater applications (2023-2024) and obscurant penetration (2022). Her work shows strong interdisciplinary connections between optical engineering, computational imaging, and environmental sensing. Key publications reveal growing impact in underwater LiDAR technology, with multiple high-citation papers on photon-efficient imaging systems. Dr. Maccarone actively supervises PhD students and has created significant research datasets. Her collaborations span international institutions, with notable contributions to sensor hardware development and computational imaging algorithms. Recent work shows increasing integration of machine learning techniques with traditional photon-counting approaches.

Matloob Khushiمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۷ 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.

Oya Celiktutanمشاهده پروفایل

دانشیار

• Multimodal Machine Learning
• Human-Robot Interaction
• Human Behavior Analysis
• +۵ مورد دیگر

Oya Celiktutan is a Reader (Associate Professor) at King’s College London in the Department of Engineering, leading the Social AI & Robotics (SAIR) Lab within the Centre for Robotics Research. She holds a BSc in Electronics Engineering from Uludag University, and an MSc and PhD in Electrical and Electronics Engineering from Bogazici University, Turkey. Her doctoral work included a visiting research period at the National Institute of Applied Sciences in Lyon, France. She has held postdoctoral positions at Queen Mary University London, the University of Cambridge, and Imperial College London before joining King’s College in 2018. Her research focuses on multimodal machine learning for autonomous agents, addressing challenges in human-robot interaction, social awareness, and navigation. Key areas include human behavior analysis, generative models, and continual learning. Her work is supported by EPSRC, The Royal Society, EU Horizon, and industry partners like Toyota and NVIDIA. Notable awards include the EPSRC New Investigator Award (2021) and a Best Paper Award at IEEE Ro-Man 2022. Dr. Celiktutan’s lab explores socially assistive robotics, extended reality systems, and ethical AI. Recent projects include the LISI initiative to enable robots to learn social interactions from human-human data and the CL-HRI project advancing continual learning in human-centric robotics. Her research has led to datasets like MHHRI and RICA, and collaborations with companies like SoftBank Robotics. She teaches modules on Sensing and Perception, and Sensors and Actuators, emphasizing practical applications in robotics and mechatronics. Current research interests span socially-aware navigation, algorithmic fairness, and multimodal data fusion for robotics in healthcare and urban environments.

• Information Theory
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Miguel Rodrigues is a Professor of Information Theory and Processing at University College London's Department of Electronic & Electrical Engineering. He leads the Information, Inference and Machine Learning Lab at UCL and serves as the founder and director of the master programme in Integrated Machine Learning Systems. Rodrigues is also the UCL Turing University Lead and a Turing Fellow with the Alan Turing Institute, the UK National Institute of Data Science and Artificial Intelligence. His academic background includes an undergraduate degree in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto, Portugal, and a PhD in Electronic and Electrical Engineering from University College London. He has held appointments at prestigious institutions worldwide including Cambridge University, Princeton University, Duke University, and the University of Porto. Dr. Rodrigues's research spans information theory, information processing, and machine learning. His work has attracted over £5 million in funding from competitive national and international funding bodies and resulted in more than 250 publications with over 8000 citations in leading journals and conferences, including top AI venues like NeurIPS, ICML, and ICLR. His recent publications demonstrate a strong focus on multimodal learning, machine learning security, climate modeling with satellite data, and applications of AI in healthcare and precision medicine. His work shows increasing interdisciplinary collaboration across fields from climate science to pharmaceutical engineering. IEEE Communications and Information Theory Societies Joint Paper Award 2011 Fellow of the Institute of Electronics and Electrical Engineers (IEEE) Prize for Merit from the University of Porto Prize Engenheiro Cristian Spratley Prize Engenheiro Antonio de Almeida Fellowships from the Portuguese Foundation for Science and Technology Fellowships from the Foundation Calouste Gulbenkian Dr. Rodrigues has served as Editor for IEEE BITS – The Information Theory Magazine and IEEE Transactions on Information Theory, among other editorial roles. He consults widely in machine learning and AI with government institutions, funding agencies, industry, and startups, and sits on committees responsible for AI standardization such as the BSI Art/1 working group. His leadership extends to directing research labs and educational programs focused on advancing machine learning systems. He leads the Information, Inference and Machine Learning Lab at UCL, which focuses on fundamental aspects of information theory and their applications to machine learning and data processing. The lab works on both theoretical foundations and practical implementations of learning systems.

Ruomeng Huangمشاهده پروفایل

دانشیار

• Neuromorphic Computing
• Machine Learning
• Nanophotonics
• +۳ مورد دیگر

Ruomeng Huang is an Associate Professor in the Sustainable Electronics Technologies group within the School of Electronics and Computer Science at the University of Southampton. He holds a PhD in nanoscale memristors (2015) and has been a faculty member since 2018, advancing to his current rank in 2023. His research focuses on neuromorphic computing using memristive materials, machine learning-driven nanophotonics, and energy harvesting devices. Huang has published over 100 peer-reviewed articles and leads six UKRI-funded projects, including the EPSRC-funded ADEPT initiative. Education: BSc Physics (2008), MEd (2009) in China; MSc Nanoelectronics & Nanotechnology (2010), PhD in nanoscale memristors (2015) at the University of Southampton. Research Interests : - Neuromorphic computing via novel memristor devices (SiC, mesoporous silica, chalcogenides) - Thermoelectric materials (SnSe, Bi₂Te₃) and AI-optimized generators - Deep learning for structural color design Teaching : Leads MSc programs in Electronic Engineering and Micro/Nanotechnology. Teaches undergraduate/graduate modules including semiconductor devices, nanoelectronics, and industrial studies. Grants & Projects : - EPSRC Doctoral Prize Fellowship (2015) - Co-Investigator on £6.33M EPSRC ADEPT grant (electrodeposition innovations) - PI on multiple thermoelectric and neuromorphic computing projects Students : Currently supervising 7 PhD students. Notable advisees include Aiden Graham, Jiale Zeng, and Dongkai Guo. Labs/Teams : Heads the Sustainable Electronics Technologies group, collaborating with interdisciplinary teams in nanomaterials and energy systems.

Tan Viet Tuyen Nguyenمشاهده پروفایل

مدرس

• Human-centered Artificial Intelligence
• Social Human-Robot Interaction
• Multimodal Perception and Interaction
• +۵ مورد دیگر

Dr. Tan Viet Tuyen Nguyen is a New Frontiers Fellow (Lecturer) in AI at the University of Southampton, specializing in Human-Centered Artificial Intelligence and Social Human-Robot Interaction. His research focuses on multimodal learning for robots to adapt their behavior to human social needs, with applications in healthcare, education, and service environments. Prior to this role, he was a Research Associate at King’s College London and a Research Assistant on the EU-funded CARESSES project, developing culturally-aware assistive robots for elderly support. Education: PhD in Information Science (Robotics) from Japan Advanced Institute of Science and Technology. He has organized conferences such as the IEEE RO-MAN 2022 special session on nonverbal communication and served as a reviewer for top-tier robotics and AI conferences. Research Interests include: Human-Robot Collaboration, Multimodal Perception, Generative AI for Social Interaction, and Context-Aware Robot Behavior Generation. His work has been recognized with awards including the Best Paper Award at ROMAN 2022 and the Prospective Research Award at ICServ 2023. Teaching Responsibilities include courses on Biologically Inspired Robotics, High-Level Programming, and MSc/Undergraduate project supervision. He currently oversees two PhD students and collaborates on projects like 'Exploring the impact of AI-driven writing of engagement in climate change' and 'Bridging Generations and Cultures through Generative AI.' Labs/Teams: Member of the Agents, Interaction and Complexity Centre and the Centre for Robotics Research at Southampton.

• 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.

He Wangمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۶ مورد دیگر

Dr. He Wang is an Associate Professor in the Department of Computer Science at University College London (UCL), affiliated with the Virtual Environment and Computer Graphics (VECG) group and the UCL Centre for Artificial Intelligence. He holds a Visiting Professorship at the University of Leeds and previously served as an Associate Professor and Lecturer there, as well as a Senior Research Associate at Disney Research Los Angeles. His research focuses on computer graphics, vision, and machine learning, with notable contributions to crowd simulation, generative models, and physics-informed neural networks. Dr. Wang earned his BEng from Zhejiang University and his PhD from the University of Edinburgh, followed by postdoctoral work at the University of Edinburgh's School of Informatics. He has been recognized as a Turing Fellow and serves as an Academic Advisor to the Commonwealth Scholarship Council and an Associate Editor of Computer Graphics Forum . His research spans cutting-edge topics including 3D reconstruction, adversarial attacks on motion recognition, and AI-driven groundwater modeling. He has supervised six PhD students to completion and actively engages in collaborative projects, consultancy, and grant evaluations. His lab welcomes students through dedicated recruitment channels.