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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.

• Interactive Artificial Intelligence
• Statistical Disclosure Control
• Privacy Leakage from AI Models
• +7 more

Sadie Creese is Professor of Cybersecurity in the Department of Computer Science at the University of Oxford. She serves as Director of the Global Cyber Security Capacity Centre at the Oxford Martin School and Director of the Oxford Martin Programme on AI Threat Detection. Additionally, she is a Governing Body Fellow at Worcester College and Chair of Examiners for the MSc in Computer Science. Professor of Cybersecurity, Department of Computer Science, University of Oxford Director, Global Cyber Security Capacity Centre, Oxford Martin School Director, Oxford Martin Programme on AI Threat Detection Governing Body Fellow, Worcester College Chair of Examiners for MSc in Computer Science Sadie Creese holds a DPhil in Computer Science from the University of Oxford, an MSc in Computation, and a BSc (Hons) in Mathematics and Philosophy. Her academic background bridges theoretical computer science with practical applications in security. Her research spans multiple domains of cybersecurity including cyber situational awareness, visual analytics for cybersecurity, predicting organizational cyber-value-at-risk, agent-based simulations for malware propagation, threat modeling and detection, network defense, and cyber-resilience strategies. She also studies national cybersecurity capacity, working with countries and international organizations worldwide. Her interdisciplinary approach integrates insights from computer science, social sciences, and policy studies to address complex security challenges in an interconnected digital world. Analysis of her recent publications reveals a consistent focus on practical cybersecurity applications with strong emphasis on human factors in security systems. Her work bridges technical security mechanisms with organizational and human elements, particularly in areas like insider threat detection, risk communication, and security operations. The research demonstrates a trajectory toward more integrated security frameworks that consider both technical vulnerabilities and human behaviors within complex systems. Sadie Creese has been recognized as one of The 50 most influential women in cyber-security UK . Her work with the Global Cyber Security Capacity Centre has influenced national cybersecurity strategies worldwide, and she actively contributes to policy discussions through platforms like the World Economic Forum's AI Governance Alliance. The 50 most influential women in cyber-security UK Professor Creese has supervised numerous PhD and Master's students including Mary Bispham, Rodrigo Carvalho, Elizabeth Phillips, Marcel Stolz, and Meredydd Williams. Her research has been supported by significant grants including AXIS-sponsored projects on cyber-risk modeling, World Economic Forum collaborations on cybersecurity futures, and Lloyds Register Foundation funding for cyber security research related to the Industrial Internet of Things. She leads major interdisciplinary projects that bridge technical security research with policy implications. Principal Investigator on AXIS-sponsored project 'Analysing Cyber-Value-at-Risk, Residual Risk and models for Systemic Cyber-Risk' Collaboration with World Economic Forum's Shaping the Future of Cybersecurity Platform Co-Chair of Lloyds Register Foundation sponsored Foresight review of cyber security for the Industrial Internet of Things Professor Creese founded and directs the Global Cyber Security Capacity Centre (GCSCC) at the Oxford Martin School, which conducts research into national cybersecurity capacity. She was also the founding Director of Oxford's Cybersecurity Network (now CyberSecurity@Oxford), established in 2008. Her team includes senior researchers like Ioannis Agrafiotis, Louise Axon, and Michael Goldsmith, who collaborate on projects spanning cyber analytics, security protocols, and identity management. The GCSCC works with governments and international organizations to build cybersecurity capacity globally, while the Oxford Martin Programme on AI Threat Detection focuses on emerging security challenges posed by artificial intelligence technologies.

• Cybersecurity
• Cyber Risk Assessment
• Threat Modeling
• +7 more

Dr. Abbas A. Fairouz is an Assistant Professor in the Computer Engineering Department at Kuwait University. He holds a PhD from Texas A&M University (2019) and BS/MS degrees from Kuwait University (2006/2011). He previously served as a system engineer at Kuwait's Ministry of Electricity & Water (2006–2014) and as a part-time faculty member at PAAET's Higher Institute of Telecommunication and Navigation (2008–2010). Affiliations: Academic Visitor at University of Oxford (OSCAR Research Group) Research Focus: VLSI design, AI/ML processor architecture, secure computer systems, microarchitecture optimization, and FPGA-based acceleration. His current projects include hardware-based sorting networks, privacy-preserving instant messaging systems, and chiplet-based secure architectures. He has received the President of Kuwait Award (2012) and was a best paper nominee at GLSVLSI'17. His work spans hardware-software co-design for cryptographic accelerators, special function units (SFUs), and low-power NoC routers. Collaborations include the Oxford Secure Computer Architecture Research (OSCAR) group.

• VLSI Design
• AI/ML Processor Design
• Secure Computer Architecture
• +3 more

Daniel O'Keeffe is a Senior Lecturer in Computer Science at the Department of Computer Science, Royal Holloway University of London (RHUL). He is affiliated with the Centre for Programming Languages and Systems (CPLS) and the Systems and Software Security Lab (S3Lab). PhD from University of Cambridge Computer Laboratory Post-doctoral research at Imperial College London Industry experience in financial software engineering His research spans distributed systems, security, databases, and networking, with a focus on cloud computing security and blockchain security. Recent work explores performance-security trade-offs in proof-of-stake blockchains under the Revoke project. Key research projects include SecureCloud (cloud security for critical infrastructure), ITA-DSM (edge computing stream processing), SeReCa (secure hardware for cloud protection), and CloudSafetyNet (data leakage monitoring). Current projects receive funding from the Engineering and Physical Sciences Research Council (EPSRC) and the Ethereum Foundation. Scientific Awards : ERA Track - Best Paper Award at SANER 2024 Grants : REDONDA: EPSRC-funded project on Byzantine State-Machine Replication for blockchain (2024-2027) Consensus-layer mitigations for validator ransomware attacks: Ethereum Foundation grant (2022-2023) Labs & Groups : Centre for Programming Languages and Systems (CPLS) Systems and Software Security Lab (S3Lab) OPERA group (University of Cambridge) LSDS group (Imperial College London)

• distributed systems
• security
• databases
• +7 more

Dr. Xi Chen is a Senior Lecturer (Associate Professor) in Machine Learning and Data Science at the Department of Computer Science, University of Bath. He holds affiliations with the UKRI CDT in Accountable AI, Institute for Mathematical Innovation, and the Centre for Mathematics and Algorithms for Data (MAD). His research focuses on Bayesian inference, machine learning, statistical signal processing, and Monte Carlo methods. He has contributed to interdisciplinary applications such as AI for medical imaging and offshore engineering. Dr. Chen completed his Ph.D. in Bayesian inference and statistical signal processing at the University of Cambridge. His teaching includes courses on Artificial Intelligence, Machine Learning, and Bayesian Machine Learning. He is also an Associate Editor for the IET Signal Processing Journal and a Fellow of the UK Higher Education Academy. His research interests span AI applications in collective intelligence, computational methods for wave dynamics, and biomechanics. Recent work includes developing Gaussian Process models for offshore engineering and wearable sensor-based systems for analyzing human movement. His publications emphasize interdisciplinary challenges in AI ethics, model vulnerability, and data-driven decision-making. Dr. Chen leads the A Novel Contour-based Machine Learning Tool for Reliable Brain Tumour Resection project (EPSRC-funded) and collaborates on offshore wind energy research (SEASWALLOWS project). His grants include funding from the National Institute for Health Research for AI-based disease monitoring tools. He supervises students in the UKRI CDT in Accountable AI and contributes to the Bath Institute for the Augmented Human. His labs focus on Bayesian machine learning and AI for science, fostering collaborations across engineering, mathematics, and healthcare domains.

• Bayesian Inference
• Machine Learning
• Deep Learning
• +3 more

Danilo Francati is a Lecturer in Information Security at the Department of Information Security, Royal Holloway, University of London. His expertise focuses on cryptography, cybersecurity, and decentralized systems. He holds a PhD in Computer Science from Stevens Institute of Technology (2017–2021) and has held postdoctoral roles at George Mason University (2024) and Aarhus University (2021–2024). His research addresses critical challenges in secure storage systems, credential security, and AI ethics. Recent work includes advancements in blockchain scalability, breach detection mechanisms, and limitations of language model watermarking. He actively participates in major conferences, serving on program committees for Usenix Security and ACM CCS, and editorial roles in Communications in Cryptography . Key contributions span cryptographic protocols, privacy-preserving technologies, and post-quantum security. His activities include invited talks, peer reviews, and public outreach on cybersecurity trends.

• Cryptography
• Decentralized Storage Systems
• Cybersecurity
• +4 more

Abir Awad serves as Principal Academic in Cyber Security within Bournemouth University's Computing Department, bringing over 18 years of international research experience in cryptographic systems and cloud security. She obtained her PhD in Cryptography from Polytech' Nantes, University of Nantes, France. Dr. Awad develops chaos-based cryptographic systems for secure communications, cloud data protection, and network anomaly detection. Her innovations include chaotic neural network encryption algorithms, secure mobile cloud storage frameworks, and self-configuring network security systems. Recent work explores AI-driven network monitoring and multi-resource optimization in virtualized environments. As co-chair of the International Workshop on Trust in Cloud Computing, Dr. Awad advances industry-academia collaboration. Her research contributes to practical security solutions including DNA-based image encryption, integrity verification protocols, and risk assessment frameworks. Dr. Awad maintains active industry connections through LinkedIn and international research partnerships, enhancing her applied cybersecurity research program.

• Cybersecurity
• Cloud Security
• Cryptography
• +2 more

Pasquale Malacaria is a Professor of Computer Science at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. He serves as a faculty member in the Centre for Fundamental Computing and AI and is part of the Leadership Team for academics. His research interests span the theoretical foundations of computer science and their practical applications, with particular focus on information theory, logic, and game theory applied to understanding information transformation and leakage in computational processes. He has made significant contributions to program analysis and the use of model-checkers for detecting and quantifying information leakage in programs and side channels. Analysis of his recent publications reveals a strong trend toward cybersecurity decision support , with emphasis on quantitative methods for risk assessment, security investment optimization, and attack graph analysis. His work bridges theoretical information theory with practical security applications, particularly in areas like smart home security, healthcare cybersecurity, and industrial control systems. The research demonstrates a consistent evolution from foundational work on information flow to applied cybersecurity frameworks. Research funding includes significant grants from major organizations: "Unrestricted donation: Formal verification of privacy properties" from Meta Platforms Inc (£58,029, 2022-2025) "CHAI: Cyber Hygiene in AI enabled domestic life" from EPSRC (£329,505, 2020-2023) "Optimal Cybersecurity Investment" from EPSRC (£388,777, 2017-2021) Professor Malacaria teaches Logic in Computer Science at the postgraduate level, covering propositional logic, temporal logics, predicate logic, and program logics with practical applications using SAT solvers and model checkers. He also teaches Object-Oriented Programming at the undergraduate level, focusing on core concepts like classes, objects, methods, and inheritance in practical software development contexts.

• Information Theory
• Program Analysis
• Computer Security
• +5 more

Dr. Patrik Huber is a Research Fellow affiliated with the Department of Computer Science at the University of York and a Visiting Academic Researcher at the Centre for Vision, Speech and Signal Processing at the University of Surrey. His research focuses on Computer Vision, Computer Graphics, and Machine Learning, with specialization in statistical 3D face models and metrically accurate 3D avatars from 2D inputs. He earned his PhD in Computer Vision from the University of Surrey and MSc in Computer Science from the University of Basel. His research integrates classical computer vision techniques with deep learning approaches. Recent publication trends show strong emphasis on 3D face reconstruction, generative models for facial synthesis, neural surface reconstruction, and applications in biometric security. He founded 4dface Ltd, specializing in professional 3D face modeling solutions. As Principal Investigator, Dr. Huber leads projects on neural implicit representations and multimodal face generation. His work involves significant industry collaboration and technology transfer through his startup venture.

• Computer Vision
• Computer Graphics
• Machine Learning
• +2 more

Cangxiong Chen is a Researcher at the Institute for Mathematical Innovation (IMI), University of Bath. Prior to this, he worked in startups in Cambridge, UK, and Beijing, China, applying machine learning and statistical methods to consumer behavior modeling, pricing strategies, and recommendation systems. He holds a PhD in Number Theory from the University of Cambridge (2017), an MPhil in Mathematics from the University of Hong Kong (2011), and a Bachelor’s degree from the same institution. His research focuses on advancing the mathematics of machine learning, particularly in designing algorithms with privacy, fairness, robustness, and generalisability. He explores topics like differential privacy in image/video processing, training data leakage prevention, and collective intelligence systems. Education: • Doctor of Philosophy in Mathematics, University of Cambridge (2011–2016) • Master of Philosophy in Mathematics, University of Hong Kong (2009–2011) • Bachelor’s degree in Mathematics, University of Hong Kong His research interests span the theoretical foundations of machine learning, with a focus on privacy-preserving techniques such as differential privacy. He investigates robust algorithms for image and video processing, leveraging compressed sensing and dynamical systems. Additionally, he contributes to collective intelligence research, exploring how AI can enhance large-scale problem-solving and interdisciplinary collaboration. His work bridges pure mathematics (e.g., number theory) with applied domains like cybersecurity and social computing. In his recent projects, he has analyzed vulnerabilities in models like CLIP, studied leakage via gradients in neural networks, and developed strategies for AI-driven collective intelligence. These studies highlight his commitment to advancing both ML theory and its practical applications in diverse fields. Grants and Collaborations: Chen has contributed to seven research projects, including those funded by UKRI, EPSRC, and the Advanced Research and Inventions Agency. Notable roles include Researcher Co-Investigator (CoI) in projects like 'A New Analytical Framework for Dexterous Soft Robotic Manipulators' and 'AI for Collective Intelligence (AI4CI)’. He collaborates with interdisciplinary teams on topics ranging from soft robotics to policy strategy, emphasizing real-world impact through UK and international partnerships. Labs and Teams: Affiliated with the Institute for Mathematical Innovation at the University of Bath, he engages with collaborative teams across multiple disciplines, including artificial intelligence, data science, and mathematical modeling. His work often intersects with industry needs, as evidenced by his startup experience and ongoing projects in applied ML.

• Machine Learning
• Artificial Intelligence
• Data Science
• +3 more

Anastasia Borovykh is an Assistant Professor in the Department of Mathematics at Imperial College London, affiliated with the Faculty of Natural Sciences and the Imperial-X initiative. Her research focuses on developing computational models to enhance artificial intelligence systems, integrating insights from neuroscience, applied probability, signal processing, and mathematical modeling. Key application areas include healthcare (e.g., Parkinson’s disease biomarkers), neuroscience (brain connectivity analysis), and privacy preservation in neural networks. Her affiliations include Academic Women in Mathematics, the Applied Mathematics and Mathematical Physics group, and the Artificial Intelligence Network. She leads a research group emphasizing interdisciplinary approaches to AI, leveraging tools from statistical mechanics and advanced signal processing techniques. Recent publications highlight her work on diffusion models, privacy in machine learning, and reinforcement learning applications in chaotic systems control. Her research often bridges theoretical foundations with practical implementations in healthcare and cybersecurity domains. No scientific awards are explicitly listed in the provided information. Her advising and grants section remains unspecified, though her active research group suggests ongoing collaborative projects. She is part of Imperial-X, a platform for cross-disciplinary innovation.

• Artificial Intelligence
• Neuroscience
• Applied Mathematics
• +3 more