جست‌وجوی استاد و پژوهشگر

• Explainable AI
• Counterfactual Explanations
• AI Robustness
• +۳ مورد دیگر

Francesco Leofante is an Imperial College Research Fellow at the Department of Computing within the Faculty of Engineering at Imperial College London. His research focuses on Explainable AI, particularly counterfactual explanations and their robustness. He is affiliated with the Centre for Explainable AI at Imperial College, supported by an Imperial College Research Fellowship. Research Interests : Leofante's work bridges theoretical foundations of AI with practical applications, emphasizing transparency and reliability in machine learning systems. His current projects investigate how counterfactual explanations can enhance model interpretability while maintaining robustness against adversarial attacks. Affiliations : Department of Computing, Faculty of Engineering Centre for Explainable AI, Imperial College London Grants & Awards : No specific grants or awards listed beyond his Imperial College Research Fellowship. Labs/Teams : Active contributor to the Centre for Explainable AI, collaborating on interdisciplinary projects involving computer science and ethics.

Matt Graysonمشاهده پروفایل

مدرس ارشد

• Computational Chemistry
• Machine Learning
• Molecular Modelling
• +۶ مورد دیگر

Dr. Matt Grayson is a Senior Lecturer in the Department of Chemistry at the University of Bath, where he leads the Grayson Group within the Institute of Sustainability and Climate Change. His research program focuses on integrating computational methods with experimental chemistry to address challenges in sustainable synthesis and drug design. Dr. Grayson received his academic training at the University of Cambridge, earning a Bachelor of Arts in Natural Sciences (2006-2009), followed by a Master of Natural Science in Chemistry (2009-2010), and completing his Doctor of Philosophy in Chemistry (2010-2014). He held prestigious postdoctoral positions including a Junior Research Fellowship at the University of Cambridge (2014-2018) and a Lindemann Trust Fellowship at UCLA (2015). His research centers on the innovative application of machine learning and molecular modeling to solve problems in synthetic chemistry, catalysis, and toxicology. The Grayson Group develops computational approaches to predict chemical reactivity, design catalysts, and understand reaction mechanisms without requiring extensive experimental data or computationally expensive quantum mechanical calculations. Their work bridges the gap between theoretical predictions and practical synthetic applications, with particular emphasis on sustainable chemistry approaches that align with UN Sustainable Development Goals. Analysis of Dr. Grayson's publication record reveals a clear trajectory toward increasingly sophisticated integration of machine learning techniques with computational chemistry. His recent work demonstrates expertise in developing data-efficient algorithms for reaction prediction, with particular focus on transition state modeling, catalytic reaction design, and applications in drug discovery and environmental toxicology. The research shows strong interdisciplinary connections between computational chemistry, artificial intelligence, and experimental organic synthesis. Dr. Grayson has received several prestigious awards recognizing his contributions to the field: Royal Society of Chemistry's Analytical Science Horizon Prize: Sir George Stokes Prize (2025) Royal Society of Chemistry's Hickinbottom Prize for Organic Chemistry (2023) MGMS Frank Blaney Award (2023) University of Bath Doctoral Recognition Award (2021) As an active supervisor, Dr. Grayson mentors numerous doctoral students and postdoctoral researchers, with several successful PhD completions to date. His research is supported by substantial funding from UK research councils including EPSRC, with multiple active projects totaling millions of pounds in research investment. The Grayson Group maintains strong industrial collaborations with pharmaceutical companies including AstraZeneca and academic partnerships across the UK and internationally. The Grayson Group operates as a vibrant interdisciplinary research environment that brings together computational chemists, machine learning specialists, and synthetic organic chemists to tackle complex problems at the interface of these fields. The group regularly hosts visiting researchers, organizes workshops to foster collaboration between experimental and computational chemists, and maintains state-of-the-art computing resources for molecular modeling and machine learning applications.

Kate Vredenburghمشاهده پروفایل

دانشیار

• Philosophy of Social Science
• Political Philosophy
• Philosophy of AI
• +۳ مورد دیگر

Kate Vredenburgh is an Associate Professor in the Department of Philosophy, Logic and Scientific Method at the London School of Economics and Political Science (LSE). Her research focuses on the philosophy of social science, political philosophy, and the philosophy of AI, with a particular emphasis on the ethical implications of technology in public policy and labor contexts. She currently holds a UKRI Future Leaders Fellowship (2024–2028) to investigate AI, worker autonomy, and the future of work. Education: Ph.D., Philosophy, Harvard University (2019) BPhil, Philosophy, University of Oxford (2011) B.A., Philosophy and English Literature, Gettysburg College (2009), summa cum laude Research Interests: Vredenburgh explores how normative commitments shape scientific and computational modeling, particularly in economics and computer science. She analyzes ethical challenges posed by AI in bureaucratic decision-making, labor dynamics, and public policy, advocating for transparent and equitable technological systems. Her work bridges theoretical philosophy with applied ethics in emerging technologies. Publications: Recent work includes analyses of revealed preferences in economics, AI governance frameworks for public institutions, and the ethical dimensions of AI-driven workplaces. Her articles appear in journals like Philosophy of Science , Inquiry , and The Canadian Journal of Philosophy . Awards: UKRI Future Leaders Fellowship Grant (2024–2028) Grants & Collaborations: Her current fellowship supports interdisciplinary research on AI and labor. She invites collaboration from scholars working on AI ethics, public policy, and workplace autonomy.

• Many-Objective Optimization
• Process Optimization
• Machine Learning Applications
• +۳ مورد دیگر

Kai Eivind Wu is a Researcher at the University of Sheffield’s School of Electrical and Electronic Engineering, specializing in many-objective optimization with applications in pharmaceutical engineering and polymer chemistry. His work focuses on advancing computational methods for process optimization, automation, and machine learning integration in complex systems. Research interests include developing novel decomposition strategies for evolutionary algorithms, improving diversity metrics in multi-objective optimization, and applying these techniques to real-world problems such as continuous pharmaceutical production and nanoparticle synthesis. Collaborations involve cross-disciplinary projects with cloud computing and automated laboratory systems. Publications highlight contributions to self-optimizing laboratory platforms, constraint-handled manufacturing processes, and visualization methods for Pareto front analysis. His work bridges theoretical optimization advancements with practical industrial applications. No scientific awards are explicitly listed. Advising and grant details are not provided in the text. Wu is affiliated with the Amy Johnson Building in Sheffield and contributes to research teams focused on automation and high-dimensional optimization challenges.

Joseph Butterfieldمشاهده پروفایل

مدرس ارشد

• Digital Manufacturing
• Automated Design Methods
• Through Life Engineering
• +۸ مورد دیگر

Joseph Butterfield is a Senior Lecturer in the School of Mechanical and Aerospace Engineering at Queen's University Belfast, specializing in Aerospace and Manufacturing Intelligent Autonomous Manufacturing Systems. His research focuses on digital manufacturing, automation, and cyber-physical systems, addressing challenges in supply chain resilience, through-life engineering, and fourth industrial revolution (I4.0) technologies. He supervises PhD students in areas like autonomous UAVs and rotational moulding. Teaching responsibilities include modules on Aircraft Design and Manufacturing Automation & Simulation for Mechanical, Aerospace, and Product Design Engineering students. He has developed courses on Digital Manufacturing and Finite Element Analysis. His work contributes to UN Sustainable Development Goals related to industry, innovation, and infrastructure. Dr. Butterfield leads and collaborates on projects such as UKRI-funded supply chain resilience modeling and Next-Generation Fuel-Cell Buses. He has received 17 awards, including Best Paper Awards at international conferences and environmental recognition for UAV innovations. His research integrates robotics, AI, and advanced simulation to enhance manufacturing processes and systems. Education: Not explicitly detailed in the text. Key Research Projects: 14 ongoing or completed, including supply chain resilience and UAV autonomy. Grants: Multiple UKRI and industry-funded initiatives. Lab/Team: Part of the Intelligent Autonomous Manufacturing Systems group.

Vicki Holmesمشاهده پروفایل

مدرس ارشد

• Technology Enhanced Learning
• Digital Education
• E-Assessment
• +۳ مورد دیگر

Vicki Holmes is the Head of Technology Enhanced Learning at the University of Reading, leading the strategic development of digital education and overseeing the TEL and Online Courses teams. She collaborates closely with Digital Technology Services to shape the university’s digital learning environment. MA in Education MSc in Computer Science MA in English Literature and Language Her research focuses on Technology Enhanced Learning , Digital Assessment , Media Streaming , and Interactive Learning Tools . Her work emphasizes improving pedagogy, student engagement, and scalable digital solutions. Recent publications highlight e-assessment systems, web-conferencing, and university-wide digital adoption. She has received prestigious awards, including the National Teaching Fellow (NTF), HEA Principal Fellow (PFHEA), University Teaching Fellow (UTF), and SEDA Fellow (FSEDA). Her career spans over 25 years in higher education digital initiatives, including professional development programs and institutional change projects.

Martine J. Baronsمشاهده پروفایل

استاد

• Decision Making Under Uncertainty
• Structured Expert Judgment
• Food Security
• +۳ مورد دیگر

Martine J. Barons is a Professor in Statistics and Director of the Applied Statistics & Risk Unit (AS&RU) at the University of Warwick. Her research focuses on decision-making under uncertainty, structured expert judgment, and their applications in food security, pollination, digital archives, and the energy sector. She holds roles in knowledge exchange and policy advisory, including membership in the Institute of Mathematics and its Applications (IMA) committees and the Academy for Mathematical Sciences advisory board. Her work integrates interdisciplinary collaborations with institutions like Melbourne University and Monash University. Key areas include developing decision support systems for complex societal challenges, such as pollinator conservation and climate risk communication. She advocates for public engagement through radio, TV, and educational outreach, aiming to demystify mathematics for broader audiences. Barons has secured grants from EPSRC, STFC, and industry partners. Her contributions include advancing Bayesian network methodologies and promoting ethical digital twin strategies. Awards include Chartered Mathematician status and Fellowship of the IMA.

Jiefei Weiمشاهده پروفایل

پژوهشگر ارشد

• Safe AI
• Explainable AI
• Computer Vision
• +۳ مورد دیگر

Jiefei Wei is a Postdoc Research Fellow in the Department of Statistics at the University of Warwick. They collaborate with Professor Giovanni Montana on a CRUK-funded project developing AI and computer vision techniques for breast cancer diagnosis and risk prediction. Their research focuses on Safe AI, Explainable AI, Computer Vision, Medical Imaging, and Machine Learning applications in healthcare. Research interests include adversarial machine learning robustness, medical image analysis, and AI-driven diagnostic systems. Their publications span topics like emotion recognition in video ads, adversarial attack defense, and autonomous vehicle decision-making. No scientific awards or advising roles are listed. Their work integrates statistical methodologies with cutting-edge AI, addressing both technical security challenges and clinical healthcare applications.

Bryan Lawrenceمشاهده پروفایل

استاد

• Climate Science
• Computational Science
• Data Management
• +۳ مورد دیگر

Professor Bryan Lawrence is a Professor of Weather and Climate Computing at the University of Reading's Department of Meteorology, with a joint position at the National Centre for Atmospheric Science (NCAS). He is Director of NCAS's Models and Data Division, overseeing CEDA (Centre for Environmental Data Analysis) and CMS (Computational Model Services). His research focuses on climate computing, data infrastructure, and high-performance computing for environmental sciences. Roles: Professor, NCAS Director (Models/Data), CEDA Lead Affiliations: University of Reading, NCAS, CEDA Research interests include petascale data storage, climate model performance, scientific data description, and Python applications in atmospheric science. He leads projects like JASMIN, ESIWACE, and IS-ENES3. Recent work emphasizes exascale computing challenges, tropical cyclone detection via deep learning, and climate data federation through ESGF. He advises students on topics like parallel data analysis and computational performance. Grants and collaborations include UK Excalibur, European HPC initiatives, and leadership in CMIP6 data infrastructure. His lab (CEDA/JASMIN) supports global climate data analysis and storage solutions.

Jat Singhمشاهده پروفایل

استاد پژوهشی

• Algorithmic Accountability
• AI Governance
• Data Privacy & Protection
• +۶ مورد دیگر

Dr. Jat Singh is a Research Professor at the University of Cambridge's Department of Computer Science and Technology (Computer Laboratory), leading the Compliant and Accountable Systems research group. His interdisciplinary work bridges computer science and law, focusing on technology's legal and regulatory compliance, governance of AI/ML systems, and ethical data practices. He co-chairs the Trust & Technology Initiative and is a Fellow of the Alan Turing Institute. Education: PhD in Computer Science (Distributed Systems & Security) from the University of Cambridge, with prior legal studies. Professional background includes roles in IT services, a medical IT startup, and consulting, emphasizing governance-intensive sectors like healthcare and judiciary. Research interests include accountability in AI, IoT compliance, data justice, and ethical algorithmic systems. Notable projects include RAInS (accountability in AI), legally-compliant IoT frameworks, and data governance in modern slavery contexts. His work explores transparency, fairness, and auditability in technology. Scientific Awards: EPSRC Fellowship and Alan Turing Institute Fellowship. Active in tech-policy advising, collaborating with governments, regulators, and civil society. Teaching: Leads the Technology, Law & Society module in the Advanced Computer Science MPhil. Supervises student projects on algorithmic accountability, security, and societal impacts of technology. Labs/Teams: Heads the Compliant & Accountable Systems Group and contributes to the Trust & Technology Initiative, advancing interdisciplinary research on technology's societal implications.

• Computer Architecture
• Machine Learning and Artificial Intelligence
• Embedded Systems
• +۴ مورد دیگر

Daniel Bates is a Senior Research Associate at the University of Cambridge's Department of Computer Science and Technology. His research focuses on Computer Architecture and Machine Learning, emphasizing co-optimization of hardware and software. Key projects include the Loki manycore processor (with 128 cores fabricated in 2018) and the Muntjac RISC-V multicore processor, designed for simplicity and extensibility. Bates explores energy-efficient machine learning optimizations like dynamic gating and network architecture search. He teaches courses including Part IA Algorithms, Part IB Computer Architecture, and Part II Advanced Computer Architecture. His supervision style prioritizes student feedback and clarity, offering tailored support and prompt communication. Bates has contributed to over a dozen peer-reviewed publications since 2013, addressing topics from neural network security to embedded system optimization. His work bridges theoretical research and practical hardware implementation, with a focus on open-source contributions like the Muntjac processor. He actively collaborates with industry through the University's research initiatives, emphasizing real-world applications of computer architecture advancements.

Claire Inglisمشاهده پروفایل

مدرس

• XR and Digital Enhancements for Education
• Simulation-based Learning
• AI and Neurodivergence Support
• +۳ مورد دیگر

Claire Inglis is a Principal Lecturer in Undergraduate Programmes at the Institute of Education, Arts and Society, University of Cumbria. She previously served as Simulation Development Lead at the Institute of Health and currently holds roles as Trustee at Rosehill Theatre, Chair of the Simulation Research Network, and Co-Chair of the Active Learning Network. Her qualifications include an MA Open, Fellow of Advance HE, and PGDip in Specialist Community Public Health Nursing. Her research focuses on XR technologies for education, simulation-based learning for neurodivergent individuals, and AI applications in inclusive pedagogy. She has pioneered immersive learning tools, gamified nursing education, and VR simulations for child protection training. Her recent work emphasizes mental health stigma reduction through dialogue simulations and stress-relief strategies in immersive environments. Claire has presented at conferences such as the National Future Facing Learning Conference and the Maudsley Mental Health Simulation. She founded the Immersive Technologies Community of Practice and Autistic Research Network North, advocating for inclusive educational practices. Her external roles reflect a commitment to cultural and educational accessibility through theatre and community engagement. She supervises Masters students exploring virtual placements in paramedic education. Her grants and projects include interdisciplinary collaborations on simulation design and XR integration across healthcare and arts disciplines.

Raheel Nawazمشاهده پروفایل

استاد

• Artificial Intelligence
• Digital Education
• Educational Data Science
• +۴ مورد دیگر

Professor Raheel Nawaz is the Pro-Vice Chancellor (Education and Research) at Staffordshire University, leading academic strategy across four strategic priorities: Next Generation Education, Experience, Environment, and Engagement. He previously served as Pro-Vice Chancellor (Digital Transformation), driving transformative digital initiatives recognized as sector-leading. His career spans leadership roles in diverse institutions, including Russell Group universities and private further education organizations. He is a top-10 global scholar in 'Applied AI' and 'Digital Education', with over £14M in grants and 200+ publications (h-index 47). He advises international educational programs and sits on boards like JISC and UVAC. Research interests focus on AI-driven education, work-based learning, and industry-academia collaboration. His National Teaching Fellowship (NTF) recognizes groundbreaking work on Degree Apprenticeship pedagogies, improving outcomes for underrepresented students. He has advised governments and policy bodies, including Pakistan’s Prime Minister’s Task Force on Science and Technology. Notable contributions include AI applications in Urdu language processing, healthcare diagnostics, and robotics for autism therapy. Awards: NTF, PFHEA, Whatuni Student Choice Awards 2023 Grants: DARPA, CERN, World Bank, UKRI, Innovate UK Leadership: Spearheaded digital transformation, advised three continents’ educational programs Labs/Teams: Led Staffordshire’s Digital Transformation research center and collaborates with global organizations like the National Centre for Artificial Intelligence (Pakistan).

• Artificial Intelligence
• Self-Organizing Systems
• Computational Sustainability
• +۷ مورد دیگر

Professor Jeremy Pitt is a Professor of Intelligent and Self-Organising Systems at Imperial College London, affiliated with the Department of Electrical and Electronic Engineering and the Faculty of Engineering. He leads the Intelligent Systems and Networks Group and the Computer-Supported Social Arrangements Laboratory (SALab). His research focuses on computational models of social processes, self-governance in socio-technical systems, and the ethical implications of technology. Pitt has published nearly 300 articles and secured over 30 grants, emphasizing interdisciplinary collaboration across computer science, political science, and philosophy. His work spans six key areas: computational sustainability, justice, self-governance, social construction, influence, and platform design. Notable contributions include formalizing Ostrom’s institutional design principles and Ober’s theories of classical democracy. As Editor-in-Chief of IEEE Technology and Society Magazine (2018–2023), he addressed societal impacts of technology, including editorials on 'Toxic Technology' and 'Futural Appropriation'. Pitt’s current research tackles re-empowerment architectures to address democratic backsliding and power asymmetries in the Digital Society. He collaborates with institutions like the Council of Europe and focuses on designing ethical AI, public interest technology, and sustainable socio-technical systems. Grants: Over 30 national/EU grants secured Labs: Intelligent Systems and Networks Group, SALab Affiliations: Artificial Intelligence Network, Centre for Cryptocurrency Research and Engineering Key Themes: Self-organisation, algorithmic governance, digital ethics, interdisciplinary policy

• Metacognition AI
• Reinforcement Learning
• Robotics
• +۳ مورد دیگر

An Nguyen An Nguyen is a Researcher in the Department of Computer Science at the University of Oxford. Their work focuses on advancing AI systems through metacognition principles, reinforcement learning, and robotics applications. Key research themes include human-AI collaboration in robotic teleoperation, decision-making frameworks, and spatiotemporal task coordination. Nguyen's recent studies explore confidence calibration mechanisms in joint human-AI systems and their implications for autonomous decision support. Research interests span: Metacognition AI architectures for self-aware systems Reinforcement learning in dynamic environments Robotic teleoperation interfaces Human-robot trust dynamics Publications from 2020-2025 reflect a progression from transportation modeling to cutting-edge AI-robotics integration. Work combines unsupervised learning techniques with real-world robotics applications, demonstrating expertise in both theoretical and applied computer science domains.