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Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

• Artificial Intelligence
• Machine Learning
• Explainability
• +9 more

Dr. Praneet Prakash is a Researcher in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge, working under Prof. Raymond Goldstein. His research focuses on interdisciplinary approaches combining microfluidics, microscopy, and theoretical physics to study biological systems. Key areas include microbial motility, active matter dynamics, and the interplay between physics and living systems. He utilizes experimental techniques such as microfluidics and advanced microscopy to investigate phenomena like bacterial swimming, nutrient exchange in microbial communities, and growth oscillations in filamentous fungi. His work bridges Soft Matter Physics, Statistical Mechanics, and Biophysics, with applications ranging from understanding microorganism behavior to developing biosensor technologies. Recent studies explore phototactic algae behavior, ciliary surface interactions in animalcules, and adaptive motility in marine microorganisms. Publications highlight contributions to microbial ecology, active matter systems, and biophysical modeling. While no formal awards are listed, his research has been published in high-impact journals like Journal of the Royal Society Interface and Physical Review Fluids . He is affiliated with the Biological Physics and Mechanics research group and maintains an active presence on academic platforms like Twitter and LinkedIn.

• Biophysics
• Soft Matter Physics
• Statistical Mechanics
• +3 more

Professor Nikos Tzavidis is a Professor of Statistical Methodology at the University of Southampton . He has held academic positions at institutions including University College London and the University of Manchester, and currently leads research initiatives integrating statistical methodology with geospatial and survey data. His work focuses on Small Area Estimation, official statistics, and poverty mapping, with applications across global development and public policy. Research interests: Small Area Estimation and Official Statistics Outlier Robust Inference Quantile and M-quantile Models Geospatial Data Analysis Poverty Mapping Machine Learning Applications in Statistics Recent research trends emphasize machine learning integration, geospatial data utilization, and advanced quantile modeling for poverty and demographic studies. His publications span journals like the Journal of Official Statistics and the Journal of the Royal Statistical Society . Scientific awards: ISI Fellow (2013) American Statistical Association (2004) Vice Chancellor's Teaching Award (2013) Johann von Spix Professor (2023) He supervises PhD students in social statistics, demography, and geography, and serves on editorial boards for journals including the Journal of Official Statistics . Externally, he has held leadership roles in the International Statistical Institute and contributed to United Nations and World Bank projects.

• Small Area Estimation
• Official Statistics
• Outlier Robust Inference
• +7 more

Professor Andrew Hudson-Smith is the Director of the Bartlett Centre for Advanced Spatial Analysis (CASA) at University College London (UCL), where he holds the title of Professor of Digital Urban Systems. He leads UCL's academic and applied research in smart cities, digital twins, and urban informatics. His roles include Editor-in-Chief of the Future Internet Journal and academic lead for the Smart Queen Elizabeth Park project. He contributes to strategic initiatives such as the Greater London Authority Smart London Board and EPSRC Digital Economy Programme Advisory Board. Education: PhD (2003), MSc (1996) from UCL; BSc in Geography from the University of Plymouth (1992). Research focuses on digital urban systems, IoT applications, sustainable urban planning, and cyberphysical systems. Notable projects include urban heat island monitoring, participatory planning platforms, and metaverse-driven urban design. His work bridges technology, art, and public engagement, exemplified by initiatives like Tales of Things and the Haggle-O-Tron. Recent publications emphasize digital twins, metaverse urbanism, and IoT integration in urban environments. Awards include a Fellowship of the Royal Society of Arts. His work addresses SDGs 3 (Good Health) and 11 (Sustainable Cities), with a focus on equitable smart city development and public space innovation.

• Digital Urban Systems
• Smart Cities
• Internet of Things (IoT)
• +7 more

Dr. Jonathan Bright is a Senior Research Fellow and Associate Professor at the Oxford Internet Institute, University of Oxford , specializing in computational approaches to the social and political sciences. His research explores how digital technologies reshape political participation and how new data sources enhance governmental decision-making. Research Interests: Focused on social media, political behavior, computational social science, and big data , Bright’s work addresses digital governance, algorithmic bureaucracy, and the dynamics of political fragmentation online. His empirical studies span open government data usage, crowdsourced geographical information, and the impact of neighborhood effects on data completeness. Scientific Contributions: Leading studies on algorithmic governance in UK local authorities (2020) Investigating geodemographic biases in crowdsourced platforms like OpenStreetMap (2018) Mapping political fragmentation in social media ecosystems (2018) Grants & Collaborations: Funded by UK taxpayers, ESRC, and Google, his work intersects with projects on vaccine misinformation, hate speech monitoring, and digital economies. Formerly held positions as Research Fellow and Research Associate at OII from 2013 to present.

• Computational Social Science
• Political Participation
• Big Data
• +3 more

Professor Emily So serves as Deputy Head of the School of Arts and Humanities at the University of Cambridge and directs the Cambridge University Centre for Risk in the Built Environment (CURBE). A chartered civil engineer with extensive field experience, she holds leadership roles in the Open-Oxford-Cambridge AHRC Doctoral Training Partnership and chairs the Faculty EDI Committee. Her research focuses on urban risk and resilience , particularly in earthquake-prone regions. Combining structural engineering with epidemiological approaches, she develops innovative casualty estimation models and engages directly with affected communities worldwide. Her work spans seismic safety, disaster epidemiology, and remote sensing applications for rapid damage assessment. Professor So's publication trends reveal strong emphasis on machine learning for disaster risk modeling , with recent work featuring graph neural networks, deep clustering for urban morphology, and LSTM-based population forecasting. Her research bridges engineering, social sciences, and data science to address resilience in developing nations. 2010 Shah Family Innovation Prize (Earthquake Engineering Research Institute) Fellow of the Institution of Civil Engineers (FICE) Scientific Advisory Group for Emergencies (SAGE) member advising UK government As Director of CURBE, she leads interdisciplinary collaborations with EEFIT, Global Earthquake Model (GEM), World Bank, and USGS. Her field investigations following major earthquakes inform practical solutions for vulnerable communities, notably contributing to the 2017 World Building of the Year design in China. Current work includes sabbatical research for 2025-2026 focused on decolonizing architectural approaches to disaster resilience. Professor So maintains active roles in professional organizations and international disaster response frameworks, with her CURBE team developing methodologies now implemented globally for seismic safety improvements.

• Disaster Risk Reduction
• Earthquake Engineering
• Urban Resilience
• +5 more

Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.

• Probabilistic Machine Learning
• Bayesian Statistics
• Digital Twins
• +5 more

Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.

• Probabilistic Machine Learning
• Environmental Prediction
• Spatio-temporal Modelling
• +4 more

Dr Olly Bartlett is a Senior Lecturer in Remote Sensing and Geography at the University of Hertfordshire , affiliated with the Department of Psychology, Sport and Geography within the School of Life and Medical Sciences. His work focuses on planetary-scale remote sensing analysis of the cryosphere, with field campaigns in Greenland, Nepal, Svalbard, and the Peruvian Andes. Education: PhD in Physical Geography (University of Exeter, 2020); MSc in Polar and Alpine Change (University of Sheffield, 2016); BSc in Physical Geography (University of Sheffield, 2015) Dr Bartlett specializes in UAV-based remote sensing and Google Earth Engine , holding a UK GVC for UAV operations. His research tracks environmental changes in polar and alpine regions, including quantifying Antarctic Peninsula greening (1986–2021) and glacier dynamics in the Himalayas and Canadian Arctic. Recent publications highlight his methodological innovations in radar surveying and supraglacial hummock morphology studies. His work bridges glaciology and geospatial analysis , focusing on climate-induced glacial retreat impacts. He supervises PhD student Lottie Pearson on pan-Arctic permafrost thaw projects and collaborates internationally on cryospheric studies.

• Remote Sensing
• Geographic Information Systems (GIS)
• Polar and Alpine Environmental Change
• +3 more

Laura Vaughan is Professor of Urban Form and Society and Director of the Space Syntax Lab at The Bartlett School of Architecture, University College London. Her research examines the relationship between urban form and society using space syntax methodologies, addressing critical urban challenges including spatial inequalities, health disparities, poverty, housing, and socioeconomic vitality. She maintains active policy engagement through memberships in the West Yorkshire Scientific Advisory Group and as Chair of the International Space Syntax Executive Committee. Education includes: Doctor of Philosophy, University of London (1995-1999) Master of Science, University of London (1992-1994) Bachelor of Design, Bezalel Academy of Arts and Design (1985-1989) Her research integrates space syntax methodology with urban geography and architecture to analyze how spatial configurations influence social patterns. Key interests include the historical evolution of urban segregation mechanisms, morphological predictors of community vitality, and geospatial determinants of public health outcomes. She maintains cross-disciplinary collaborations spanning public health, history, anthropology, and urban planning. Publication trends show consistent focus on urban morphology's societal impacts, with recent work emphasizing longitudinal health studies, historical cartography, and high street resilience. Methodological innovations include developing individual-level environmental exposure metrics and integrating geospatial analysis with cohort studies. Research consistently demonstrates how spatial configurations mediate social processes across temporal scales. Scientific recognition includes: Fellow of the Royal Geographical Society (elected 2005) Fellow of the Royal Historical Society (elected 2015) As Director of the Space Syntax Lab, she leads research on socio-spatial systems and advises PhD students. She teaches the 'Urban Form and Society' module in the MSc/MRes Space Syntax program, exploring urban design's relationship with crime, health, and inequality. Her pedagogical approach emphasizes integrating research with design practice and historical analysis.

• Urban Form
• Space Syntax
• Urban Geography
• +5 more

David Steinsaltz is an Associate Professor of Statistics at the University of Oxford, affiliated with Worcester College. His research focuses on stochastic processes, biodemography, survival analysis, and Bayesian methods, with applications to aging, mortality, and population dynamics. He holds a PhD in probability theory from Harvard University, followed by postdoctoral work at UC Berkeley. His work bridges theoretical probability and applied statistics, addressing questions in demography, ecology, and epidemiology. Education: PhD in Mathematics (Probability Theory), Harvard University (1996); Postdoctoral Research, UC Berkeley (Departments of Demography and Statistics). Research interests include stochastic flows, Markov processes, and statistical methods for longitudinal data. He contributes to interdisciplinary projects, such as earthquake impact modeling and vaccine efficacy analysis. His collaborations span fields like biostatistics, ecology, and machine learning. He advises students on topics including survival analysis and demographic modeling.

• Stochastic processes
• Biodemography
• Survival analysis
• +4 more

Anil Madhavapeddy serves as Professor of Planetary Computing at the University of Cambridge's Department of Computer Science and Technology and directs the Cambridge Centre for Carbon Credits (4C). A Fellow of Pembroke College, he integrates systems research with environmental conservation through the Computer Laboratory's Environment and Energy Group. His career spans industry leadership (NetApp, Citrix, Intel), academic appointments (Cambridge, Imperial, UCLA), and entrepreneurial ventures (XenSource, Unikernel Systems, Docker). Madhavapeddy earned his PhD at Cambridge's Computer Laboratory in 2006. His research bridges computational systems and planetary-scale environmental challenges, with deep expertise in open-source development (OCaml, Xen, Docker, OpenBSD) and technology strategy advising for organizations including Zededa, Tezos Foundation, and Tarides. His work centers on environmental computing and climate informatics, leveraging distributed systems and functional programming to develop sensing infrastructure for conservation. Recent projects focus on carbon credit systems, AI-driven biodiversity monitoring, and sustainable computing architectures that minimize ecological footprints while maximizing analytical capability. Analysis of his 2025 publications reveals a concentrated effort on AI-integrated conservation tools, privacy-preserving carbon accounting, and energy-efficient computing. Key themes include spatial networking for ecological data, LLM-enhanced evidence retrieval in conservation science, and novel metrics for extinction risk assessment—demonstrating computational innovation applied to urgent planetary boundaries. No scientific awards were documented in the source material. Madhavapeddy advises multiple technology firms on strategic development while leading the Cambridge Centre for Carbon Credits, though specific grant funding details remain unreported. He actively contributes to the Environment and Energy Group at Cambridge's Computer Laboratory and directs the interdisciplinary Cambridge Centre for Carbon Credits (4C). His open-source leadership spans critical infrastructure projects including OCaml, Xen, and Docker, fostering collaborative development communities that underpin modern cloud and container technologies.

• Environmental Computing
• Climate Change Informatics
• Distributed Systems
• +5 more

Dr Duncan Smith is an Associate Professor in GIS and Visualisation at the Centre for Advanced Spatial Analysis (CASA) at University College London (UCL). He serves as Programme Co-Director for the MSc Urban Spatial Science, focusing on digital visualisation and Geographical Information Systems (GIS) education. His research emphasizes urban sustainability, transport accessibility, and interactive urban visualisation, with a particular interest in global mega-cities like London, Singapore, and São Paulo. Dr Smith holds a PhD from UCL (2011), an MSc from the University of Edinburgh (2005), and a BA (2003). His recent projects include leading the ESRC-funded Driving Urban Transitions project (2024) exploring sustainable travel in small cities and outer metropolitan areas. Previously, he was Co-Investigator on the SIMETRI project (NSFC JPI Urban Europe) analyzing urban sustainability in London, the Randstad, and China's Greater Bay Area. His research interests span urban visualisation techniques, transport equity analysis, and polycentric urban development. Key areas include GIS-driven urban modelling, accessibility metrics, and the intersection of housing policy with sustainable urban form. His interactive visualisations, available on citygeographics.org , showcase innovative approaches to spatial data presentation. Dr Smith's work consistently addresses global sustainability challenges through geospatial methodologies. Recent studies highlight inequalities in transport accessibility and the spatial implications of urban policies. Upcoming projects will further investigate small-city mobility dynamics using multi-national collaborations with Amsterdam and Lisbon researchers.

• Urban Sustainability
• Transport Accessibility Analysis
• Urban Visualization
• +7 more

Dr. Pavlos Tafidis is a Lecturer in Transport (Systems) Engineering at the School of Engineering, University of Edinburgh. His work integrates interdisciplinary approaches to advance transport planning and engineering, with a focus on smart and sustainable mobility solutions. PhD in Transport Engineering, Hasselt University (2022) M.Sc in Transport Planning, Aristotle University of Thessaloniki (2015) M.Eng in Transportation, Aristotle University of Thessaloniki (2013) He leads projects like "BikeHood" (Science Foundation of Ireland), developing Ireland’s first cycling neighborhood, and contributes to initiatives such as "REALLOCATE" (Horizon 2020) and "CISMOB" (Interreg Europe). His research emphasizes accessible mobility solutions, equity in transport infrastructure, and urban livability. Recent publications analyze cyclist crash hotspots using machine learning, electric bike route preferences via GPS data, and traffic-emission correlations in Dublin. His expertise spans digital twins, virtual reality, and geospatial analysis. Dr. Tafidis teaches courses including Transport Engineering 3, Transport and Society, and Multi-Scale Energy Demand, affiliated with the School of Engineering and Edinburgh Future Institute.

• Transport Planning
• Smart Mobility
• Sustainable Transportation
• +7 more

Professor Nicole Metje is a Professor of Infrastructure Monitoring at the Department of Civil Engineering, University of Birmingham, and Director of the National Buried Infrastructure Facility. She also serves as Co-director of the Institute of Quantum Technology. Her research focuses on quantum sensing, geotechnical engineering, and infrastructure monitoring, with a particular emphasis on buried infrastructure detection and sustainable urban development. Education: PhD in Civil Engineering (University of Birmingham, 2001); Dipl.-Ing. in Civil Engineering (Hannover University, Germany, 1998). Key roles and affiliations include membership in EPSRC Infrastructure Strategic Advisory Team, Chartered Institution of Civil Engineering Surveyors, and international advisory boards for Hong Kong Polytechnic University. She has authored/co-authored over 50 journal papers and a textbook on tunnel construction. Research areas include quantum sensor applications for buried asset detection, geophysical soil properties, and sensor development for infrastructure monitoring. She leads projects such as UKCRIC National Buried Infrastructure Facility and Quantum Technology Hub in Sensors and Metrology. Awards and fellowships include FICE, FCInstCES, CEng, FHEA, and multiple committee memberships in national/international bodies. Teaching includes MSc courses on underground construction and geotechnical engineering. She supervises doctoral research on quantum sensors, soil-structure interactions, and sustainable infrastructure.

• Quantum Sensing
• Geotechnical Engineering
• Infrastructure Monitoring
• +4 more