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Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.

• Social Science Methodology
• Applied Social Data Science
• High-dimensional Econometrics
• +8 more

Dr. Johan P Larsson serves as a Lecturer in Economics and Public Policy within the Department of Land Economy at the University of Cambridge, concurrently holding a Fellowship and Director of Studies position in Land Economy. His academic profile bridges theoretical and empirical research in spatial economic phenomena, with emphasis on policy-relevant investigations of regional competitiveness and urban dynamics. His educational foundation includes: PhD from Jönköping University Dr. Larsson's research program centers on economic geography and regional science, specializing in urban economics and entrepreneurship. He examines how historical industrial structures, neighborhood effects, and policy interventions shape economic integration and innovation ecosystems. Methodologically, he combines quantitative analysis of large-scale datasets with natural experiments (e.g., pandemic-induced remote work) to isolate causal mechanisms in spatial economic processes. His recent publication trajectory (2021-2025) reveals consistent focus on three interconnected domains: evaluation of place-based policies (e.g., City Deals), analysis of the entrepreneurial state's role in innovation, and investigation of immigrant integration dynamics. These works predominantly utilize Scandinavian and English case studies while generating globally applicable theoretical insights, demonstrating methodological sophistication through spatial econometrics and policy counterfactuals. Recognition for his scholarly contributions includes: The JIBS Student Association’s Pedagogical Prize (2017) The Martin Beckmann Award from Regional Science International (2015) Jan Wallander scholar designation (2015) His teaching portfolio spans Microeconomics, Macroeconomics, Urban economics, and Applied statistics, reflecting his interdisciplinary expertise. While doctoral advisees aren't explicitly listed, his Director of Studies role indicates active undergraduate supervision within Cambridge's collegiate system. Current research appears focused on post-pandemic labor geography and mission-oriented innovation policy evaluation, though dedicated laboratories or formal research teams aren't mentioned in available materials.

• Economic geography
• Regional science
• Urban economics
• +1 more

Cong Ling is a Professor of Information Theory and Cryptography at Imperial College London's Department of Electrical and Electronic Engineering, within the Faculty of Engineering. His research focuses on lattice theory and its applications in coding, cryptography, quantum information, and number theory. Key affiliations include the Academic Centre of Excellence in Cyber Security Research and the Engineering Secure Software Systems group. Education details are not explicitly provided in the text, but his professional experience indicates advanced qualifications in electrical engineering and mathematics. Research interests span lattice-based cryptography, post-quantum security, algebraic coding theory, and quantum-resistant algorithms. His work bridges information theory and number theory, with contributions to MIMO systems, secure communication protocols, and cryptographic protocol design. Recent publications emphasize lattice reduction techniques, quantum algorithms for the shortest vector problem, and advancements in polar codes. Notable trends include exploration of non-commutative algebras for cryptography, Gaussian sampling optimizations, and hybrid quantum-classical approaches to hard integer problems. Over 50+ articles published since 2018 reflect his leadership in lattice-based research and quantum-safe technologies. Awards: None explicitly listed in the text. Grants/Advising: No specific grants or student advisees mentioned; focus remains on collaborative research outputs. Labs/Teams: Associated with Imperial's Cyber Security Research groups and quantum engineering initiatives.

• Information Theory
• Cryptography
• Lattice Theory
• +5 more

Jo Wood is Professor of Visual Analytics in the Department of Computer Science at City, University of London, where she has been employed since January 14, 2000. Her work bridges computer science, geographic information science, and human-computer interaction, focusing on innovative methods for visualizing complex spatial and behavioral data. Her research interests center on visual analytics , information visualization , and geovisualization , with applications in transportation, public health, crisis response, and citizen science. She investigates how interactive visual interfaces can support exploratory data analysis, decision-making, and storytelling, particularly through small multiples, faceted views, and sketch-based rendering techniques. The trends in her recent publications reflect a consistent focus on user-centered design , spatial data abstraction , and interactive exploration of multivariate datasets. Her work often integrates real-world behavioral data such as GPS tracks, cycling patterns, and crowd-sourced information to build meaningful visual narratives and support analytical reasoning. Throughout her career, Jo Wood has contributed significantly to the advancement of visual analytics through high-impact publications in top-tier venues such as IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum. Her collaborations with researchers like Jason Dykes and Aidan Slingsby highlight her role in a vibrant research community. She has supervised numerous research projects and mentored students in visualization and geospatial analytics, though specific names are not listed in the provided text. Her work has been supported by various research grants, particularly in domains involving urban mobility, energy modeling, and crisis informatics, though grant details are not specified here. Jo Wood has also contributed to the design of visual analytics systems for applications including disease spread modeling, bicycle-hire scheme monitoring, and persuasive technology for health and leisure, demonstrating a strong commitment to impactful, interdisciplinary research.

• Visual Analytics
• Information Visualization
• Geographic Information Science
• +7 more

Professor Gary Gibbons is a distinguished academic at the University of Cambridge, holding the position of Professor of Theoretical Physics within the Department of Applied Mathematics and Theoretical Physics (DAMTP), part of the Faculty of Mathematics. His research focuses on general relativity, quantum gravity, cosmology, and black hole physics. He is a core member of the Relativity and Gravitation Group, known for contributions to gravitational wave theory, black hole thermodynamics, and geometric methods in physics. His work intersects with areas such as the memory effect of gravitational waves, Carroll symmetry, and the mathematical structure of spacetime. Prof. Gibbons collaborates internationally, publishing extensively in top journals like Physical Review D and Classical and Quantum Gravity . His research also extends to applied mathematics, including studies on abrasion processes and shape evolution in geosciences. He maintains an active role in theoretical physics, advising graduate students and contributing to the academic community through lectures and seminars.

• Theoretical Physics
• General Relativity
• Cosmology
• +3 more

Alan Grafen is an Honorary Fellow at Jesus College, University of Oxford, and holds Tutorial Fellow roles at St John’s College in Quantitative Biology. His primary affiliation is the University of Oxford’s Faculty of Biology and Medicine. He is a leading evolutionary biologist specializing in mathematical and logical models of evolutionary processes. Research Interests: Grafen’s work focuses on evolutionary theory, particularly the formalization of Darwinism, inclusive fitness, signal evolution, and sexual/kin selection. He leads the Formal Darwinism Project to mathematically formalize natural selection’s role in evolution. His models include the mathematical underpinning of Zahavi’s handicap principle and advancements in reproductive value theory. Key Contributions: Published influential works like Modern Statistics for the Life Sciences (2002) and co-authored analyses of Richard Dawkins’ evolutionary ideas. His recent articles (2018–2024) explore natural vs. sexual selection distinctions, kin recognition stability, and extensions to Fisher’s fundamental theorem. Awards and Grants: Not explicitly listed, but his work has shaped evolutionary theory. His research spans theoretical models in Nature -level journals and interdisciplinary collaborations with statisticians and biologists. Labs/Teams: Directs the Formal Darwinism Project and collaborates with evolutionary theorists globally. His work bridges mathematics and empirical biology, influencing both academic and popular science discourse.

• Evolutionary Biology
• Mathematical Biology
• Theoretical Evolutionary Theory
• +4 more

Ran Spiegler is a Professor of Economics at both Tel Aviv University and University College London (UCL). He holds a PhD in Economics from Tel Aviv University (1999). His research focuses on economic theory, behavioral economics, and bounded rationality, with notable contributions to understanding decision-making under flawed causal reasoning, narrative-driven political and economic dynamics, and market behaviors influenced by limited consumer rationality. Spiegler has held roles including Member at the Institute for Advanced Study (Princeton, 2000-2001) and Prize Research Fellow at Nuffield College, Oxford (1999-2000). Education: PhD in Economics, Tel Aviv University (1999) Affiliations: Professorships at Tel Aviv University (2009–present) and UCL (2006–present) Research Interests: Spiegler’s work explores how bounded rationality shapes economic outcomes, including consumer decision-making, market competition, and the role of narratives in political mobilization. He has authored influential books like Bounded Rationality and Industrial Organization (2011) and The Curious Culture of Economic Theory (2024), which critique and expand economic theory’s methodologies. Recent Articles: His recent work examines topics such as false narratives in politics, monopolistic data practices, and competitive markets with imperfectly discerning consumers. These studies highlight interdisciplinary applications of economic theory to modern challenges like algorithmic transparency and platform economics. Awards: Prize Research Fellow, Nuffield College, Oxford (1999–2000) Grants & Labs: While specific grants are not detailed, his research is funded through institutional affiliations. He collaborates widely, with notable co-authors like Kfir Eliaz and Yair Antler.

• Economic Theory
• Behavioral Economics
• Political Economy
• +3 more

Dr. Eleanor Power is an Associate Professor in the Department of Methodology at the London School of Economics and Political Science (LSE). Her research focuses on the interplay between belief, practice, identity, and social relationships, with a particular emphasis on how reputational dynamics shape social inequality and cooperation. She combines ethnographic methods with social network analysis to study these phenomena in South India and cross-culturally. Education: Eleanor holds a PhD in Anthropology from Stanford University (2015). Prior to joining LSE in 2017, she was an Omidyar Postdoctoral Fellow at the Santa Fe Institute. She is fluent in English and Tamil. Research Interests: Eleanor examines signaling theory, religious practice, and the micro-dynamics of social inequality, including the 'reputational poverty trap.' Her current projects include co-directing the ENDOW project on social and economic inequality and leading the Rep2SI project on reputational dynamics. Her work bridges anthropology, sociology, and computational social science. Publications: Her recent work explores latent network models, cooperative behavior in South Indian communities, and the role of reputation in human social networks. She also collaborates on interdisciplinary projects, such as analyzing reproductive inequality across species and the impact of art workshops on prisoners' well-being. Awards: While no specific awards are listed, her research has been highlighted in LSE's 30th Anniversary celebrations for its interdisciplinary innovation and collaborative nature. Grants and Labs: Eleanor leads the Rep2SI project and co-directs the ENDOW project, indicating active grant-funded research. She is affiliated with LSE's Department of Methodology and has collaborated with institutions like the Santa Fe Institute.

• Social Network Analysis
• Ethnography
• Religion and Society
• +5 more

Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.

• Machine Learning
• Artificial Intelligence
• Bayesian Non-parametric Methods
• +6 more

Professor Tony Shardlow is affiliated with the Department of Mathematical Sciences at the University of Bath , UK. His research spans Stochastic Differential Equations , Bayesian Inverse Problems , Statistical Shape Modelling , and Numerical Analysis , with applications in data science, medical imaging, and computational physics. Labs/Teams : IMI (Institute for Mathematical Innovation), Prob-L@b (Probability Laboratory at Bath), SAMBa (EPSRC Centre for Doctoral Training in Statistical Applied Mathematics). Recent Research Trends : Focus on geometric shape analysis using flow fields, stochastic PDEs for particle dynamics, and Bayesian inference techniques in industrial and medical contexts. Collaborative work bridges computational mathematics with applications in hip dysplasia assessment and pesticide delivery systems. Advising : Supervised Fengpei Wang's PhD thesis on dimension reduction and Sinkhorn algorithms. Collaborates with researchers like N. D. F. Campbell and C. Poon. Teaching : Offers MA30170 - Numerical Solution of Elliptic PDEs.

• Stochastic Differential Equations
• Bayesian Inverse Problems
• Statistical Shape Modelling
• +3 more

Julia Camps is a postdoctoral research associate at the University of Oxford, Department of Computer Science. Her work bridges Computational Biology and Health Informatics, focusing on cardiac digital twin development for precision medicine applications. She specializes in combining data-driven and mechanistic approaches for in silico clinical trials, particularly through Purkinje network modeling and ECG-based calibration. Education: Informatics Engineer (2014) and Master's in Artificial Intelligence (2015-2017) from Universitat Politècnica de Catalunya PhD in Computer Science (2017-2021) at Oxford, completed within the Computational Cardiovascular Science research group under Prof Blanca Rodriguez Current role: postdoc in Prof Rodriguez's group since 2021, focusing on post-myocardial infarction disease progression Software development: open-source cardiac digital twin tools available on GitHub Her research interests center on creating patient-specific cardiac digital twins using multimodal clinical data. This work enables virtual therapy evaluation and in silico clinical trials through: Integration of statistical inference and machine learning techniques Development of Purkinje network models from clinical ECG data Electrophysiological and repolarization sequence modeling Gait detection algorithms for Parkinson's disease applications Recent publications (2024-2025) demonstrate trends in: GPU-accelerated cardiac electrophysiology simulations (MonoAlg3D) Topology-informed ECG electrode localization Sex-specific electromechanical cardiac modeling Multi-modal characterisation of diabetic cardiac deterioration Pro-arrhythmic risk assessment for stem cell therapies

• Computational Biology
• Health Informatics
• Machine Learning
• +3 more

Professor Washington Yotto Ochieng serves as Head of the Department of Civil and Environmental Engineering and Chair Professor in Positioning and Navigation Systems at Imperial College London. He directs the Centre for Active Resilience and Security (CARS) and maintains key affiliations with the Centre for Systems Engineering and Innovation, Centre for Transport Engineering and Modelling, Institute for Molecular Science and Engineering, and Space Lab. His extensive advisory roles include the Science Museum Group Board of Trustees, Royal Institute of Navigation Presidency, and Royal Academy of Engineering Africa Steering Committee. His educational background includes a BSc (First Class) in Engineering from the University of Nairobi and MSc (Distinction) and PhD in Civil Engineering from the University of Nottingham. He received an honorary DSc from Technical University of Kenya in 2023. Ochieng's research pioneers critical infrastructure resilience, user-centric mobility, and positioning/navigation/timing (PNT) systems. He has designed satellite navigation systems (including Europe's EGNOS and GALILEO) for multi-domain applications and advanced Air Traffic Management and Intelligent Transport Systems. His work integrates geomatics, transportation engineering, and sustainable mobility to solve global urban infrastructure challenges, with recent emphasis on decarbonization and AI-driven solutions. His 2024-2025 publications reveal strong trends in sustainable transportation decarbonization, AI-optimized traffic management, and resilient urban positioning systems. Research focuses on hydrogen fuel cell trains, carbon-efficient aviation, and deep reinforcement learning applications for emission reduction, demonstrating interdisciplinary integration of engineering, environmental science, and artificial intelligence to address climate challenges. Fellow of the Royal Academy of Engineering (2013) Harold Spencer-Jones Gold Medal from Royal Institute of Navigation (2019) Doctor of Science (honoris causa) from Technical University of Kenya (2023) Elder of the Order of the Burning Spear (EBS) from Kenya (2023) Commander of the Order of the British Empire (CBE) (2024) Ochieng provides strategic guidance to UK Government bodies (Government Office for Science, Department for Transport, FCDO), European Parliament, and European Court of Auditors. His advisory work shaped the Blackett Review on Satellite-derived Time/Position, UK Space Strategy, and Future of Mobility report. He chairs the Science Museum London Advisory Board and leads FCDO's Sustainable Urban Economic Development program in Africa, with significant grant influence through UK National Physical Laboratory and Department for International Development. He directs the Centre for Active Resilience and Security (CARS) and leads Space Lab initiatives, focusing on mission-critical PNT systems and infrastructure resilience. His teams collaborate with international consortia including RTCM Special Committee 134 and US Institute of Navigation, developing next-generation navigation solutions for safety-critical applications across transport, aviation, and urban environments.

• Positioning and Navigation Systems
• Transportation Engineering
• Intelligent Transport Systems
• +6 more

Dr. Shirley Coleman is a distinguished Professor at Newcastle University Business School, specializing in the application of statistical methods to business and industrial problems. With over two decades of academic contributions, she has established herself as a leading expert in statistics, data science, and quality management within industrial contexts. Her research interests span several interconnected domains: Statistics, Data Science, Business Analytics, Quality Management, Six Sigma methodologies, Kansei Engineering (which integrates emotional design with product development), Industrial Statistics, Design of Experiments, Predictive Maintenance, and Customer Lifetime Value analysis. Coleman's work consistently bridges theoretical statistical concepts with practical business applications across diverse sectors including healthcare, manufacturing, facilities management, and digital marketing. Analysis of her recent publications reveals a strong focus on the evolving role of statistics in the digital age, particularly examining how statistical expertise contributes to AI development, Industry 4.0 initiatives, and data-driven business transformation. Her work demonstrates increasing emphasis on customer analytics, predictive maintenance modeling, and the strategic implementation of data science in small and medium enterprises. Coleman's publications frequently address methodological challenges while maintaining strong practical relevance for industry practitioners. Throughout her career, Coleman has been actively involved with the European Network for Business and Industrial Statistics (ENBIS), contributing to the development and dissemination of statistical methods in business contexts. Her collaborative approach is evident in numerous co-authored publications across disciplines, demonstrating her ability to work effectively with researchers from diverse fields including engineering, healthcare, and business management. Her advisory work appears focused on helping organizations implement statistical thinking in business processes, with particular attention to small and medium enterprises seeking to leverage data analytics for competitive advantage. Though specific grant information isn't detailed in the available publications, her extensive industry-focused research suggests significant engagement with practical business problems and industry partnerships. Dr. Coleman has made substantial contributions to the field through her leadership in professional organizations, particularly ENBIS, where she has helped shape the discourse around industrial statistics and their business applications. Her work on Kansei Engineering demonstrates innovative approaches to integrating human factors with statistical methods for product development.

• Statistics
• Data Science
• Business Analytics
• +7 more

Dr. Daphne Lemasquerier is a Lecturer in Fluid Dynamics at the School of Mathematics and Statistics, University of St Andrews, and holds a UKRI Future Leaders Fellowship (£1.4M) since November 2024. Her research combines experimental , numerical , and theoretical approaches to planetary fluid dynamics, with a focus on gas giants and icy moons. Current Roles: Lecturer, UKRI Future Leaders Fellow Research Themes: Jupiter's atmospheric coupling, Europa/Saturnian moons' ocean dynamics, rotating turbulent flows Research Expertise spans: Experimental modeling of zonal jets and polar cyclones Direct numerical simulations of rotating convection Application of fluid dynamics to planetary science Interdisciplinary work bridging geophysics and astrophysics Scientific Recognition: Andreas Acrivos Dissertation Award (2022) Donald L. Turcotte Award (AGU, 2022) L'Oreal-UNESCO International Rising Talents (2022) L'Oreal-UNESCO French Young Talents (2021) Milton van Dyke Award (2019) Collaborative Networks: Carnegie Trust-funded experimental setup (2023), Royal Society Research Grant (2024), partnership with NERC’s National Centre for Atmospheric Science (2024), and international collaborations with UTIG, IRPHE, and Spinlab (UCLA).

• Planetary Fluid Dynamics
• Atmospheric Dynamics of Gas Giants
• Ocean Dynamics of Icy Moons
• +3 more

Dr. Anna Raffoni serves as a Senior Lecturer in Accounting and Finance at Loughborough Business School, Loughborough University, and holds the strategic role of Programme Lead for Social Science Research (Business and Management Studies). She joined the institution in January 2015 after accumulating academic experience at the University of East Anglia, Cranfield University School of Management, and the University of Bologna, establishing herself as a key contributor to the Accounting and Finance research group. Her academic foundation includes a PhD in Management Accounting (specializing in customer value management) awarded by the University of Florence in 2009, which continues to inform her interdisciplinary research trajectory. Raffoni's scholarly work centers on performance management, business analytics, and strategic management accounting, with publications appearing in premier journals including the European Journal of Operational Research, British Accounting Review, Production, Planning & Control, and Omega. Her research uniquely bridges quantitative operations research methods with accounting frameworks, particularly examining how data analytics transforms traditional performance measurement in banking and service sectors through techniques like machine learning and data envelopment analysis. Analysis of her 15 most recent publications (2012-2024) reveals a clear evolution toward integrating advanced analytics with performance management systems. She has pioneered multidimensional efficiency measurement in banking branches, explored total cost of ownership in supply chains, and investigated strategic performance measurement adoption, consistently demonstrating how operational research methods solve real-world accounting challenges while advancing theoretical models. Her scientific recognition features the Dean’s Award for Early Career Teacher of the Year (2017), acknowledging her excellence in undergraduate and postgraduate instruction. In her capacity as Programme Lead, Raffoni shapes research strategy and supports faculty development across Business and Management Studies. While specific supervisees and grant details aren't documented in available materials, her leadership role and active publication record indicate substantial engagement in research mentorship and academic community building. She actively collaborates within Loughborough's Accounting and Finance research group, contributing to interdisciplinary projects that address contemporary challenges in financial management through analytical approaches, with emerging work suggesting increasing focus on artificial intelligence applications in performance systems.

• performance management
• business analytics
• strategic management accounting
• +3 more