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Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI

• Complex Systems
• Financial Mathematics
• Statistical Physics
• +6 more

Professor Richard Samworth is a leading academic at the University of Cambridge , affiliated with the Department of Pure Mathematics and Mathematical Statistics and serving as Director of the Statistical Laboratory . His research focuses on Nonparametric and High-dimensional Statistics , addressing challenges in data analysis, statistical learning, and computational methods. Research Interests : Richard Samworth's work emphasizes Nonparametric Statistics , High-dimensional Data , and Statistical Learning . His research spans topics such as Missing Data , Changepoint Detection , Log-concave Density Estimation , and Subgroup Analysis , with applications in Machine Learning and Data Science . Key methodologies include Score Matching , Random Projections , and Minimax Estimation . Recent publications highlight advancements in Semi-Supervised Learning , Robust Statistical Testing , and High-dimensional PCA with heterogeneous missingness. His work bridges theoretical rigor with practical applications, particularly in Statistical Algorithms and Optimization .

• Nonparametric Statistics
• High-dimensional Statistics
• Machine Learning
• +3 more

Timothy M. Hospedales is a Professor of Artificial Intelligence at the Institute of Perception, Action and Behaviour within the School of Informatics at the University of Edinburgh . He also serves as VP AI and Head of Samsung AI Research Centre Europe . His research focuses on efficient and robust AI , emphasizing meta-learning , lifelong transfer-learning , and domain adaptation in both probabilistic and deep learning frameworks. Applications span computer vision , vision and language , reinforcement learning for robotics , and finance . Professor at University of Edinburgh (2020–present) ELLIS Fellow (2021) Head of Samsung AI Research Europe (2020–present) Founding Director of Applied Machine Learning Lab at QMUL (2012–2016) His work includes pioneering contributions to meta-learning , few-shot learning , and self-supervised methods , with notable awards such as the Best Paper Prize at ICML AutoML 2018 and Best Student Paper at ICPR 2018 . He has co-authored 15+ recent papers on topics like Vision-Language Models , Medical AI Fairness , and Diffusion Model Optimization . He served as Program Co-Chair for BMVC 2018 and AAAI 2022 , and authored a book on Visual Adaptation in the Deep Learning Era (2022). Co-Chair, BMVC 2018 Guest Editor, IET CV Special Issue (2016) Keynote Speaker at TASK-CV Workshop (ECCV 2016) Special Issue on Fewer Labels (IEEE PAMI 2020) His leadership extends to organizing workshops like the Learning-to-Learn Workshop at ICLR 2021 , Meta-Learning Workshop at NeurIPS 2020 , and Domain Generalisation Workshop at ICLR 2023 . Current projects include Meta-Omnium (CVPR 2023) for general-purpose meta-learning and MetaAudio (ICANN 2022) for few-shot audio classification benchmarks.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +6 more

Mungo Wilson is a Professor of Finance at the Saïd Business School , University of Oxford, and an associate member of the Oxford-Man Institute of Quantitative Finance. He holds a PPE degree from Oxford, an MSc in Economics from LSE, and a PhD in Economics from Harvard. His research focuses on asset pricing, credit risk, mutual funds, and macroeconomic influences on financial markets. He has published extensively in top journals and engages actively with industry through board roles and advisory work. Education : Bachelor's in Philosophy, Politics & Economics (PPE), University of Oxford MSc Economics, London School of Economics PhD in Economics, Harvard University Research Interests : Assesses how macroeconomic conditions and credit risk shape asset prices Analyzes mutual fund behavior and market information transmission mechanisms Explores political dynamics' impact on financial markets His professional engagements include board memberships with funds like EA Asia Absolute Return Master Fund and advisory roles at Broadwell Capital. Teaching focuses on microeconomics for MBA programs, covering game theory, market structures, and price discrimination. Advisory & Grants : Supervises doctoral students at Saïd Business School and serves on programme committees for major finance conferences (e.g., European Finance Association, CEPR Annual Spring Symposium). Awards : While no specific prizes are listed, his impactful research has been widely cited and presented globally.

• Asset Pricing
• Macroeconomics
• Credit Risk
• +3 more

Tom Rainforth is an Associate Professor of Statistical Machine Learning at the University of Oxford's Department of Statistics, leading the RainML Research Lab (rainml.uk). He holds a Tutorial Fellowship at Mansfield College and is Principal Investigator of the ERC Starting Grant 'Data-Driven Algorithms for Data Acquisition' (2024–2029). Previously, he held roles including a postdoc under Yee Whye Teh (2017–2019), Junior Research Fellow at Christ Church College (2019–2019), and Florence Nightingale Bicentennial Fellow (2020–2024). He earned his MEng in Mechanical Engineering from the University of Cambridge and his D.Phil from Oxford under Frank Wood and Michael Osborne, focusing on probabilistic programming and Monte Carlo methods. He briefly worked in Ferrari's Formula 1 team. Research Interests : Bayesian experimental design, probabilistic and data-efficient machine learning, active learning, deep learning (with a focus on probabilistic approaches), probabilistic programming, and Monte Carlo methods. His work emphasizes statistical efficiency and adaptive algorithms. Publications : Recent contributions span modern Bayesian experimental design, adaptive importance sampling (Daisee), and applications of probabilistic methods in LLMs and generative models. His research bridges theory and practice, addressing challenges in scalability and robustness. Awards : ERC Starting Grant (2024–2029), highlighting his leadership in foundational AI research. Advising & Grants : Supervises 15 graduate students, including work on Bayesian neural networks, generative flows, and experimental design. His ERC grant supports cutting-edge data-driven algorithm development. Labs & Teams : Directs the RainML Lab, which develops scalable Bayesian methods and probabilistic AI systems.

• Statistical Machine Learning
• Bayesian Experimental Design
• Probabilistic Programming
• +3 more

Dr. Shan Lu is a Lecturer in Finance at the Department of Accounting and Finance, Kent Business School, University of Kent, since August 2021. He previously held positions at the University of Aberdeen and the University of Bradford and earned his PhD from the University of Aberdeen. Research interests: Financial derivatives, option pricing, and quantitative finance. His work focuses on volatility modeling, risk-neutral density estimation, and computational finance, with publications in journals such as the European Journal of Finance, Journal of Futures Markets, and Economics Letters. Teaching: Covers financial markets, derivatives, econometrics, and quantitative methods at undergraduate and postgraduate levels. Scientific awards: Fellow (FHEA) of Higher Education Academy Advising: Offers PhD supervision in topics aligned with his research interests, including financial derivatives and quantitative finance. He emphasizes collaboration on research ideas directly related to his expertise. Publications: Recent work explores volatility dynamics in VIX/VXX options, risk-neutral density extraction, and implied volatility forecasting, leveraging computational methods and empirical finance techniques.

• Financial Derivatives
• Option Pricing
• Quantitative Finance
• +3 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

Sho Konishi serves as Director of the Nissan Institute of Japanese Studies and Associate Professor in Modern Japanese History at the University of Oxford, holding dual appointments in the Faculty of History and the Oxford School of Global and Area Studies. His interdisciplinary work bridges Japanese studies with transnational intellectual history, focusing on non-imperial connections between Japan, Russia, and global networks. Undergraduate studies completed as a U.S. military school cadet after extensive coursework in Russia Georgetown University: Philosophy and International Affairs University of Chicago: Ph.D. in History (Theology and History) Postdoctoral fellowship at Harvard University Konishi's research centers on intellectual and cultural history with emphases on transnational encounters, anarchist thought, and the history of science. He investigates non-imperial knowledge production across 19th-20th century Japan, Russia, and Eastern Europe, with particular focus on symbiosis theory, linguistic democracy, and the role of nonverbal communication in epistemology. His work challenges Western-centric modernity narratives through examinations of anarchist natural science and transnational emotional histories. His publications reveal consistent engagement with alternative epistemologies and cross-cultural exchanges, spanning transnational history, the history of science, and cultural studies. Key themes include Japan-Russia intellectual networks, anarchist cooperative movements, and non-imperial frameworks that reposition Japan within global knowledge systems rather than colonial paradigms. ICAS Book Prize 2023 – Humanities for Japan’s Russia: Challenging the East-West Paradigm Konishi contributes to international academic governance as Advisory Board Member for the Esperantic Studies Foundation, External Faculty at the University of Chicago Stevanovich Institute on the Formation of Knowledge, and International Advisory Board Member for Manchester University Press's Anarchist Studies Series. His cultural impact extends to architectural projects like the Venice Biennale's Philippine Pavilion through 'Symbiogenesis: The Art of Bayanihan Architecture'.

• Intellectual History
• Cultural History
• Transnational History
• +7 more

Dr Ann Lewis is Senior Lecturer in French Studies at Birkbeck, University of London, within the School of Creative Arts, Culture and Communication . Since her appointment in 2008 she has held numerous leadership roles, including Subject Lead for Languages and Linguistics and programme director for several BA and MA programmes. Education: BA Joint Honours in History and French, New College, University of Oxford M.St. in European Literature, University of Oxford PhD in French Literature, Queen Mary, University of London PGC-HE (with distinction), Fellow of the Higher Education Academy Research Interests: Dr Lewis’s scholarship is situated at the intersection of eighteenth-century French literature and visual culture. Her work pursues two complementary strands: the first investigates text–image relations , exploring how eighteenth-century French novels were illustrated, re-imagined and circulated beyond the book through almanacs, extra-illustrated editions and domestic display. The second strand interrogates the representation of prostitution in eighteenth-century France, combining literary analysis with cultural history to illuminate questions of gender, commerce and morality. Both research lines deploy interdisciplinary methodologies that draw on illustration studies, adaptation theory and material bibliography. Publications profile: Across more than two decades, Dr Lewis has produced a monograph, three major edited collections and a series of peer-reviewed articles and book chapters (2004-2021). Her output charts an evolving focus from close readings of illustrated novels toward broader questions of intermediality, adaptation and the social lives of images, culminating in recent work on Rousseau’s Julie as material artefact. Honours & Awards: Leverhulme Early Career Fellowship, Leverhulme Trust (2009) Ronald Tress Prize, Birkbeck, University of London (2012) Haydn Mason Lecture, British Society for Eighteenth-Century Studies (2012) Nominated for Aurora Women into Leadership programme, Advance HE (2016) Doctoral supervision & grants: Dr Lewis welcomes proposals in eighteenth-century French literature and culture. She currently co-supervises two doctoral researchers: Miki Jablkowska – “The Reception and Cultural Adaptation of Voltaire in Eighteenth-Century Poland” Kirsten Tambling – “Watteau and Hogarth: Seduction, Space and Time” (awarded 2019) Laboratories, networks & teams: She is Co-founder and Co-convenor of the Birkbeck Eighteenth-Century Research Group , an interdisciplinary forum spanning French, English, History of Art and Comparative Literature. She additionally serves on the steering group of the Illustr4tio international research network devoted to illustration studies and is a member of the executive committee of AUPHF+ (Association of University Professors and Heads of French +).

• Eighteenth-century French literature and culture
• Text/image relations
• Book illustration
• +4 more

Professor Matthew Treherne is a leading scholar in Italian literature at the University of Leeds, where he holds the position of Professor of Italian Literature in the School of Languages, Cultures and Societies within the Faculty of Arts, Humanities and Cultures. He currently serves as Pro-Dean for Research and Innovation, and co-directs the Leeds Centre for Dante Studies. His research bridges medieval and Renaissance literature with contemporary issues such as finance, ecology, and technology. PhD in Italian Literature, University of Cambridge (2007) Diploma in Management Studies, Judge Business School, University of Cambridge (2000) M.Phil in European Literature (Distinction), University of Cambridge (1999) BA in Modern and Medieval Languages (First-class), University of Cambridge (1997) Matthew Treherne's research focuses on Dante and late-medieval religious culture, with a strong emphasis on the liturgical imagination and theological discourse in poetry. He explores how Dante’s work can inform modern debates on the ecological crisis, financialisation, and time. His interdisciplinary approach integrates theology, philosophy, and cultural studies. He leads the project The Cultural Life of Money and Finance , which investigates how the humanities can shape financial futures. His recent publications include Dante’s ‘Commedia’ and the Liturgical Imagination and co-edited volumes on Dante’s theology. His recent scholarly articles and chapters show a consistent engagement with Dante’s theological and poetic structures, Tasso’s religious poetry, and the role of art and liturgy in early modern culture. Themes such as sacramentality, time, vision, and interdisciplinarity recur across his work, reflecting a deep commitment to understanding literature as a living, ethically engaged practice. University Teaching Fellowship Developmental University Teaching Fellowship Faculty of Arts Development Teaching Prize Treherne has supervised numerous PhD students and fosters research-led teaching, notably through projects like Discover Dante and the Leeds Dante Podcast . He has led major research initiatives, including the AHRC-funded Dante and Late Medieval Florence , and collaborates widely across disciplines and institutions. He is also active in public engagement, contributing to BBC Radio 4’s In Our Time and curating podcast series such as Conversations on Dante and Arts and Humanities Futures . He co-leads the Cultural Life of Money and Finance project with Invisible Flock, blending humanities research with creative practice.

• Medieval Italian Literature
• Renaissance Italian Literature
• Dante Studies
• +7 more

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 Zeno Leoni is a Lecturer in the Defence Studies Department at King’s College London, based at the Joint Services and Staff College (JSCSC) of the Defence Academy of the UK. He is also an affiliate of the Lau China Institute, co-convening their policy brief series. His research focuses on US and Chinese grand strategies, US-China relations, AUKUS, and world order, with recent publications including a forthcoming book on the new Cold War. He leads a research group studying middle-power diplomacy in the Indo-Pacific and has advised Italian and UK government bodies on geopolitical trends. Teaching: Delivers courses on China for the Higher Command and Staff Course (HCSC) at the JSCSC, and lectures on International Security Studies, Airpower, and International Political Economy. Additionally teaches at Italy’s IASD defence institute. Key Research Areas: Specializes in US-China competition, AUKUS dynamics, and the evolving Indo-Pacific order. His work bridges academia and policy, with contributions to UK Parliament hearings on AUKUS and US defense policies. Leads research on middle powers’ strategies amid great power competition. Professional Activities: Executive Director of ITSS Verona, a non-profit focused on international security. Active in King’s research groups including Contemporary Marxism, Environmental Security, and Economic Conflict & Competition. Frequently contributes to media analyses for outlets like the Financial Times and BBC. Awards: While no specific prizes are listed, his extensive policy engagement and academic output reflect recognition in strategic studies circles. Collaborations include work with the EU, UAE, and other middle powers on geopolitical strategy.

• US-China relations
• Grand Strategy
• AUKUS
• +7 more

François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.

• Machine Learning
• Statistical Inference
• Computational Statistics
• +4 more

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +8 more

Kenneth J. Falconer is the Regius Professor of Mathematics at the University of St Andrews, where he is a member of the School of Mathematics and Statistics and the Analysis Research Group. He has held prestigious positions at the University of Bristol and Corpus Christi College, Cambridge, and has been a visiting professor at institutions including Oregon State University and the Australian National University. Regius Professor of Mathematics, University of St Andrews (2017–present) Professor of Mathematics, University of St Andrews (1993–2017) Reader, University of Bristol Lecturer, University of Bristol Research Fellow, Corpus Christi College, Cambridge His research centers on fractal and multifractal geometry, geometric measure theory, and related fields. He has made seminal contributions to the understanding of fractal projections, dimensional analysis of self-affine sets, and fractal processes. His work includes the concept of the digital sundial and the introduction of the affinity dimension and Falconer’s distance problem. His research spans dimensional analysis, random fractals, PDEs on fractal domains, and combinatorial geometry. The most recent publications show a sustained focus on intermediate dimensions, projections of fractal sets and measures, and the dimensional properties of stochastic processes. His work frequently involves collaboration with leading mathematicians and appears in top journals such as Transactions of the American Mathematical Society , Ergodic Theory and Dynamical Systems , and Journal of Fractal Geometry . Fellow of the Royal Society of Edinburgh (1998) Shephard Prize, London Mathematical Society (2020) CBE, King’s New Year’s Honours (2024) Kenneth Falconer has supervised numerous students and collaborated with many researchers including Jonathan Fraser, Pertti Mattila, and Xiong Jin. He has served on editorial boards for Fractals , Journal of Fractal Geometry , and Mathematical Proceedings of the Cambridge Philosophical Society . He has been active in professional service, including as Chair of the British Mathematical Colloquium 2018 and Publications Secretary of the London Mathematical Society (2006–2009). He organized major programs at the Isaac Newton Institute and Mittag-Leffler Institute. He is also known for his involvement in the Long Distance Walkers Association, where he served as Chairman and Editor of Strider , and for his mathematical poetry featured in publications like the London Mathematical Society Newsletter .

• Fractal Geometry
• Geometric Measure Theory
• Multifractal Measures
• +6 more