Prof. Rama Cont is a Statutory Professor of Mathematics at the University of Oxford and a Professorial Fellow at St Hugh's College . He serves as Director of the Centre for Doctoral Training in Mathematics of Random Systems , Faculty Member of the Stochastic Analysis Group , and Senior Research Fellow at the Institute for New Economic Thinking . Additional roles include Director of the Oxford Martin Programme on Systemic Resilience , Principal Investigator at the Oxford Suzhou Centre for Advanced Research , and Editor-in-Chief of Mathematical Finance . His research interests span pathwise methods in stochastic analysis, rough analysis, functional Ito calculus, mathematical modeling in finance, systemic risk, and data-driven decision systems. Recent publications focus on causal transport, rough volatility, and deep residual networks, reflecting his interdisciplinary approach to mathematics and finance. Functional Ito calculus and pathwise integration Rough volatility and financial market dynamics Systemic risk in financial networks Deep learning applications to finance and stochastic processes He has received prestigious awards including the Louis Bachelier Prize , SIAM Fellowship, Royal Society APEX Award, and IMA Fellowship. His editorial roles and seminar leadership underscore his influence in mathematical finance and stochastic analysis.
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.
Dr. Yongchao Huang is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been employed since August 2023. He also holds affiliations with the University of Oxford and the University of Cambridge through past postdoctoral and collaborative roles. He is actively involved in research, teaching, and academic service, and is currently accepting PhD students. His educational background includes: DPhil in Engineering Science, University of Oxford (2013–2017) Additional training in Machine Learning at Oxford (2015–2019) Dr. Huang's research focuses on fundamental and physics-informed machine learning, with core interests in Bayesian inference, variational methods, generative modeling (especially score-based), reinforcement learning, and interdisciplinary AI applications in mechanics, biology, energy, climate, and finance. A central theme of his work is the inference and sampling of probability densities, particularly through innovative particle-based and physics-inspired computational frameworks. He founded the Computational and Physical Learning (CPL) lab at Aberdeen in 2023. His recent publications (2020–2025) reflect a strong trend in probabilistic machine learning, with increasing focus on physics-based inference methods such as electrostatics, fluid dynamics, and material point methods. These works bridge machine learning with applied mathematics and physical simulation, demonstrating a unique interdisciplinary approach. Topics span Bayesian neural networks, acoustic wave propagation, mortality modeling, and adversarial cybersecurity. Dr. Huang has received academic recognition through invitations to serve on program committees and editorial roles: Program Committee Member, ECAI 2024 Organizing Committee, Bioinference 2024 Guest Editor, Journal of Theoretical Biology Senior Scientific Advisor to a UK firm He has supervised 57 MSc theses independently and currently supervises one PhD student. He has secured research engagement through collaborations with institutions including Oxford, Cambridge, and industry partners. His teaching includes courses such as Introduction to Software Engineering , Software Process and Management , and Computational Intelligence at Aberdeen, as well as practicals in inference at Cambridge. Dr. Huang leads the Computational and Physical Learning (CPL) lab at the University of Aberdeen, a curiosity-driven research group focused on foundational advances in machine intelligence. Though currently a solo researcher due to limited resources, the lab emphasizes end-to-end research and open collaboration. He encourages student mobility and interdisciplinary exploration.
Dr Yvo Pokern is an Associate Professor in Statistics at University College London since 2018. His research focuses on computational statistics and machine learning, with expertise in diffusion processes and Bayesian methodology. He earned his PhD in mathematics under Andrew Stuart, a Masters at Paris XI with a dissertation at the Max-Planck-Institute in Leipzig, and was a postdoctoral researcher at Warwick University with Gareth Roberts and Wilfrid Kendall. His primary research interests include statistical inference for diffusion processes (particularly hypoelliptic diffusions and diffusions on manifolds), Bayesian methods such as Markov chain Monte Carlo, and statistical applications in spectroscopy (ENDOR). His work combines theoretical rigor with practical applications in diverse scientific domains. Analysis of his recent publications reveals a consistent theme of developing and applying advanced statistical techniques to complex real-world problems, including traffic flow, fingerprint analysis, and magnetic resonance spectroscopy. Dr Pokern has supervised numerous PhD students, several of whom have gone on to academic careers. Notable former students include Mai Ngoc Bui (now lecturer at the British University Vietnam) and Tjun Yee Hoh (now lecturer at UCL School of Management).
Dr. Alfred Kume is a Senior Lecturer in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. He has held this position since 2004 and has been involved in examining processes for the Institute of Actuaries. His research focuses on shape analysis, directional statistics, image analysis, and stochastic geometry. Kume obtained his PhD and postdoctoral training at the University of Nottingham after working as an actuary. He has supervised students including Theodoros Gkolias and Justyn Campbell-White. His work spans statistical methodology applied to astronomy (e.g., stellar light observations, HII regions) and computational statistics (e.g., holonomic gradient methods, clustering algorithms). His publications reflect expertise in probability distributions, algorithm development, and interdisciplinary applications. His office is located in Cornwallis South, Canterbury Campus. Research interests emphasize statistical techniques for shape and directional data, with applications in astronomy and biology. Key contributions include saddlepoint approximations for normalizing constants and statistical clustering methods. His work bridges theoretical statistics with practical problems in astrophysics and actuarial science. Publications highlight trends in statistical methodology (e.g., Bingham/Fisher-Bingham distributions), computational algorithms, and interdisciplinary collaborations. While no specific awards are listed, his extensive publication record and academic roles reflect scholarly recognition. Advising focuses on statistical shape analysis and Bayesian methods, with grants possibly tied to collaborative projects. He is part of research teams analyzing molecular clouds and astronomical phenomena. His lab or team activities are integral to interdisciplinary projects, though specific lab names are not mentioned.
Dr. Curt von Keyserlingk is a Reader (equivalent to Associate Professor) in theoretical physics at King's College London, based in the Theory & Simulation of Condensed Matter Group within the Department of Physics, Faculty of Natural, Mathematical & Engineering Sciences. His research focuses on understanding complex quantum systems through both analytical and numerical approaches. His educational background includes an MMath from the University of Cambridge, followed by DPhil studies at the University of Oxford under Professor Steve Simon. Prior to his current position at King's, he held a postdoctoral research fellowship at the Princeton Center for Theoretical Science and was a lecturer at the University of Birmingham. Dr. von Keyserlingk's research centers on interacting quantum systems, studying exotic phenomena such as superconductivity, topological order, localization, and time crystallinity. His work bridges the gap between fundamental quantum mechanics and practical applications in quantum computing. He develops both analytical frameworks and numerical tools to understand how quantum systems evolve and behave under various conditions, with particular emphasis on non-equilibrium dynamics, quantum information processing, and topological phases of matter. His recent publications reveal a strong focus on quantum many-body systems, with particular attention to topological phases in three dimensions, operator dynamics in quantum systems, and the interplay between dissipation and quantum information. His work spans from fundamental theoretical questions about quantum thermalization to practical applications in quantum error correction and quantum computing architectures. Dr. von Keyserlingk is the recipient of a prestigious UKRI Future Leaders Fellowship, which supports his research on robust many-body quantum phenomena. His current projects include 'Robust Many-body Quantum Phenomena Through Driving And Dissipation' (2025-2028) and 'Robust many-body Quantum phenomena through Driving and Dissipation' (2022-2025). He actively supervises PhD students and runs the physics intercollegiate programme between King's College London and Royal Holloway, University of London. His research group focuses on developing new theoretical frameworks to understand quantum systems that could potentially be harnessed for quantum computing applications.
Professor Klaus McDonald-Maier is a full Professor in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex , where he leads the Embedded and Intelligent Systems (EIS) Research Laboratory and heads the Intelligent Embedded Systems and Environments Research Group . He is also Director of Impact , Visiting Professor at the University of Kent, and Visiting Research Affiliate at NASA Jet Propulsion Laboratory, California Institute of Technology. Education PhD in High-Performance Parallel Neural Network Architectures, Friedrich-Schiller-University Jena (Germany, 1999) Electronic Engineering studies, University of Ulm (Germany) Electronic Engineering studies, Cardiff University (Wales) Electronic Engineering studies, École Supérieur de Chimie Physique Électronique de Lyon (CPE-Lyon) (France) Research Interests Professor McDonald-Maier’s research integrates embedded systems , System-on-Chip (SoC) architectures , and AI-driven robotics . He pioneers visual place recognition techniques that remain robust under severe appearance and viewpoint changes, develops cybersecurity frameworks based on ICMetrics for autonomous vehicles and IoT, and designs approximate real-time computing solutions for energy-constrained multicore and FPGA platforms. His work on radiation-tolerant systems supports space and nuclear applications, while his bio-inspired algorithms enable lightweight, neuromorphic perception on resource-limited robots. Publication Trends Between 2022 and 2025 his output converges on FPGA-accelerated AI , secure edge intelligence , visual navigation for autonomous systems , and healthcare analytics . He repeatedly couples rigorous algorithmic innovation with practical hardware deployment, yielding energy-efficient, real-time systems validated in domains ranging from autonomous driving to post-stroke rehabilitation. Scientific Awards & Recognition Best Paper Award – IEEE Transactions on Sustainable Computing (2024) Best Paper Award – IEEE/ACM DATE (2024) Best Paper Award – IEEE Systems Journal (2022) Best Paper Award – IEEE Sensors Journal (2021) Best Paper Award – IEEE Access (2020) Research Grants & Industrial Collaboration He has secured major funding from EPSRC , EU Horizon 2020 , Innovate UK , and industry partners. Current projects span trustworthy autonomy, radiation-hardened edge AI, and AI-enhanced rehabilitation technologies. He is Chief Scientist of UltraSoC Technologies Ltd and CEO of Metrarc Ltd , commercialising University research in semiconductor debug and cybersecurity respectively. Laboratory & Team Leadership As Director of the Embedded and Intelligent Systems Laboratory (EIS Lab) , he oversees a multidisciplinary team of researchers and PhD students, providing state-of-the-art FPGA, robotics, and embedded-systems facilities. The lab collaborates closely with NASA JPL, UK Atomic Energy Authority, and leading semiconductor firms to translate fundamental research into high-impact industrial solutions.
Professor Oliver Johnson is a faculty member at the School of Mathematics, University of Bristol, UK, where he serves as Head of School and holds the Professor of Information Theory position. His research bridges information theory, probability, and statistics, focusing on entropy convergence, group testing, and fundamental limits in data analysis. Current PhD students: Kieran Morris, Conor Crilly Ex-PhD students: Matt Aldridge, Leonardo Baldassini, Dan Cowley, Vaia Kalokidou, Tom Kealy, Jennifer Chakravarty, Zichen Gui, Chrys Paschou Ex-postdoc: Erwan Hillion His work includes ORCiD profile and collaborations across information theory, cybersecurity, and ecological modeling.
Professor Wes Armour is a Professor of Scientific Computing at the University of Oxford and serves as the Associate Head of Department for Research in the Department of Engineering Science. He previously directed the Oxford e-Research Centre, an interdisciplinary research center within the Engineering Science Department. With over £31 million secured as PI or Co-I, his work spans supercomputing, signal processing, machine learning, computational fluid dynamics, and protein crystallography. Professor Armour's research focuses on extracting science from data through fundamental challenges in modeling, simulation, and data processing. His work draws from numerical analysis, signal processing, and machine learning to develop technologies enabling future scientific discoveries, particularly for the Square Kilometre Array (SKA) telescope. Key interests include GPU computing, high performance computing, and machine learning applications across diverse domains from radio astronomy to finance. As Director and Principal Investigator of JADE and JADE2, a 700-GPU machine, he established the UK's first national High Performance Computer facility dedicated to advancing Artificial Intelligence and Machine Learning. His research group has pioneered GPU applications across multiple fields, including Square Kilometre Array data processing, protein crystallography, and graphene simulations. Current projects span energy-efficient machine learning, stock price prediction in finance, prime number prediction in cryptography, and multi-modal CCTV data analysis. Professor Armour has been instrumental in developing real-time signal processing techniques for astronomical observations, including the ARTEMIS system for millisecond radio transient detection. His publications demonstrate consistent innovation in GPU-accelerated computing dating back to early work in 2008 on accelerating conjugate gradient routines for electron transport in graphene.
Christophe Andrieu is a Professor in Statistics within the School of Mathematics at the University of Bristol. His research bridges theoretical probability, computational statistics, and applied mathematics, with significant contributions to Markov Chain Monte Carlo methodologies and Bayesian inference frameworks. He maintains active collaborations across engineering and data science domains. His educational background includes: M.A. from List.Natnl.Scis.App.Lyon Additional M.A. (institution unspecified) Ph.D. from Paris Andrieu's research focuses on Markov Chain Monte Carlo theory , where he develops convergence guarantees and efficiency bounds for complex samplers. His work extends to non-reversible MCMC algorithms , piecewise deterministic processes , and gradient-free optimization techniques. Recent publications demonstrate innovative approaches to state-space models and numerical integration, often addressing high-dimensional statistical challenges through stochastic approximation methods. His fingerprint reveals deep specialization in Markov chain convergence analysis and computational Bayesian statistics. His 15 most recent publications (2021-2025) exhibit consistent focus on theoretical foundations of Monte Carlo methods, particularly convergence analysis of Markov chains and novel sampler designs. Key trends include the application of weak Poincaré inequalities to pseudo-marginal MCMC, development of self-organizing state-space models, and exploration of hypocoercivity in piecewise deterministic processes. The work spans both theoretical advancements and practical implementations for engineering and statistical applications. Andrieu has secured significant research funding including: COmputational Statistical INference for Engineering and Security (COSINES) (2018-2023) New Approaches to Data Science (2018-2023) He has supervised 5 research students and maintains active collaborations in computational statistics and machine learning. His network shows strong connections with probability theory and engineering research groups.
Federico Malizia is a Postdoctoral Research Associate at Northeastern University's Network Science Institute, specializing in complex systems and network science with a focus on higher-order interactions in social and biological contexts. Education PhD in Complex Systems, University of Catania, Italy His research bridges complex networks , epidemic processes , and computational social science , investigating how group dynamics emerge from higher-order structures. Using mathematical modeling and simulations, he examines contagion phenomena, social polarization, and synchronization in systems where interactions extend beyond pairwise connections. Malizia's recent publications (2024-2025) reveal fundamental patterns in higher-order network dynamics, demonstrating how hyperedge overlap and structural heterogeneity drive explosive transitions, synchronizability, and polarization. His work spans theoretical frameworks for simplicial contagion to practical inference methods for epidemic data, establishing critical links between network topology and dynamical outcomes across disciplines. As an active contributor to the Network Science Institute, he participates in collaborative research initiatives and maintains his own laboratory focused on computational models of complex systems. His work was featured in NetSci 2025 proceedings, highlighting the institute's leadership in network science research.
Dr. Wei Dai is a Senior Lecturer (Associate Professor) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the EPSRC Centre for Maths of Precision Healthcare and the Communications and Signal Processing group. His research focuses on sparse signal processing, machine learning applications in signal processing, linear and bilinear inverse problems, wireless communications, and random matrix theory. Notably, he contributed to the first compressive sensing DNA microarray prototype and has a highly cited 2009 paper on compressive sensing reconstruction. Dr. Dai's educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Colorado at Boulder (2007) and postdoctoral research at the University of Illinois at Urbana-Champaign (2007-2010). His work bridges theoretical signal processing with practical applications in sensing, communication systems, and biomedical signal analysis. He leads research initiatives in gridless DOA estimation, robust beamforming, and cortico-muscular coupling analysis using advanced optimization techniques. His research outputs span topics like spectral compressed sensing, Bayesian methods for integrated sensing-communication systems, and dictionary learning for causal discovery. Ongoing work emphasizes low-rank matrix recovery, distributed compressed sensing, and mathematical frameworks for super-resolution localization. Dr. Dai collaborates across disciplines, leveraging signal processing innovations for healthcare technology and next-generation wireless systems.
Lu Yin is an Assistant Professor in the School of Computer Science and Electronic Engineering at the University of Surrey. He holds affiliations as a long-term visiting researcher at Eindhoven University of Technology (TU/e) and collaborator with the Visual Informatics Group (VITA) at the University of Texas at Austin. Previously, he served as a Postdoctoral Fellow at TU/e and worked as a research scientist intern at Google's New York City office. His work bridges academic and industrial research, focusing on AI Efficiency, AI for Science, and Large Language Models. His research emphasizes optimizing neural networks through sparsity techniques, including pruning strategies for LLMs and vision models. Notable contributions include the OWL method for LLM pruning and Lottery Pools for improving sparse network performance. Yin actively collaborates with institutions like TU/e, Google Research, and Intel Research, and has organized conferences such as CAPBS 2025 and CAI 2025 Workshops. Yin has secured significant grants, including a 10,000,000 NWO-funded grant for NVIDIA A100 GPU resources. He has delivered invited talks at prestigious institutions like Carnegie Mellon University and City University of Hong Kong. His work has been recognized with the Best Paper Award from LoG 2022.
Dr. Yunxiao Chen is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), where he co-leads a psychometric lab with Professor Irini Moustaki. Previously, he was an Assistant Professor at Emory University (2016–2018) and earned his PhD in Statistics from Columbia University (2016). His research focuses on developing statistical and computational methods for social data science, addressing challenges in high-dimensional data analysis, latent variable models, and educational assessment. Education: PhD in Statistics, Columbia University, 2016 Research Interests: High-dimensional factor models (matrices, tensors, counting processes) Dynamic behavioral data analysis Sequential decision theory in personalized learning Statistical inference for large-scale item response data Applications in education, psychology, and marketing Publications: Recent work includes advancements in factor analysis, change-point detection, and DIF statistical inference Key journals: Journal of the American Statistical Association , Psychometrika , Journal of Machine Learning Research Awards: 2024 Psychometrics Society Best Reviewer Award 2022 Early Career Award 2018 NCME Loyd Dissertation Award Advising & Grants: Accepts PhD students in statistical methodology Funded by National Academy of Education/Spencer Fellowship (2018–2020) and IEA R&D grants (2022–2023) Labs & Teams: Runs LSE’s psychometric lab focused on educational measurement Collaborates with interdisciplinary teams on machine learning applications
Dudley Stark is a Reader in Mathematics and Probability at the School of Mathematical Sciences, Queen Mary University of London. He holds a BA in Mathematics and BS in Physics from the University of Rochester, an MA in Mathematics from UCLA, and a PhD in Mathematics from the University of Southern California. Prior to his current role, he held postdoctoral positions at the University of Zurich, University of Melbourne, and Hewlett Packard Laboratories in Bristol, and was a visiting scholar at Green-Templeton College (University of Oxford) and on secondment to the University of Bristol. His research focuses on probabilistic and enumerative combinatorics, random combinatorial objects (e.g., permutations and graphs), Poisson approximation, generating functions, and asymptotic expansions. He teaches courses such as Bayesian Statistical Methods and Advanced Derivatives Pricing and Risk Management. His work spans over 30 years, with contributions to stochastic processes, graph theory, and combinatorial probability. His research has been published in top journals like Stochastic Processes and Their Applications , Discrete Mathematics , and Advances in Applied Mathematics . Stark collaborates with institutions globally and contributes to the Centre for Combinatorics, Algebra and Number Theory. His expertise includes random graph theory, asymptotic enumeration, and applications of probabilistic methods in combinatorial structures.
