Brett Kolesnik is a Research Fellow in the Department of Statistics at the University of Warwick. His research focuses on probability theory, random structures, bootstrap percolation, and interactions with combinatorics. He has held postdoctoral fellowships at UC Berkeley, San Diego, and the University of Oxford, and was a Senior Demy at Magdalen College. His work includes organizing workshops on bootstrap percolation and collaborating with leading researchers in probability and combinatorics. Education: PhD in Mathematics from the University of British Columbia (advised by Omer Angel). Notable awards include the NSERC Postdoctoral Fellowship and the Florence Nightingale Bicentennial Fellowship in Statistics. Research interests span bootstrap percolation models, random graph dynamics, and stochastic processes. Recent work includes studies on Brownian map geometry, tournament score sequences, and Coxeter group structures. Selected articles explore topics such as critical beta-splitting processes, Catalan percolation, and random walks on algebraic structures. His publications appear in top journals like Electronic Journal of Probability and Annals of Applied Probability . Awards include the Florence Nightingale Fellowship and NSERC Postdoctoral Fellowship. Professional involvement includes organizing the 2024 BIRS workshop on Bootstrap Percolation and contributing to interdisciplinary collaborations in probability and combinatorics.
Dr. Primoz Skraba is a Professor in Applied and Computational Topology at the School of Mathematical Sciences, Queen Mary University of London. As Deputy Head of the Centre for Probability, Statistics and Data Science, he bridges theoretical topology with practical applications in data analysis, machine learning, and optimization. Education : PhD in Electrical Engineering from Stanford University (2009) Prior Roles : Positions at INRIA, France; Jozef Stefan Institute, Slovenia; University of Primorska; University of Nova Gorica His research focuses on applying topological methods to analyze complex data. Key areas include: Stability of persistence diagrams for quantitative control in finite sampling Variants of persistence (zig-zag, robustness, multiparameter) Algorithmic Complexity in computational topology Stochastic Topology for random geometric models (Poisson, Boolean) Recent publications emphasize persistent homology in random geometric complexes, universality theorems, and integrating topological methods into machine learning. He received grants from the Leverhulme Trust, EPSRC, and Alan Turing Institute for projects on topological universality and AI foundations. His advisee Gabryel Mason-Williams explores wireless sensor network applications of homology.
Professor Jorge Eduardo Pinto Santos holds the position of Professor of Theoretical Physics at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. His career includes roles as Reader (2018–2022), Lecturer (2013–2018), and a Junior Visiting Professorship at the Institute for Advanced Study in Princeton (2019–2020). He completed his Ph.D. at DAMTP between 2006 and 2010, followed by postdoctoral fellowships at Stanford University (2013–2014) and the University of California, Santa Barbara (2010–2013). His research focuses on general relativity, quantum gravity, and gravitational aspects of string theory. Key areas include numerical relativity and applications of general relativity to condensed matter physics via holography. Notable contributions involve studies on black hole binaries, extremal black holes, and the AdS/CFT correspondence. His recent work explores topics such as spinning black binaries in de Sitter space, gravitational instabilities in anti-de Sitter space, and entanglement islands in higher dimensions. He is affiliated with the High Energy Physics and Relativity and Gravitation research groups at DAMTP. His advising record includes seven doctoral students, with ongoing supervision of William Boyce (2024–present) and John Crump (2021–present). Publications highlight interdisciplinary advances, including investigations into black hole thermodynamics, cosmic censorship violations, and holographic models of condensed matter systems. His research bridges fundamental physics and numerical methods, addressing both theoretical and applied questions in gravitational physics.
Raphael Hauser is an Associate Professor in Numerical Mathematics at the University of Oxford's Mathematical Institute, Director of Graduate Studies - Teaching, and Tanaka Fellow in Applied Mathematics at Pembroke College. His affiliations include membership in the Data Science, Numerical Analysis, and Mathematical and Computational Finance research groups, as well as a fellowship at the Alan Turing Institute. Education: PhD in Operations Research, Cornell University, Ithaca, USA Dipl. Math. ETH, Swiss Federal Institute of Technology (ETH Zurich), Switzerland Research interests span data science, numerical optimisation, medical imaging, distributed computing, and applied probability/statistics. His work integrates mathematical rigor with practical applications, particularly in optimization algorithms, machine learning theory, and medical imaging technology. Publications focus on optimization theory, stochastic processes, medical imaging systems, and computational finance, with recurring themes in non-convex optimization guarantees, PCA variants, and X-ray tomography innovations. Awards: Oxford University Teaching Award (2007) SIAM Optimization Prize (2005) SIAM Student Paper Prize (2000) Advising includes 15+ DPhil students and 40+ MSc students, with projects in optimization, finance, imaging, and machine learning. Current postdocs and students are affiliated with the Alan Turing Institute and industrial partners like Siemens and Macquarie Group. He leads teams in the Mathematical Institute's research groups and collaborates with the Alan Turing Institute on large-scale data science initiatives.
Dr. Matthias Winter is a Senior Lecturer in the Department of Mathematics at Brunel University's College of Engineering, Design and Physical Sciences. He has been affiliated with Brunel since 2005, following academic positions at the University of Stuttgart (1996-2005) and postdoctoral fellowships at the Institute for Advanced Study in Princeton (1993-94) and Heriot-Watt University in Edinburgh (1994-96). His educational background includes a PhD from Stuttgart University in 1993 and a Habilitation from the same institution in 2003. Dr. Winter's research focuses on mathematical biology, particularly pattern formation in biological systems through reaction-diffusion equations. His work examines spike solutions, pattern formation mechanisms, and the mathematical analysis of biological phenomena. He has made significant contributions to understanding stable spike clusters in various contexts including the Gierer-Meinhardt system. His research spans Mathematical Biology, Pattern Formation, Reaction-Diffusion Systems, Nonlinear Partial Differential Equations, and several related mathematical disciplines. His recent publications (2023-2025) demonstrate continued activity across diverse applications including cancer modeling, ecological systems, climate modeling, and fundamental mathematical analysis of reaction-diffusion phenomena, showing his ability to apply sophisticated mathematical techniques to real-world biological problems. Editorial Board, ISRN Mathematical Analysis, since 2010 Academic Appeals Committee, since 2013 Level One Coordinator for Mathematics, since 2013 Course Director MSc Programme Computational Mathematics with Modelling Mathematics, 2008-2010 Dr. Winter teaches various mathematics courses including Mathematics and Statistics for Economists, Vector Calculus, and Group Projects in Mathematics, with a teaching portfolio spanning from foundational courses to specialized topics related to his research interests.
Daria Frank is a Senior Teaching Associate in Computational Mathematics at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position held since 2023. Previously, she served as Sultan Qaboos Early-Career Research Fellow and College Lecturer in Mathematics at Corpus Christi College (2020-2023), Director of Studies in Mathematics at Selwyn College (2019-2020), and Research Associate in Fluid Mechanics at DAMTP (2015-2019). Her academic foundation includes a PhD from DAMTP, University of Cambridge (2012-2015), Part III Mathematical Tripos at Cambridge (2009-2010), Diploma in Mathematics from Friedrich-Alexander-University of Erlangen-Nuremberg (2010-2011), and undergraduate studies in Mathematics and Physics at the same institution (2006-2009). Dr. Frank's research centers on complex fluid phenomena with emphasis on buoyancy-driven environmental flows , rotating plume dynamics , and practical engineering applications . Her work investigates pollution dispersal mechanisms, tornado formation processes, and energy-efficient building systems through rigorous theoretical and experimental frameworks. Key contributions address multiphase interactions in stratified environments and vortex behavior under rotational forces, bridging fundamental fluid dynamics with real-world environmental challenges. Publication trends reveal consistent focus on geophysical and environmental fluid mechanics, with recurring themes of density stratification effects, multiphase plume behavior, and rotational influences across atmospheric, oceanic, and built environments. Her work demonstrates strong interdisciplinary connections between theoretical modeling and practical engineering solutions. Dr. Frank held the prestigious Sultan Qaboos Early-Career Research Fellowship, supporting her investigations into fluid mechanics phenomena. Her academic service includes significant teaching responsibilities as Director of Studies in Mathematics at Corpus Christi College. As Director of Studies, she oversees undergraduate mathematics education while conducting research within DAMTP's fluid dynamics community. Her Sultan Qaboos Fellowship provided dedicated research time for fluid mechanics investigations, complementing her teaching role through integration of cutting-edge research into pedagogy. Current work continues to explore environmental fluid dynamics with potential applications in climate modeling and sustainable engineering.
Jon Keating is a Professor at the University of Oxford affiliated with the Mathematical Institute . His research spans Mathematical Physics , Number Theory , and Stochastic Analysis , with a focus on Random Matrix Theory and its applications to quantum systems and number theory. Research Trends: His recent work explores connections between random matrices and number-theoretic functions, with contributions to understanding moments of L-functions, characteristic polynomials, and quantum chaos. Key themes include asymptotic analysis, recursive structures, and interdisciplinary applications in nonlinear systems. Publications: Highlights include studies on CUE characteristic polynomials, the Ratios Conjecture, and collaborations in Nonlinearity , International Mathematics Research Notices , and Transactions of the American Mathematical Society .
Sebastian Schemm is a Heisenberg Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position regarded as equivalent to a non-permanent Associate Professor. He leads research within the Atmosphere-Ocean Dynamics group and previously held an ERC Starting Grant-funded Assistant Professorship (without tenure track) at ETH Zurich. Education and Career Path PhD (2013) and MSc (2010), ETH Zurich, Switzerland Postdoctoral researcher, University of Bergen, Norway (2014–2017) Postdoctoral researcher, Laboratoire de Météorologie Dynamique, ENS Paris (2017–2018) Assistant Professor (ERC Starting Grant), ETH Zurich (2020–2024) Heisenberg Fellow, DAMTP, University of Cambridge (2025–present) Research Focus Schemm’s work centres on atmospheric and climate dynamics, spanning turbulence to planetary scales. Core themes include the physics of extratropical cyclone life cycles, jet-stream and storm-track dynamics, Rossby waves and teleconnection patterns, high-resolution atmospheric modelling, and the integration of machine-learning techniques for parameter estimation, data assimilation, and kilometre-scale global simulations. He also contributes to large-scale initiatives such as ECMWF’s WeatherGenerator. Scientific Awards and Editorial Service DFG Heisenberg Fellowship (2025) ERC Starting Grant (2020–2024) European Meteorological Society Young Researcher Medal (2019) Co-Editor, Weather and Climate Dynamics (EGU) Co-Editor, Quarterly Journal of the Royal Meteorological Society PhD Supervision & Funding He currently supervises PhD students at both Cambridge and ETH Zurich, with funding streams including the Cambridge CREATES Doctoral Training Partnership and Swiss/EU grants. Ongoing students explore reinforcement-learning parameterisations, jet-stream–storm-track relationships, mid-latitude eddy energetics, machine-learning ensemble forecasting, and Bayesian parameter estimation in LES. Active Projects EU Horizon project WeatherGenerator (led by ECMWF) PASC HiRAD-Gen : High-Resolution Atmospheric Downscaling Using Generative Models
Professor Paul Fearnhead is a leading academic in Statistics at Lancaster University 's School of Mathematical Sciences . His research focuses on Bayesian and Computational Statistics , with applications in Anomaly Detection , Continuous-time Markov Processes , and Changepoint Analysis . Department: Mathematics and Statistics Academic Rank: Professor Email: p.fearnhead@lancaster.ac.uk His work bridges theoretical statistics and computational efficiency, notably through pruning techniques for change detection and novel Monte Carlo methods. Current projects include AI Hub initiatives, probabilistic AI foundations, and real-time anomaly detection in streaming data. Research outputs span Bayesian Analysis , Time Series Modeling , and Scalable Statistical Algorithms , with applications in fields like astronomy and epidemiology. Recent publications emphasize simulation-based composite likelihoods and efficient distributed changepoint detection. Scientific contributions include leadership roles in the STOR-i Centre for Doctoral Training and Data Science Institute (DSI) projects such as CoSInES and Statscale. He supervises PhD students including Dylan Bahia, Yuntang Fan, and Ziyang Yang.
Timothy John O'Brien is a Professor of Astrophysics and Associate Director of the Jodrell Bank Centre for Astrophysics at the University of Manchester, Faculty of Science & Engineering, Department of Physics & Astronomy. He also served as Director of Teaching & Learning in the Department of Physics & Astronomy from 2016-2020 and as Associate Dean for Social Responsibility for the Faculty of Science & Engineering during the same period. His educational background includes a B.Sc. (Hons) in Physics with Astrophysics from the University of London (1985) and a Ph.D. in Astrophysics from the University of Manchester (1990). Professor O'Brien's research concentrates on the study of exploding stars—mainly nova outbursts caused by thermonuclear explosions on the surface of white dwarfs in binary star systems. Over the years, he has developed expertise in a wide range of astrophysical techniques, working across the spectrum from radio waves to X-rays while also carrying out numerical simulations of the aftermath of explosions using his own hydrodynamic codes. Recently, he has developed an interest in the search for extra-terrestrial intelligence (SETI) using radio telescopes. His research fingerprint shows strong activity in Novae (100%), Optical Bursts (94%), Recurrent Novae (56%), Ejecta (45%), Planetary Nebula (38%), Emissions (32%), Classical Novae (32%), and Nebula (28%). His recent publications demonstrate a continued focus on nova outbursts, particularly in symbiotic novae systems like RS Ophiuchi, with high-resolution imaging techniques and multi-wavelength observations. His work spans theoretical modeling, observational astronomy across the electromagnetic spectrum, and data analysis from major telescope facilities. Kelvin Medal of the Institute of Physics (2014) for Public Engagement Professor O'Brien has extensive teaching experience, having worked as a lecturer in three universities since 1988. He has taught a wide range of courses in astronomy & astrophysics, physics, applied mathematics, and computing. From 1999-2009, he directed a distance learning program in astronomy that enrolled over 1,300 students. His current teaching includes Dynamics, Physics of the Solar System, and a course on the Search for Extraterrestrial Life. He has also supervised numerous project students throughout his career. He is actively involved in public engagement, including regular media appearances and events at the Jodrell Bank Centre for Engagement. He was a co-founder of the bluedot festival and contributed to the successful nomination of Jodrell Bank Observatory as a World Heritage Site in 2019.
Dimitrios P. Tsomocos is a Professor of Financial Economics at Saïd Business School and a Fellow in Management at St Edmund Hall, University of Oxford. He holds a BA, MA, MPhil, and PhD from Yale University and previously worked at the Bank of England. He serves on editorial boards including Annals of Finance and Economic Theory, and is a Senior Research Associate at the Financial Markets Group at the London School of Economics. His educational background includes: University of Oxford: M.A. by resolution, 2002 Yale University: Ph.D. in economics, 1996 Yale University: M.Phil. in economics, 1992 Yale University: M.A. in economics, 1990 Yale University: B.A. in economics, 1989 Professor Tsomocos is a mathematical economist specializing in Central Banking, Banking and regulation, Incomplete asset markets, Systemic risk, Financial instability, and Issues of new financial architecture. His research focuses on contagion, financial fragility, interbank linkages, and the impact of the Basel Accord using General Equilibrium models with incomplete asset markets, money, and endogenous default. He is working toward designing a new paradigm of monetary policy, financial stability analysis, and macroprudential regulation. His recent publications show a consistent focus on financial stability, banking regulation, and the interaction between monetary policy and financial stability. The research spans theoretical modeling of bankruptcy and default in general equilibrium frameworks, practical applications to bank regulation, analysis of commodity cycles in emerging economies, and policy responses to crises like the COVID-19 pandemic. His work frequently employs quantitative methods and general equilibrium modeling to address pressing issues in financial economics. His scientific achievements include: 2004 Bank Sabatell prize for the best work on the economics of banking (for "Book vs. Fair Value Accounting in Banking and Intertemporal Smoothing") Co-development of the Goodhart-Tsomocos model of financial fragility (2003) Testimony to House of Lords for the Economic and Financial Affairs and International Trade Sub Committee's report (2011) Appointment to Research Advisory Board, Central Bank of Russian Federation (2018) Professor Tsomocos has advised numerous PhD students and collaborated extensively with central banks worldwide. He has served as an economic advisor to a major political party in Greece and regularly provides commentary on the Greek economy. His research has had substantial policy impact, with the Goodhart-Tsomocos model implemented by more than ten central banks including the Bank of Bulgaria, Bank of Colombia, Bank of England, and Bank of Korea. He continues to collaborate with researchers from the ECB, Central Bank of the Russian Federation, and Bank of Chile on updated versions of his financial fragility model. He co-developed the Goodhart-Tsomocos model of financial fragility while working at the Bank of England, which has been implemented at various central banks globally. His research group at Oxford continues to refine this model and apply it to contemporary financial stability challenges.
Dr. Chunyan Mu serves as a Senior Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen, actively contributing to both academic instruction and cutting-edge research in computing science while currently accepting new PhD students. Her research program centers on Trustworthy AI and Safe Autonomy, with specialized expertise in formal verification of responsibility, accountability, and privacy mechanisms within multi-agent systems. She investigates resilience frameworks for autonomous intelligent systems and develops advanced methodologies for information flow security analysis, bridging theoretical computer science with practical security implementations. Analysis of her publication trajectory (2014-2025) reveals consistent innovation in applying formal methods to security-critical systems. Key thematic developments include probabilistic strategy logic for observability analysis, quantitative verification of opacity properties, and game-theoretic approaches to security verification, demonstrating increasing sophistication in handling multi-agent accountability and system resilience challenges. Dr. Mu currently supervises PhD candidates and offers a fully funded doctoral position focused on formal verification of safety properties in autonomous systems, providing comprehensive financial support including tuition coverage, £20,780 annual stipend, and dedicated research funding for candidates with strong backgrounds in formal methods and artificial intelligence.
Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University
Professor Martin Liebeck is a Professor of Pure Mathematics and Head of the Pure Mathematics Section at the Department of Mathematics, Imperial College London. He is affiliated with the Algebra and Algebraic Combinatorics research groups and is part of the Mathematics research and teaching staff within the Faculty of Natural Sciences. His research interests span Pure Mathematics, with a focus on group theory, algebraic combinatorics, Lie groups, and representation theory. His work often involves the structure and properties of finite simple groups, algebraic groups, and their applications to permutation groups and character theory. Recent publications explore topics such as the actions of simple groups, unipotent classes in algebraic groups, character ratios, and the classification of permutation groups. His work frequently intersects with combinatorial and algebraic structures, with implications for both theoretical and applied mathematics. Professor Liebeck’s contributions include foundational studies on group generation, covering numbers, and character bounds, reflecting his deep engagement with the interplay between algebraic structures and combinatorial problems. His research has been published in leading mathematics journals, and he maintains an active academic profile through his personal webpage at http://www.ma.ic.ac.uk/~mwl/ .
Professor Athanassios Manikas holds the Chair of Communications & Array Processing in the Department of Electrical & Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He is a Fellow of both the IET and IMA, and has held significant editorial roles including Associate Editor for IEEE Transactions on Aerospace and Electronic Systems. His research focuses on wireless communications, radar systems, antenna array processing, and applied mathematics, with over 50 supervised PhD students and 150+ Masters projects. He leads Imperial's research group in array processing and has extensive industry collaborations, including technical leadership of the University Defence Technology Centre in Signal Processing (2008-2013). Awards include the IEEE PIMRC 2022 Best Paper Award and recognition as an IEEE COMSOC Distinguished Lecturer (2016-2017). His work integrates differential geometry principles with array processing, as detailed in his monograph Differential Geometry in Array Processing . Professor Manikas has served as an expert witness in high-profile cases and contributes to academic governance roles such as the Royal Society's International Fellowship Committee. His research group is affiliated with the Space Lab at Imperial, focusing on innovative applications of array signal processing in aerospace and defense systems.
