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Tengyao WangView profile

Professor

• High-dimensional Statistics
• Change-point Analysis
• Sparse Signal Detection
• +7 more

Tengyao Wang is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), serving as the MSc Statistics (Financial Statistics) Programme Director. Prior to LSE, he held positions as a Lecturer at University College London and a Research Fellow at the Cantab Capital Institute for the Mathematics of Information, University of Cambridge. His research focuses on high-dimensional statistics, computational efficiency, and statistical limitations imposed by computational constraints. Education: PhD in Statistics under Prof Richard Samworth at the University of Cambridge, with earlier studies including a Part III Essay in Empirical Process Theory. Research interests include sparse signal detection, change-point analysis, dimension reduction, robust statistics, and applications in medical statistics, financial data analysis, and material discovery. Key contributions include methodologies for handling missing data, high-dimensional change-point detection algorithms, and statistical learning techniques. Publications span theoretical advancements and applied innovations, with recent work emphasizing deep learning with missing data, residual permutation tests, and semi-supervised learning via random projections. His work has been recognized with awards such as the Royal Statistical Society Research Prize (2019) and the Guy Medal in Bronze (2023). He is an Associate Editor of the Journal of the Royal Statistical Society, Series B (JRSS B), and actively contributes to open-source tools like the 'ocd' and 'MissInspect' R packages for changepoint detection and missing data analysis.

Christos MiasView profile

Associate Professor

• Electromagnetic Shielding
• Computational Electromagnetics
• Microwave Engineering
• +3 more

Dr Christos Mias is a Reader in Electrical and Electronic Engineering at the School of Engineering, University of Warwick, UK. He serves as Director of Studies for the Electrical and Electronic Engineering stream and has held roles such as UK URSI Commission B (Fields and Waves) representative from 2009-2014. His research focuses on electromagnetic shielding, computational electromagnetics, microwave engineering, antennas, periodic structures, and radiowave propagation. Dr Mias' work bridges theoretical advancements with practical applications in antenna design, frequency selective surfaces, and electromagnetic modeling. His research interests emphasize innovative solutions in antenna array analysis, periodic structure optimization, and advanced simulation techniques such as finite element time-domain (FETD) modeling. He has contributed to understanding scattering phenomena, non-reflecting boundary conditions, and molecular vs electromagnetic wave propagation comparisons. Recent studies include railway infrastructure monitoring and critiques of superlensing concepts in plasmonics. Publications highlight advancements in MoM formulations, FSS analysis, and sparse matrix methodologies for boundary condition simulations. His work often integrates computational methods with real-world engineering challenges, such as optimizing antenna performance and improving electromagnetic compatibility in complex environments. Dr Mias advises through his role as Director of Studies, fostering academic excellence in electrical engineering education. His office is located in A325, with advice hours on Fridays 10 AM-12 PM during term time.

Tim RogersView profile

Professor

• Stochastic Processes
• Random Matrix Theory
• Collective Behavior
• +4 more

Professor Tim Rogers is affiliated with the University of Bath as a faculty member in the Department of Mathematical Sciences . He is actively involved in research spanning complex systems, network theory, and stochastic processes. PhD in Random Matrix Theory from King's College London (2010) His research focuses on emergent behavior in random systems , including: Collective Behavior : Crowd dynamics, lane formation, and noise-enhanced synchronization Epidemics & Networks : Spread prediction, node risk assessment, and misinformation impacts Ecology & Evolution : Trait emergence, species boundaries, and demographic noise effects Random Matrix Theory : Spectral analysis and applications to complex systems Publication trends reflect interdisciplinary work bridging Physics, Biology, and Mathematics , with a focus on network structures , stochastic modeling , and emergence phenomena . Scientific awards include: 2015 : Editor's Choice for Europhys. Lett. 109, 28005 2016 : Highlight of Journal of Physics A 2017 : Editor's Suggestion for Phys. Rev. E 92, 032708 He has supervised numerous PhD students and postdocs on projects related to stochastic dynamics , network modeling , and mathematical biology , with ongoing grants from agencies like EPSRC and The Leverhulme Trust .

• Functional Analysis
• Artificial Intelligence
• Foundations of Computational Mathematics
• +7 more

Anders C. Hansen is Professor of Mathematics at the University of Cambridge (Faculty of Mathematics, Department of Applied Mathematics and Theoretical Physics) and Professor II at the University of Oslo. He leads the Applied Functional and Harmonic Analysis group and holds a Royal Society University Research Fellowship. His research bridges pure mathematics and cutting-edge applications in AI, computational harmonic analysis, inverse problems, and compressed sensing. Education: PhD from the University of Cambridge, MA from UC Berkeley, and BA from the Norwegian University of Science and Technology. Research Interests: Hansen's work centers on foundational challenges in computational mathematics, including the Solvability Complexity Index hierarchy for classifying computational problems, instability phenomena in deep learning, and theoretical advances in compressed sensing. His group develops rigorous frameworks for high-dimensional data analysis, medical imaging, and AI safety, often exposing paradoxes in algorithmic reliability. Publication Trends: Recent articles focus on the limits of deep learning (e.g., Smale's 18th problem, instability in image reconstruction), mathematical foundations of AI (trustworthiness, feature selection, LLMs), and advanced compressed sensing (asymptotic incoherence, spectral computations). His work consistently intersects functional analysis with computational feasibility. Awards: PROSE Award Finalist (2022) Whitehead Prize (2019) IMA Prize in Mathematics and Applications (2018) Leverhulme Prize (2017) Royal Society University Research Fellow (2012) Advising & Leadership: Hansen has supervised 17 PhD students and 8 postdocs. He leads the Applied Functional and Harmonic Analysis group, coordinating interdisciplinary projects in mathematical data science. His editorial roles include SIAM Journal on Imaging Sciences and Proceedings of the Royal Society A .

• Sparse signal processing
• Signal processing for machine learning
• Wireless communications
• +4 more

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.

Yunxiao ChenView profile

Associate Professor

• Multivariate Statistics
• Social Statistics
• Latent Variable Models
• +5 more

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

• Numerical and Computational Mathematics
• Atmospheric Sciences
• Oceanography
• +3 more

Dr Jack Betteridge is an Honorary Research Fellow in the Department of Mathematics, Faculty of Natural Sciences, at Imperial College London. His work bridges computational mathematics with environmental sciences, focusing on numerical methods for atmospheric and oceanic systems. His research interests include: Numerical and Computational Mathematics Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience Computation Theory and Mathematics Distributed Computing Analysis of his 2019-2024 publications reveals deep engagement with finite element methods, particularly through the Firedrake project for automated PDE solutions. His work emphasizes high-performance computing applications in geophysical fluid dynamics, developing novel preconditioners and solvers for atmospheric modeling while contributing to computational education for mathematicians.

Arnaldo Delli CarriView profile

Associate Professor

• Nonlinear Dynamics
• Finite Element Analysis
• Nonlinear Vibration Analysis
• +3 more

Arnaldo Delli Carri is an Associate Professor at the CEES School of Engineering, serving as Curriculum Lead and actively involved in academic leadership. His research focuses on Nonlinear Dynamics and Finite Element Analysis, with expertise in structural analysis, modal testing, and vibration dynamics. He is currently accepting PhD students in areas like Nonlinear Vibration Analysis and FE model validation. Research interests include advanced topics such as nonlinear system identification, model upgrading, and machine learning applications in structural performance prediction. Notable projects involve analyzing planetary gearboxes, 3D printing opportunities in Africa, and laser vibrometer-based nonlinear detection. His work integrates experimental and computational methods to address challenges in mechanical systems and biomedical engineering. Collaborations span international partnerships, focusing on structural dynamics and nonlinear systems. His contributions include over 17 publications since 2011, with a focus on vibration analysis, finite element validation, and additive manufacturing applications. He currently supervises PhD candidates exploring nonlinear dynamics and FE formulations.

Jared TannerView profile

Professor

• Compressed Sensing
• Matrix Completion
• Signal and Image Processing
• +7 more

Professor Jared Tanner is Professor of the Mathematics of Information at the University of Oxford's Mathematics Institute and a Fellow of Exeter College. Previously, he held positions at the University of Edinburgh (2007-2012) as Professor, Reader, and Lecturer in Mathematics, University of Utah (2006-2007) as Assistant Professor, and Stanford University (2004-2006) as an NSF Postdoctoral Fellow. His research focuses on extracting models from high-dimensional data to reveal essential information, with specific contributions including sampling theorems in compressed sensing using stochastic geometry, efficient algorithms for matrix completion, and theoretical understanding of deep neural networks. Recent interests include neural network initialization techniques to preserve geometric and information-theoretic properties, as well as network pruning methods. Professor Tanner has supervised numerous doctoral students at Oxford and Edinburgh, including Alireza Naderi, Thiziri Nait Saada, Ilan Price, Giuseppe Ughi, Charles Millard, Michael Murray, Simon Vary, Bernadette Stolz, Bogdan Toader, Rodrigo Mendoza-Smith, Ke Wei, Bubacarr Bah, and Andrew Thompson, many of whom have gone on to prestigious positions in academia and industry. His publication record spans over two decades with significant contributions to compressed sensing, matrix completion, and more recently deep learning theory. His work demonstrates a consistent progression from foundational theoretical work to practical applications in signal processing and machine learning. As an academic leader, Professor Tanner serves as Founding Editor-in-Chief of Information and Inference: A Journal of the IMA and has held editorial positions at several prestigious journals including Applied and Computational Harmonic Analysis and IEEE Signal Processing Letters . He has organized numerous conferences and workshops including Prospects in Mathematics and the FoCM Computational Harmonic Analysis workshop.

Petros DellaportasView profile

Professor

• Bayesian Statistics
• Machine Learning
• Financial Econometrics
• +7 more

Petros Dellaportas holds dual appointments as a Professor of Statistical Science at University College London (UCL) and a Professor of Statistics at the Athens University of Economics and Business (AUEB). His research focuses on Bayesian statistics, machine learning, financial econometrics, and dynamic pricing. He leads projects on topics such as Poisson processes for cybersecurity, reservoir computing for macroeconomic forecasting, and probabilistic fault detection in wind parks. His recent publications emphasize advancements in Bayesian methods, variational autoencoders, and spatio-temporal point processes. Dellaportas has supervised over 20 PhD students, contributing to areas like stochastic volatility models and inverse reinforcement learning. He co-founded Thales and Friends, an organization bridging mathematics and cultural activities, and organizes the Greek Stochastics workshop series on topics ranging from causal learning to computational statistics. Key projects include anomaly detection in VAT networks and scalable Gaussian process models. His work often integrates statistical theory with applications in finance, sports analytics, and environmental science. Dellaportas maintains active collaborations with institutions globally, advancing interdisciplinary research and methodological innovations in statistical science.

• Computational Statistics
• Machine Learning
• Generative Modelling
• +7 more

O. Deniz Akyildiz is an Assistant Professor in Statistics at the Department of Mathematics, Imperial College London. His research focuses on computational statistics, machine learning, and generative modelling, with applications to sampling, optimization, and inverse problems. He holds affiliations with the Artificial Intelligence Network and Mathematics research groups. Previously, he obtained degrees in Electronics and Communications Engineering from İTÜ, followed by a PhD in Signal Processing at Universidad Carlos III de Madrid. Before joining Imperial, he worked as a postdoctoral researcher at Warwick CS and The Alan Turing Institute. His research interests span diffusion-based parameter estimation, score-based generative models, Langevin dynamics for optimization, and adaptive importance samplers. Recent work includes contributions to latent diffusion models, Sinkhorn semigroups, and stochastic filtering techniques. Notable publications include works on statistical finite elements, interacting particle Langevin algorithms, and physics-informed deep generative models. His technical blog almost stochastic and GitHub repository provide further insights into his research.

• Quantum Computing
• Computational Complexity
• Communication Complexity
• +3 more

Dr Nikhil Mande is a researcher at the University of Liverpool's Department of Computer Science within the School of Electrical Engineering, Electronic and Computer Science. His work bridges quantum computing and classical complexity theory, with a focus on decision trees, communication complexity, and Fourier-sparse Boolean functions. His research includes groundbreaking work in quantum query complexity and communication complexity, supported by a Royal Society grant (2024-2026) titled "Power of Knowledge in Explorable Uncertainty." He has developed novel frameworks for comparing quantum and classical algorithms and analyzing complexity lower bounds. Key research outputs include studies on quantum sabotage complexity (2024), parity decision trees (2024), and randomized vs deterministic decision tree size (2023). His theoretical work challenges established conjectures, such as the Log-Approximate-Rank Conjecture (2020). He co-authored publications in venues like Quantum journal and LIPIcs conference proceedings. Scientific awards include: Royal Society Grant (2024-2026)

• Statistical Learning
• Machine Learning
• Latent Factor Analysis
• +6 more

Gabriel Wallin is an Assistant Professor (Lecturer) in Statistics at the School of Mathematical Sciences, Lancaster University. He previously held a Research Fellow position at the London School of Economics and Political Science and a postdoctoral role at Inria, France. His PhD was completed in 2020 at Umeå University under Professor Marie Wiberg. Research interests span statistical and machine learning methods for identifying latent structures in social, behavioral, and health data. He explores learning acquisition, health inequalities, public opinion, fairness in educational measurement, and interpretability in psychological testing. Collaborative efforts include economics, psychology, political science, educational testing industries, and NGOs. Publications focus on latent factor models, multivariate outlier detection, model-based clustering, regularization, and change-point detection. Scientific awards include the Best Reviewer Award (Psychometric Society, 2025), ODA Early Career Fellowship (2024), and JSPS Invitational Fellowship (2025). His advising includes PhD student Yawen Ma, with grants from EPSRC STOR-i, Duolingo, and ODA. He is an Associate Editor for Teaching Mathematics and its Applications and joint Editor for Psychometric Society proceedings.

Mark StevensonView profile

Senior Lecturer

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +8 more

Mark Stevenson is a Senior Lecturer in the School of Computer Science at the University of Sheffield, UK. He leads undergraduate programs and serves as a key member of the Natural Language Processing research group , focusing on knowledge extraction from text and user information access solutions. Research Interests : Natural Language Processing Information Retrieval Machine Learning Biomedical Text Disambiguation Lexical Semantics Exploratory Search Systems Scientific Awards : EPSRC Advanced Research Fellowship (2006-2011) Best Paper Award at CLEF 2004 Grants & Projects : He has secured significant funding including the EU FP7 PATHS project (£709,407), EPSRC grants for biomedical disambiguation (£239,920) and Lexical Adaptation (£30,000), and NIHR funding for public health research access systems.

• Power Systems
• Renewable Energy Integration
• Smart Grids
• +3 more

Professor Janusz Bialek is a Principal Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the Control and Power Research Group and the Global Power Systems Transformation Consortium. Previously, he held Full Professor positions at Newcastle University, Durham University, the University of Edinburgh, and Skolkovo Institute of Science and Technology (Skoltech), resigning from Skoltech following Russia's invasion of Ukraine. He also served as a Visiting Professor at Hong Kong University and École Polytechnique Fédérale de Lausanne (EPFL). His research focuses on addressing techno-economic challenges in power systems, particularly the integration of renewable energy sources (e.g., wind, PV, batteries) and Inverter-Based Resources (IBRs). Key areas include smart grids, power system dynamics, blackout prevention, and power market design. He has led multi-million-pound research grants funded by EPSRC and industry, advising UK government, European Commission, and International Energy Agency. Education : Educated in Poland (specific details not disclosed). Affiliations : Energy Systems Integration Group, Global Power System Transformation Consortium, Control and Power Research Group. Awards : Fellow of the IEEE (FIEEE). His work emphasizes grid resilience, renewable energy integration, and transitioning to carbon-free systems. He has contributed to pioneering solutions for decentralized control systems, transmission cost allocation, and stabilizing power systems with high IBR penetration.