Dr Stuart Barber is a Senior Lecturer in Statistics at the School of Mathematics, University of Leeds, affiliated with the Faculty of Engineering and Physical Sciences. His expertise spans wavelet methods, statistical bioinformatics, and applied statistics, with interdisciplinary applications in fields such as industrial tomography, spatial data analysis, and genomics. His research focuses on wavelet-based statistical techniques, Bayesian inference, and computational methods. Recent applications include clustering, phylogenetics, anaesthesiology, and copy-number genomic data segmentation. Research trends from his publications highlight advanced methodologies like Gaussian process modeling, Voronoi tessellations, and Approximate Bayesian Computation (ABC), alongside bioinformatics-driven studies on protein alignment and structure analysis. Responsibilities include serving as Head of the Department of Statistics. He actively supervises postgraduate researchers, including Fatih Gezer, and acknowledges support from the Simons Foundation. His work is associated with the Statistics research group, fostering collaborations and PhD opportunities in diverse statistical domains.
Michael Bronstein is the DeepMind Professor of Artificial Intelligence at the University of Oxford and Founding Scientific Director at the Aithyra Institute. He holds affiliations with Imperial College London (previous) and institutions like Stanford, MIT, and Harvard. His research focuses on geometric deep learning, graph neural networks, protein design, and non-human species communication. Bronstein received his PhD from the Technion in 2007 and has been awarded multiple fellowships and grants, including ERC, Google, and Amazon awards. Education: PhD in Computer Science, Technion, 2007 Research Interests: His work spans geometric deep learning, graph neural networks, 3D shape analysis, and applications in protein design. Notable projects include protein interaction design using surface fingerprints and advancing graph neural network architectures. He also explores AI in non-human communication, combining machine learning with biological systems. Publications: Recent work emphasizes knowledge graph foundation models, graph homomorphism analysis, and generative models for discrete data. His research bridges theoretical foundations (e.g., graph expressivity) with practical applications in biomedicine and AI. Awards: EPSRC Turing AI Fellowship Royal Society Wolfson Research Merit Award Academia Europaea Membership IEEE/IAPR/ELLIS Fellowships Advising & Grants: Supervises students in AI and graph learning. Active in securing ERC, Google, and industry grants. His entrepreneurial ventures include founding companies like Fabula AI (acquired by Twitter). Labs/Teams: Leads Graph Learning Research at DeepMind and collaborates with interdisciplinary teams in AI, biology, and quantum systems.
Dr. Yogendra P. Chaubey is a Professor in the Department of Mathematics and Statistics at Concordia University, Montréal, Canada. Holding a Ph.D. from the University of Rochester (1977), his research focuses on statistical methodology with emphasis on sampling theory, distribution modeling, and survival analysis. Education: Ph.D., University of Rochester (1977) His work spans nonparametric density estimation for circular and non-negative data, wavelet-based noise reduction in biological imaging, entropy estimation, left-censored spatial modeling (e.g., arsenic contamination), and inverse Gaussian distributions. Recent collaborations include applications in protein classification and public health. Dr. Chaubey has published extensively on statistical theory and methodology, including corrections and extensions to classical estimators. His teaching includes advanced courses in sample survey theory and applications.
Euan McGonigle is a Lecturer in Statistics at the University of Southampton, affiliated with the Department of Mathematical Sciences and the Statistical Sciences Research Institute (S3RI). He holds a PhD in Statistics and Operational Research from Lancaster University (2020), an MRes in the same field (2017), and an MSci in Mathematics from the University of Glasgow (2016). His research focuses on nonstationary time series analysis, change point detection, and multiscale statistical methods, particularly involving wavelet-based techniques for analyzing complex temporal data. His work emphasizes developing robust methodologies for segmenting time series data, analyzing locally stationary processes, and addressing nonstationarity in statistical models. He has contributed to software packages like TrendLSW and CptNonPar, which implement his research into practical tools for data analysis. McGonigle currently supervises PhD students in Mathematical Sciences and is accepting new PhD applications. Key areas of application include signal processing, financial time series, and environmental data analysis. His recent publications highlight advancements in wavelet-based trend estimation, multiscale variance analysis, and nonparametric change point detection techniques for multivariate settings.
Dr. Gery Geenens is a Senior Lecturer in the School of Mathematics & Statistics at UNSW Sydney, specializing in nonparametric and semiparametric statistical methods. He holds a PhD in Sciences (Statistics) from Université catholique de Louvain (Belgium, 2008) and joined UNSW in 2009 after postdoctoral work at the University of Melbourne under Professor Peter Hall. His research focuses on developing flexible nonparametric models for statistical analysis, with particular expertise in copula modeling, functional data analysis, and high-dimensional density estimation. Key contributions include: Nonparametric copula-based conditional density estimation Wavelet-based multivariate density estimation with shape preservation Applications in sports analytics (e.g., football/soccer outcome modeling) Solutions to the "Curse of Dimensionality" in functional regression Recent publications demonstrate strong activity in dependence modeling and theoretical foundations of nonparametric statistics, with significant work on Sklar's theorem and Hellinger correlation. His research bridges theoretical advances with practical applications in finance, hydrology, and sports analytics. As Director of Postgraduate Studies (Coursework) and Associate Editor of "Statistics and Probability Letters", he actively contributes to academic service. His teaching portfolio includes core statistics courses for engineering and advanced probability. Current research is supported by UNSW Faculty Research Grants (2014-2016) and prior Early Career Research Grants (2010-2013). He co-supervises PhD candidates including Carlos Aya Moreno on wavelet-based density estimation.
Professor Zeynep Yasemin Kahya is a distinguished academic in the Department of Electrical and Electronics Engineering at Boğaziçi University's Faculty of Engineering. With a career spanning over three decades at Boğaziçi University, she has progressed from Assistant Professor to her current position as Professor. She has held significant administrative roles including Department Chair (2012-2021) and Deputy General Secretary (2008-2012). Dr. Kahya's research focuses on biomedical signal processing, particularly lung acoustics and respiratory sound analysis. PhD in Biomedical Electronics, Boğaziçi University (1981-87) MS in Electrical Engineering, Yale University (1980-81) BS in Electrical Engineering & Physics, Boğaziçi University (1976-80) Professor Kahya's research interests center on biomedical signal processing with specific emphasis on lung acoustics, respiratory sound analysis, and digital stethoscope design. Her work combines advanced signal processing techniques like wavelet transforms with clinical applications for pulmonary disease diagnosis. She has pioneered methods for pulmonary sound classification, crackle and wheeze detection, and respiratory sound parameterization. Her laboratory, the Lung Acoustics Laboratory (LAL), develops "smart stethoscope" diagnostic systems based on respiratory sounds. Her publications demonstrate a consistent research trajectory in respiratory sound analysis, with recent work focusing on machine learning approaches for pulmonary disease diagnosis. Professor Kahya has successfully translated her research into practical applications, including patented technologies for auscultation data acquisition systems and founding Electrosalus Biyomedikal, a company commercializing smart stethoscope technology. TÜBİTAK high school, undergraduate, and doctoral scholarships (1973-84) Fellowships from MIT, Yale, Stanford, and Caltech for undergraduate studies (1976) Fellowships from Yale and Princeton for doctoral studies (1980) Multiple research grants from Boğaziçi University Research Fund TÜBİTAK TEYDEP grants for smart stethoscope development Professor Kahya has supervised numerous PhD and Master's students, many of whom have worked on projects related to respiratory sound analysis and biomedical instrumentation. She has led multiple research projects funded by Boğaziçi University and external agencies like TÜBİTAK. Her laboratory, the Lung Acoustics Laboratory (LAL), brings together students from various disciplines to work on hardware and software projects related to "smart stethoscope" diagnostic systems.
Odd Kolbjørnsen is an Associate Professor at the University of Oslo's Department of Mathematics, affiliated with the Faculty of Mathematics and Natural Sciences. His primary research focuses on geophysics, seismic inversion, Bayesian statistics, and data-driven reservoir modeling. He holds a strong academic background in mathematical geosciences, with contributions to methodologies like Bayesian inversion, Markov mesh modeling, and deep learning applications in geoscience. His work bridges traditional geophysical analysis with modern computational techniques, emphasizing uncertainty quantification and high-resolution imaging in reservoir characterization. Key research areas include seismic data reconstruction, multi-task neural networks for flow metering, and 4D seismic inversion for lithology-fluid prediction. His publications span journals like Geophysics , Mathematical Geosciences , and Neural Networks , reflecting interdisciplinary collaborations in geostatistics and energy engineering. No scientific awards are explicitly listed, though his extensive publication record underscores his research impact. His advisory role and involvement in research groups like 'Statistics and Data Science' at UiO highlight his academic leadership. Ongoing work focuses on integrating machine learning with geophysical data analysis and CO2 sequestration optimization.
Janusz A. Starzyk is a Professor of Electrical Engineering and Computer Science at Ohio University's Russ College of Engineering and Technology, and holds a concurrent professorship at the University of Information Technology and Management in Rzeszów, Poland. He earned his M.Sc. and Ph.D. in Electrical Engineering from Warsaw University of Technology and a habilitation from Silesian University of Technology. His research focuses on embodied intelligence, machine learning, neural networks, and VLSI design, with notable contributions to self-organizing systems and reinforcement learning. Starzyk has supervised 47 M.Sc. and 16 Ph.D. students, and his work spans over 190 peer-reviewed publications. He leads the Embodied Intelligence Lab at Ohio University and collaborates with institutions worldwide, including Nanyang Technological University and the Institute for Artificial Intelligence in Switzerland. His awards include the Best Research Paper Award from Ohio University and nominations for IEEE Fellow. He has secured $3.8 million in research grants, with projects in GPS signal processing, radar target recognition, and machine learning applications. His patents include innovations in object identification systems and self-organizing learning hardware. Starzyk's expertise bridges academia and industry, with roles as a consultant for companies like Magnolia Broadband and Sarnoff Research.
Ran Wei is a researcher at the University of Science and Technology of China, Department of Chemistry, with extensive contributions across interdisciplinary domains. His work bridges Computer Science , Biomedical Imaging , and Transportation Systems , focusing on active inference models, digital twins, and multimodal learning. Academic Affiliation : University of Science and Technology of China, Department of Chemistry Research Themes : Ran Wei's research explores active inference for adaptive systems, multimodal fusion in computer vision, and domain-specific knowledge graphs for engineering applications. He also investigates federated learning on heterogeneous networks and automated safety analysis for cyber-physical systems. Recent Article Trends : His publications from 2025–2021 span AI-driven engineering (e.g., construction project management), biomedical imaging (lesion detection, radiomics), and transportation systems (driver behavior modeling). Methodologically, he integrates transformers , Bayesian inference , and active learning frameworks. Scientific Collaborations : Ran Wei collaborates with experts in autonomous vehicles (Alfredo García, Anthony D. McDonald), biomedical engineering (Qiaolin Ye, Yifan Cai), and safety-critical systems (Tim Kelly, Simon Foster).
Paulo Félix Lamas is a Full Professor at the University of Santiago de Compostela (USC), specializing in Computer Science and Artificial Intelligence. He obtained his PhD in Physics from USC in 1999 and became an Associate Professor in 2002. As a foundational leader of CiTIUS (Research Centre in Intelligent Technologies), he served as Head (2010–2019) and Deputy Director (2019–2020). His research focuses on probabilistic learning , temporal abductive reasoning , and biomedical signal interpretation . Key applications include ECG analysis, glucose monitoring, and AI-driven healthcare solutions. His work bridges machine learning with clinical diagnostics, emphasizing interpretability and real-time systems. Recent publications (2015–2024) demonstrate interdisciplinary innovation, spanning: Healthcare AI : Personalized drug dosing, arrhythmia detection, and continuous glucose monitoring. Computational methods : Stochastic embeddings, kernel-based missing data handling, and adaptive clustering. Theoretical advances : Abductive reasoning frameworks for time-series interpretation. He leads major projects like: SOSFood (2024–2028): AI for sustainable food systems. XAI4SOC (2022–2025): Explainable AI for healthy aging. INSIDE (2022–2024): Predictive cardiac rehabilitation. At CiTIUS, he directs research on intelligent healthcare technologies, integrating sensor data with machine learning for chronic disease management.
Felix Abramovich is a Professor in the Department of Statistics and Operations Research at Tel Aviv University. His research focuses on theoretical foundations of statistical and machine learning, specializing in high-dimensional inference, sparsity, model selection, and nonparametric estimation techniques. Professor Abramovich's work spans statistical learning theory, classification methods, wavelet-based estimation, and inverse problems. His research develops minimax-optimal procedures for sparse high-dimensional data and explores the interplay between Bayesian methods and frequentist optimality. Key themes include adaptive estimation under sparsity constraints, theoretical analysis of classification algorithms, and wavelet applications in nonparametric statistics. Analysis of Abramovich's recent publications reveals a strong emphasis on classification methodologies, high-dimensional regression, and deep learning theory. His work consistently develops minimax-optimal procedures, examines sparsity patterns in complex data structures, and bridges Bayesian and frequentist frameworks. The research demonstrates increasing focus on modern machine learning challenges including neural network theory and multiclass classification. Professor Abramovich actively recruits students and post-doctoral researchers with strong mathematical statistics backgrounds to work on fundamental problems in statistical learning theory and high-dimensional inference.
Haotian Xu is a Harrison Early Career Assistant Professor in the Department of Statistics at the University of Warwick. His research focuses on nonparametric estimation in high-dimensional settings, change point detection, and inference under temporal dependence and contamination. He holds a Ph.D. from the University of Geneva (2021) and an MSc from the University of Illinois at Urbana-Champaign (2015). His postdoctoral work involved collaborations at institutions like Pennsylvania State University and Université Catholique de Louvain. Key research areas include robust statistical methods, wavelet-based inference, and applications in sensor calibration and functional data analysis. He has developed R packages such as 'changepoints' and 'FragmentCP' for change point detection. Teaching responsibilities include Bayesian Forecasting and Intervention at Warwick and courses in statistical modeling at Penn State and the University of Geneva. His publications span high-impact journals like the Annals of Statistics and IEEE Transactions, addressing challenges in time series analysis, multivariate modeling, and computational statistics. Current projects emphasize scalable inference for large datasets and fragmented functional data analysis.
Ed Cohen is an Associate Professor in Statistics at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). His research focuses on statistical methodologies for analyzing signals and images, including point processes, time series analysis, and change-point detection. Applications span biological imaging, networks, and manifold-based domains. He leads the EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) and contributes to the EPSRC NeST Programme. His work integrates mathematical theory with computational tools for microscopy data analysis and interdisciplinary collaborations. Education & Professional Roles: Joint Director, EPSRC StatML CDT (Imperial/Oxford) Investigator, EPSRC NeST Programme Associate Editor, Statistics and Computing Vice-Chair, Royal Statistical Society's Emerging Applications Section Research Interests: Statistical methods for spatial and temporal data Bioimaging applications (e.g., super-resolution microscopy) Network analysis and community detection Bayesian online estimation and changepoint detection Interdisciplinary projects in neuroscience and microbiology PhD Opportunities: Current openings focus on computational/statistical methods for neuronal protein organization using microscopy data, with collaboration between Imperial College London and Bordeaux's Institute of Interdisciplinary Neuroscience. Labs & Teams: Active in Mathematics in Medicine and Time Series/Signal Processing groups at Imperial, contributing to collaborative projects bridging statistics, computer science, and biology.
Mário A. T. Figueiredo is an IST Distinguished Professor and Feedzai Professor of Machine Learning at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST) . He leads the Lisbon Unit for Learning and Intelligent Systems (LUMLIS) within ELLIS and coordinates research clusters at Instituto de Telecomunicações. His research focuses on machine learning , signal processing , and image restoration , particularly using sparsity-based methods , wavelet transforms , and Bayesian algorithms . Recent work includes 3D shape correspondence , hyperspectral sharpening , and adaptive optimization techniques . Key article themes: Biclustering , ADMM algorithms , sparsity regularization , and generative embeddings Scientific recognition: EURASIP Fellow , Clarivate Highly Cited Researcher , IEEE/IAPR Fellow , and multiple best paper awards He has mentored numerous PhD/MSc students and contributed to information-theoretic kernels , feature discretization , and multimodal data analysis in biomedical and computer vision applications.
Raymond Ka Wai Wong is an Associate Professor and Director of the PhD Program in the Department of Statistics at Texas A&M University. He holds a PhD in Statistics from the University of California, Davis (2014), an MPhil from The Chinese University of Hong Kong (2010), and a BSc with minors in Mathematics and Risk Management Science (2008). His research focuses on causal inference, functional data analysis, low-rank modeling, reinforcement learning, and statistical learning with applications in astronomy, brain imaging, and genomics. Wong's professional roles include Associate Editor for Journal of Computational and Graphical Statistics , Journal of the American Statistical Association , and member of the Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He has secured grants from NSF, NASA, and NIH, including leadership in projects like Virtual Assistant for Spacecraft Anomaly Treatment and phenomic selection in maize hybrids. His awards include Top Reviewer distinctions at NeurIPS (2023) and ICML (2020), and a 2016 Discussion Paper in the Annals of Applied Statistics. He has advised numerous doctoral students, with notable advisees receiving awards such as the ICSA Student Paper Award and Emanuel Parzen Fellowship. Wong’s recent work emphasizes methodological advancements in reinforcement learning (e.g., distributional off-policy evaluation) and matrix/tensor completion under informative missingness. His research bridges theoretical statistics with practical applications in interdisciplinary domains such as neuroscience, agriculture, and space exploration.
