María José Madero Ayora is a Professor at the Department of Signal Theory and Communications , Universidad de Sevilla , specializing in nonlinear system modeling and digital predistortion for wireless communication systems. Her research focuses on Volterra series applications in power amplifier linearization, microwave measurements , and machine learning techniques for signal processing. Principal Investigator for projects like Statistical Signal Modeling for Brain-Computer Interfaces (PID2021-123090NB-I00) Recipient of the Arftg Roger Pollard Student Fellowship in microwave measurement Her work spans 5G waveform linearization , I/Q modulator impairments , and thermal memory effects in RF amplifiers. Recent publications combine sparse Bayesian methods with Volterra models to address nonlinear distortion in OFDM and visible light communication systems. She has supervised doctoral theses and participated in international conferences across the U.S., Europe, and Asia.
Sigrunn Holbek Sørbye is a Professor at the Department of Mathematics and Statistics, UiT The Arctic University of Norway. Her research focuses on Bayesian statistics, time series analysis, and spatial data modeling with applications in climatology, ecological statistics, and computational statistics. Institution: UiT The Arctic University of Norway Department: Department of Mathematics and Statistics Research Interests: Dr. Sørbye specializes in Bayesian computation using integrated nested Laplace approximation (INLA), statistical modeling of long-range dependent processes, and applications to climate systems. Her work includes modeling cosmic dust detection rates, analyzing metabolic risk factors, and studying population dynamics through capture-recapture data. Selected Publications Trends: Recent research spans dietary pattern analysis, solar dust modeling, climate sensitivity studies, and ecological monitoring. Key methodologies involve Bayesian hierarchical modeling (2025, 2023), INLA applications (2022, 2020), and long-memory stochastic processes (2020-2019). Collaborative Networks: Dr. Sørbye collaborates on interdisciplinary projects including "Modellering av komplekse systemer" (Complex Systems Modeling) and "Intermittent fluctuations in physical systems." She also contributes to "Transforming ocean surveying by the power of DL and statistical methods." Contact: Tromsø, Norway. sigrunn.sorbye@uit.no | +47 77 64 55 04
Pedro Galeano San Miguel is an Associate Professor at the Department of Statistics, Universidad Carlos III de Madrid. He is affiliated with the Nonparametric Inference for Complex Data and its Applications (NICDA) research group and contributes to the Flores de Lemus Institute and UC3M-Santander Big Data Institute. His work bridges statistics, computer science, and economics with a focus on financial and high-dimensional data. Research Interests: Functional data analysis and outlier detection Bayesian nonparametric methods and stochastic volatility models Copula models for systemic risk and portfolio selection High-dimensional statistical inference and dynamic correlation Big data applications in economics and finance Publication Trends: His recent work (2024–2016) emphasizes copula models for financial risk, functional regression techniques with missing data, and Bayesian inference for high-dimensional time series. He explores systemic banking risks, volatility prediction, and correlation structure changes across economic and financial domains. Grants & Projects: He leads or contributes to projects on computational statistics for complex dependencies, big data customer network analysis, and multivariate asymmetric GARCH modeling, funded by institutions like the State Research Agency (AEI) and Banco Santander.
Max Hinne is an assistant professor at the Department of Artificial Intelligence, Radboud University, Nijmegen, The Netherlands, where he leads the Uncertainty in Complex Systems research group. His work bridges artificial intelligence, neuroscience, and statistics through advanced Bayesian methodologies. Dr. Hinne's research focuses on Bayesian modeling of brain networks using neuroimaging data. His primary interests include: Bayesian nonparametric models, particularly Gaussian processes Structural and functional brain connectivity analysis Predictive modeling of neural systems Causal inference frameworks Uncertainty quantification in complex systems Development of computational tools for neuroscience His approach emphasizes how probabilistic methods can address uncertainty in complex biological systems while providing interpretable models of brain function. Analysis of Dr. Hinne's publication trajectory reveals a consistent focus on Bayesian methods applied to increasingly diverse domains. Starting with foundational work in brain connectomics, his research has expanded to include applications in developmental psychology, medical genetics, and educational technology. His most recent work demonstrates sophisticated integration of nonparametric Bayesian methods with domain-specific challenges, particularly in handling uncertainty in complex, high-dimensional data across multiple scientific fields. Dr. Hinne actively mentors students and invites master's thesis projects focused on Bayesian nonparametric methods for neuroimaging data. He has developed several software tools including the Bayesian Connectomics Toolbox (BaCon), latent space modeling code, and GP CaKe for causal inference. His research group maintains strong connections with the Donders Institute for Brain, Cognition and Behaviour, facilitating interdisciplinary collaborations between statisticians, neuroscientists, and domain experts.
Gianluca Mastrantonio is an Associate Professor at the Department of Mathematical Sciences (DISMA) of the Polytechnic University of Turin , Italy. He is a member of the Interdepartmental Center SmartData@PoliTO and actively contributes to the Statistics and Data Science research group. His academic roles include teaching in PhD programs (Mathematical Sciences, 2023-2025) and master's courses such as Statistical Methods in Data Science and Statistical Models/Statistical Learning . His research focuses on Bayesian Statistics and Hierarchical Models , with applications spanning Biostatistics , Environmental Monitoring , Machine Learning , and Computational Statistics . Key keywords include RNA Velocity , Sea Climate Analysis , Animal Movement Modeling , and Bayesian Software Development . Selected Scientific Awards Steering Committee Member, Royal Statistical Society (Emerging Applications, 2021-) Effective Member, International Statistical Institute (ISI, 2019-) Effective Member, Graspa (Italy, 2017-) Associate Editor, Journal of Statistical Computation and Simulation (2020-) Key Research Themes Bayesian Modeling of RNA Dynamics Environmental and Wildlife Behavior Analysis Statistical Software Development (Julia/R packages) Climatic Change-Point Detection Integration of Linear/Circular Data in Ecology Applications in Prostate Cancer Diagnostics
Heikki Haario is a Professor in Computational Engineering at the LUT School of Engineering Sciences, LUT University, Lappeenranta. His research focuses on robust Bayesian inference, parameter estimation, and uncertainty quantification in chaotic and stochastic systems. Broad research areas: Bayesian Statistics, Chaotic Dynamical Systems, Gaussian Processes, Machine Learning The 15 most recent publications (2025-2023) demonstrate expertise in computational modeling, data-driven methods, and interdisciplinary applications spanning finance, biology, and engineering. Articles emphasize Bayesian techniques, kernel flows, and neural network integration for solving inverse problems and optimizing predictions in uncertain environments.
Dr Amin Karami is an Associate Professor in the School of Computer Science and Digital Technologies (CDT) at the University of East London, within the School of Architecture, Computing and Engineering. He serves as course leader for MSc Big Data Technologies and leads postgraduate programs, having secured £1.23 million in funding from the Office for Students to develop inclusive AI and Data Science courses for non-STEM and far-STEM graduates. His research spans Artificial Intelligence, Big Data Analytics, Blockchain, and Optimization, with focus on Industry 5.0 applications. Current work addresses federated learning heterogeneity, smart contract security, healthcare fraud detection, and ethical AI implementation. He develops cloud-based platforms for large-scale data processing and computational intelligence solutions for real-world industry challenges. Recent publications demonstrate strong trends in federated learning techniques, blockchain vulnerability mitigation, and big data applications across healthcare, finance, and social media. His work consistently bridges academic research with industry needs through partnerships with Multiverse and Cambridge Spark, emphasizing practical solutions for credit risk assessment, satellite telemetry, and personalized marketing. Scientific recognition includes: UEL Vice-Chancellor & President Impact & Innovation Award for Industry 4.0 readiness (2023) Fellow of the Higher Education Academy (FHEA) Dr Karami actively supervises UG/PG/PhD students while leading curriculum innovation through externally funded projects. His Chainlink Bootcamp initiative connects academia with industry practitioners, and he serves as external examiner and conference program chair. Significant grant achievements include developing diversity-focused STEM pathways that enhance graduate employability through industry-aligned training in AI and Data Science.
Megan Peters is an Associate Professor at the University of California, Irvine, with appointments in both the Department of Cognitive Sciences and the Department of Logic and Philosophy of Science within the School of Social Sciences. She serves as president and co-founder of Neuromatch.io and is a Fellow in the Brain, Mind & Consciousness program at the Canadian Institute for Advanced Research (CIFAR). Her research focuses on the intersection of perception, metacognition, and subjective experience, employing methodologies including fMRI, computational modeling, and artificial intelligence. Peters investigates how humans form metacognitive judgments about their perceptions and decisions, examining the neural and computational mechanisms underlying confidence, uncertainty, and conscious awareness. Her work spans theoretical frameworks in philosophy of science to practical applications in neuroscience methods. Peters' recent publications reveal a strong emphasis on metacognitive processes across various domains, with particular attention to how uncertainty is represented in the brain and communicated through behavior. Her research integrates computational neuroscience with philosophical approaches to consciousness and perception, creating a unique interdisciplinary perspective. She has made significant contributions to understanding the representational geometry of psychological spaces, the dynamics of perceptual decision-making, and the development of novel methods for analyzing neural data. Fellow, CIFAR Brain Mind & Consciousness Program As co-founder and president of Neuromatch.io, Peters has created a global platform for computational neuroscience education that has democratized access to advanced training. Her leadership in organizing large-scale virtual conferences has demonstrated innovative approaches to scientific collaboration across geographical boundaries. Through her work with Neuromatch, she has mentored numerous students and early-career researchers in computational neuroscience methods. Peters maintains active involvement in the consciousness science community, regularly participating in and organizing events such as the Metacognitive Science Satellite meeting and CCN (Cognitive Computational Neuroscience) conferences. Her research laboratory investigates fundamental questions about how the brain generates subjective experiences and metacognitive awareness, with implications for both theoretical understanding and potential clinical applications.
Dr. Jakub Stoklosa is a Senior Lecturer at the School of Mathematics & Statistics, University of New South Wales. He holds a PhD in Applied Statistics (2012) and a BSc (Hons) in Science (2007) from The University of Melbourne. PhD in Applied Statistics, The University of Melbourne (2012) BSc (Hons) in Science, The University of Melbourne (2007) His research focuses include: Analysis of capture-recapture data Estimation of animal abundance Measurement error modeling Model selection for multivariate data Non-parametric smoothing Recent publications emphasize statistical applications in ecology, biodiversity, and environmental science, with methodological contributions to error-in-variables regression and zero-truncated models. Scientific awards: 2018 Australian Museum Eureka Prize top 3 finalist (Burramys Genetic Rescue Team) NSW Office of Environment and Heritage Eureka Prize for Environmental Research (2018) Grants: ARC Discovery Project Grant DP210101923 (2021–2023) for "Innovative statistical methods for analysing high-dimensional counts" with D.I. Warton
Boris Beranger is a Senior Lecturer in Statistics and Data Science at the School of Mathematics and Statistics, UNSW Sydney . He is also a member of the UNSW Data Science Hub (uDASH) and previously served as an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . His research spans theoretical and applied statistics, focusing on Extreme Value Theory (environmental, financial, and insurance applications) and Symbolic Data Analysis (complex/non-standard data structures). Education: PhD in Statistics (Université Pierre and Marie Curie & UNSW, 2016), MSc in Mathematics (Université Pierre and Marie Curie, 2011) Research Trends are evident in: High-dimensional extremal dependence modeling (ExtremalDep package) Spatial extremes and max-stable processes Symbolic/histogram/interval-valued data analysis Composite likelihood and aggregated data methods Tail density estimation via kernel methods Scientific Awards & Grants include: J.B. Douglas Award for Postgraduate Excellence (2014) Multiple ARC ACEMS Research Support Schemes Discovery Project DP220103269 ($405,000) for modeling real-world extremes Supervision covers PhD, Masters, and Honours students in areas like Symbolic Data Analysis, Spatial Extremes, and Statistical Computing. He also co-organized workshops and served as Vice-President (2025-26) of the Statistical Society of Australia's NSW Branch.
Kaniav Kamary is a researcher specializing in Bayesian inference and mixture distribution modeling, affiliated with the Laboratory of Mathematics and Computer Science for Complexity and Systems. Their work bridges computational statistics, probabilistic modeling, and applied mathematics. Research Interests Kamary's research focuses on Bayesian model choice Non-informative prior development Reparameterization techniques for mixture models Statistical validation of computational methods Scientific Contributions Recent publications address methodological challenges in Bayesian inference and applications in health informatics and environmental modeling.
Robert Zeithammer is a Professor of Marketing at the Anderson School of Management , University of California, Los Angeles. He is globally recognized for his expertise in pricing strategies, consumer preference modeling, and auction theory, with a focus on digital marketplaces and quantitative market research. Ph.D. in Management Science, MIT Sloan School of Management, 2003 M.A. in Mathematics, University of Pennsylvania, 1998 B.A. in Economics and Mathematics, University of Pennsylvania, 1998 His research spans participative pricing mechanisms and Bayesian preference measurement . Zeithammer's work combines analytical modeling with structural estimation techniques to analyze digital pricing innovations like eBay auctions and Priceline's NYOP model. Key themes include: Dynamic pricing in sequential auctions Consumer risk preferences in bidding Conjoint analysis methodology Policy implications of pricing algorithms Recent publications focus on algorithmic pricing regulation , abortion policy preferences , and ignition interlock device effectiveness . Scientific awards include the Paul Green Award finalist status (2016) and the German Academic Association for Business Research Best Paper Award (2015).
Professor Rachel McCrea holds a Chair in Statistics at Lancaster University's School of Mathematical Sciences and is a core member of the Data Science Institute. She actively supervises PhD students in statistical methodology development for real-world ecological and conservation challenges, with current research focusing on statistical ecology, multiple systems estimation, and illegal wildlife trade applications. Her research program centers on: Advanced capture-recapture and state-space modeling for population dynamics Multiple systems estimation for hidden populations (wildlife trade/human trafficking) Integrated population modeling for conservation decision-making Bias correction in translocation management and extinction inference Bayesian methods for ecological time-series and demographic analysis Recent publications (2021-2025) reveal strong methodological innovation in ecological statistics, with key contributions to integrated population models, multi-system estimation frameworks, and conservation applications. Her work bridges theoretical statistics with urgent biodiversity challenges, particularly in extinction risk assessment and wildlife trade monitoring. Current research projects include 'New stochastic models to quantify illegal trade' (2023-2024), 'A transdisciplinary approach to inferring the end of political violence' (2022-2023), and 'Modelling removal and re-introduction data for improved conservation' (2022-2023). She supervises PhD student Lucas Howell through the STOR-i Centre for Doctoral Training. Professor McCrea collaborates extensively through Lancaster's Centre of Excellence in Environmental Data Science, Environmental and Ecological Statistics group, Social and Economic Statistics group, and STOR-i Centre for Doctoral Training, driving interdisciplinary approaches to ecological data science.
Manuela Karola Zucknick is a Professor in the Department of Biostatistics at the University of Oslo , where she leads statistical learning research for translational and clinical cancer applications. Her work focuses on integrating multi-omics data for personalized cancer therapies, predicting drug responses, and modeling prognosis. Director of Oslo Centre for Biostatistics and Epidemiology (2023–present) Professor (2022–present) and Associate Professor (2015–2022) at University of Oslo Research Interests span high-dimensional statistical modeling, Bayesian methods for heterogeneous data integration, regularization techniques, and applications in pharmacogenomics. She develops tools for drug combination screens, survival modeling, and risk prediction incorporating prior biological knowledge. Key domains: Biostatistics, Integrative Genomics, Precision Medicine Methodological focus: Bayesian structured variable selection, Penalized Regression Publications demonstrate expertise in pan-cancer transcriptomics, proteomics for pregnancy complications, and machine learning for DNA methylation analysis. Recent work includes Tutorial on Survival Modeling (2024) and Dose-Response Prediction (2023) applied to pharmacogenomic datasets. Collaborations span clinical trials in colorectal cancer nutrition, prostate cancer screening for Lynch syndrome patients, and chronic pain research using molecular profiling.
Peter Grünwald is a Full Professor of Statistical Learning at Leiden University's Mathematical Institute and senior researcher in the machine learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His pioneering work focuses on developing flexible statistical inference frameworks using e-values and e-processes, creating a novel middle ground between Bayesian and frequentist paradigms that allows optional continuation in data analysis. Grünwald's research revolutionizes statistical methodology through: Development of anytime-valid tests and confidence intervals Novel approaches to hypothesis testing with data-driven significance levels E-value based methods for safe sequential testing Applications in clinical trials, causal inference, and high-dimensional data His publications demonstrate consistent focus on developing robust statistical frameworks applicable to flexible experimental designs, with recent work appearing in PNAS, JRSSB, and Annals of Statistics. Research consistently addresses fundamental challenges in statistical evidence evaluation across medical, social, and computational domains. Major Scientific Recognition: ERC Advanced Grant (2024) for revolutionary statistical theory development NWO Top Award (2016) for outstanding contributions to statistics As primary advisor, Grünwald has supervised PhD candidates including Tyron Lardy, Yunda Hao, and Rosanne Turner on topics spanning e-statistics, conditional independence testing, and stratified data analysis. He leads methodological development for the R package 'safestats' and collaborates internationally on statistical foundations.
