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Anton WestveldView profile

Senior Lecturer

• Bayesian Methodology
• Network Analysis
• Game Theoretic Data
• +3 more

Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.

Jack GallantView profile

Professor

• Computational Neuroscience
• Cognitive Neuroscience
• Systems Neuroscience
• +5 more

Jack Gallant is a Professor of Neuroscience at the University of California, Berkeley, where he leads a research laboratory focused on cognitive, systems, and computational neuroscience. His work centers on developing and applying advanced methods for analyzing functional magnetic resonance imaging (fMRI) data, particularly through the Voxelwise Encoding Model framework. Dr. Gallant's research interests span computational neuroscience, cognitive neuroscience, and systems neuroscience, with a particular focus on understanding how the brain represents visual and semantic information. His lab develops cutting-edge tools for brain mapping and neural decoding, creating detailed maps of cortical organization related to visual and language processing. Recent work has emphasized group-level analysis techniques that integrate data across multiple participants while accounting for individual differences. His publication record demonstrates consistent innovation in fMRI methodology, with recent papers focusing on comprehensive frameworks for encoding models, individual differences in brain organization, and high-resolution mapping of semantic representations. Gallant's work bridges theoretical neuroscience with practical applications, resulting in widely used software tools that advance the field of neuroimaging. Sloan Research Fellow (1998) Dr. Gallant has mentored numerous graduate students and postdoctoral researchers, including Emily Meschke who recently completed her PhD. His lab maintains active collaborations and is currently recruiting postdocs to continue developing the Voxelwise Encoding Model framework. The lab also produces educational resources including interactive brain viewers and comprehensive tutorials that have become standard tools in the neuroimaging community.

Giancarlo ManziView profile

Associate Professor

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +5 more

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

• Applied Probability
• Insurance Mathematics
• Finance
• +3 more

Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997

• Applied Statistics
• Quantitative Research Methods
• Medical Statistics
• +7 more

Professor Paul White is a distinguished academic at the University of the West of England (UWE Bristol), serving as Professor of Applied Statistics within the Faculty of Engineering and Technology's Department of Engineering, Design and Mathematics. His work focuses on applying statistical methods to benefit society (ASBOS), with particular emphasis on collaborative research across UWE's applied and life sciences departments and with the National Health Service (NHS). Dr. White holds a BSc, MSc, and PhD, though specific institutions are not mentioned in the provided text. His academic journey has established him as a leading figure in applied statistics education and research methodology. Professor White's research spans multiple domains of applied statistics, with particular expertise in quantitative research methods, research methodology, sample size determination, and the design and analysis of experiments. He maintains a strong ethical focus in quantitative research while applying his skills to medical statistics and multivariate statistics. His work often intersects with healthcare applications, as evidenced by his involvement in clinical trials and medical research collaborations. A notable initiative he co-founded is the DARK ARTS (Design And Research Knowledge for Analysis of Randomised Trials) group, which focuses on advancing methodologies for randomized trials. His extensive publication record shows a clear trend toward interdisciplinary collaboration, particularly in healthcare applications of statistics. The most recent articles demonstrate expertise in clinical trials methodology, medical statistics, and psychological interventions. Many publications involve randomized controlled trials across diverse areas including eating disorders, body image interventions, respiratory medicine, oncology, and obstetrics. This reflects his commitment to 'Applying Statistics for the Benefit of Society' (ASBOS) through rigorous quantitative methods. Professor White is actively involved in mentoring students and colleagues, describing himself as 'a willing coach or mentor.' His teaching portfolio is diverse, including courses on 'GANSTA's' (Good At Numbers and STAts) for mathematics students, quantitative methods for healthcare programs, and professional development courses through the UWE Graduate School. He expresses particular interest in developing new statistical techniques using stochastic simulation and welcomes potential PhD students to collaborate on these research programs. Among his collaborative efforts, the DARK ARTS initiative stands out as a significant research group focused on randomized trials methodology. This team, which includes Dr. Caterina Gentili, Dr. Jason Anquandah, and Dr. Deirdre Toher, represents a concentrated effort to advance statistical approaches to clinical research design and analysis.

• Environmental Epidemiology
• Biostatistics
• Chemical Mixtures and Health Effects
• +3 more

Nina Lazarevic is a Research Fellow at the Australian National University's Centre of Epidemiology for Policy and Practice within the National Centre for Epidemiology and Population Health. She holds a PhD in environmental epidemiology and biostatistics, along with Masters degrees in biostatistics and environmental science. Prior to her academic career, she worked as an econometrician in banking and market research. Research Fellow, Centre of Epidemiology for Policy and Practice PhD in Environmental Epidemiology and Biostatistics Masters in Biostatistics and Environmental Science Her research focuses on statistical methods in environmental epidemiology, particularly Bayesian nonparametric approaches for analyzing health effects of environmental chemical mixtures and exposome-health associations. Key projects include studies on PFAS exposure impacts on maternal and child health, telehealth policy evaluation, and asbestos-related cancer incidence in the ACT. Nina has published extensively on environmental chemical exposure impacts, including PFAS contamination studies (2023-2024) and prenatal exposure effects on fetal growth (2022). Her methodological work includes simulation studies for chemical mixture analysis (2020). Current projects involve: ACT Asbestos Health Study II: Linked Data Project ANU Telehealth in Primary Care Study PFAS Health Study Her work leverages large-scale linked data to inform government policy on cardiovascular disease prevention and chronic disease management.

Kyle ParadisView profile

Lecturer

• Sport Psychology
• Mental Health
• Group Dynamics
• +4 more

Kyle Paradis is a Lecturer in Sport Sociology/Sport Psychology (Mental Health) at the School of Sport, Faculty of Life & Health Sciences, Ulster University, Jordanstown Campus. His work bridges sport psychology, mental health, and physical activity research, with a focus on longitudinal behavioral studies and depressive disorder correlations. Education: PhD in Psychology (Western University, 2014) Master of Education: (2023) Master of Human Kinetics: (University of Windsor, 2010) Bachelor of Arts (Honours): (Laurentian University, 2008) Research interests include: Sport Psychology Mental Health in Sports Contexts Group Dynamics Longitudinal Behavioral Analysis Physical Literacy Adolescent Movement Behavior Depressive Disorders His recent publications emphasize longitudinal studies of organizational stressors, physical activity's role in mitigating depression, and group dynamics in sports officiating. He actively participates in international conferences and serves on program committees for sport psychology events.

Zhenxia LiuView profile

Associate Professor

• Mathematical Statistics
• Probability Theory
• Large Deviations
• +3 more

Zhenxia Liu is an Associate Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). Her work contributes to theoretical and applied probability, with a focus on stochastic processes and statistical modeling. Her research interests lie at the intersection of mathematical statistics and probability theory. Key areas include large deviations , longest runs in Markov chains , and Monte Carlo methods . These topics are central to understanding rare events, sequential dependencies, and numerical estimation techniques in complex systems. The recent publications demonstrate a consistent focus on probabilistic analysis of dependent structures, particularly through Markov models. Her work combines theoretical rigor with applications in computational statistics, showing trends toward improving bounds and simulation efficiency in stochastic modeling. Mathematical Statistics Probability Theory Large Deviations Markov Chains Monte Carlo Methods Computational Mathematics Zhenxia Liu has actively contributed to high-quality journals such as Statistics and Probability Letters , Results in Applied Mathematics , and Probability and Mathematical Statistics . While no formal advising or grant information is available in the provided text, her collaborative publications suggest engagement in research networks within applied mathematics. She is part of the research environment in Applied Mathematics at Linköping University, which focuses on computational mathematics, optimization, and mathematical modeling across science and engineering disciplines.

Arnold PolanskiView profile

Associate Professor

• socio-economic networks
• game theory
• financial economics
• +5 more

Arnold Polanski is an Associate Professor in Economics at the School of Economics, University of East Anglia (UEA), where he is an active member of the Applied Econometrics and Finance, Economic Theory, and Statistics research groups. He is currently accepting PhD students and supervising research in socio-economic networks, game theory, financial economics, and financial tail risk. His academic journey includes a PhD from the University of Alicante, postdoctoral research at the University of Minnesota, and prior teaching at Queen’s University Belfast. PhD in Economics, University of Alicante (2004) Postdoctoral Studies, University of Minnesota (2005) Postgraduate Certificate in Higher Education Teaching, Queen’s University Belfast (2007) Arnold Polanski's research focuses on socio-economic networks , game theory , information economics , and financial tail risk , with a growing emphasis on integrating machine learning into economic modeling. His work explores how network structures influence cooperation, information diffusion, and financial interdependencies, particularly during extreme market events. He investigates the role of homophily, influence, and strategic behavior in shaping economic outcomes. His recent publications (2019–2025) reveal a consistent trend toward analyzing tail risk interdependence , network stability , and information flows using advanced econometric and computational methods. Many of his articles apply machine learning and axiomatic frameworks to bargaining and financial risk, published in journals like Journal of Economic Theory , Journal of Applied Econometrics , and Computational Economics . His work bridges theoretical economics with empirical and computational approaches. Arnold Polanski has received research funding from prestigious institutions including the British Academy and the Institut Europlace de Finance Louis Bachelier . He leads the Economic Theory Group at UEA and serves in key administrative roles such as Plagiarism Officer and Chair of the Faculty Appeals and Complaints Panel. He actively contributes to the academic community as co-organizer of an annual international workshop on the economics of networks. His research supervision includes PhD projects on socio-economic networks, game theory, and financial tail risk. He collaborates with scholars such as E. Stoja, F. Vega-Redondo, and J. Sikora, and his work often involves interdisciplinary methods combining economics, statistics, and computer science. Arnold Polanski is involved in the Economic Theory Group and contributes to collaborative research within UEA’s School of Economics. His projects emphasize network-based modeling, financial risk analysis, and the application of machine learning in economic contexts. He fosters academic exchange through organizing international workshops and leading research initiatives focused on the intersection of networks and economic behavior.

• Chronic Rhinosinusitis
• Olfactory Dysfunction
• Vitamin D Metabolism
• +5 more

Jennifer K. Mulligan is an Associate Professor in the Department of Otolaryngology – Head & Neck Surgery at the University of Florida. She holds multiple leadership roles including Vice Chair for Research, Co-Director of the UF Health Smell Disorders Program, and Director of Resident Research Education. Her academic work focuses on the immunopathogenesis of chronic rhinosinusitis (CRS) and olfactory dysfunction, with a particular interest in vitamin D metabolism, complement system regulation, and epithelial cell biology. Her research employs experimental models and clinical studies to investigate mechanisms underlying sinonasal inflammation and olfactory impairment. Notable contributions include elucidating roles of vitamin D3 in modulating immune responses and epithelial function, and developing biomarker-based endotyping strategies for CRS subtypes. She has pioneered nasal mucus proteomics and olfactory cleft analysis to identify novel therapeutic targets. Dr. Mulligan's grant portfolio includes NIH-funded projects on vitamin D modulation of CRS inflammation, immune responses to SARS-CoV-2 in upper airways, and biologic therapies for nasal polyposis. She leads clinical trials evaluating treatments like dupilumab for smell recovery. Over 100 peer-reviewed publications span immunology, otolaryngology, and translational research methodologies. Her teaching activities include instructing the GMS6847 course on translational research principles. She mentors residents and fellows in surgical research through structured educational programs. Active collaborations involve immunologists, bioengineers, and clinical researchers across multiple institutions.

Javier CabreraView profile

Professor

• Biostatistics
• Data Mining
• Clinical Trial Methodology
• +5 more

Javier Cabrera is a Professor in the Department of Statistics at Rutgers University with a joint affiliation at the Cardiovascular Institute. He holds a Ph.D. from Princeton University and is recognized as a Fulbright Scholar. His office is located at Hill Center 471, 110 Frelinghuysen Road, Piscataway, NJ. His research focuses on: Biostatistics and clinical trial methodology Data mining for functional genomics and DNA/protein arrays Statistical computing, machine vision, and high-dimensional data analysis Cardiovascular health applications using statistical modeling Recent publications (2022-2025) demonstrate strong emphasis on: Novel statistical methods for medical/biological data Machine learning applications in diagnostics and genomics Clinical risk modeling and epidemiological studies Big data reduction techniques and computational efficiency He frequently publishes in interdisciplinary collaborations at the intersection of statistics, biomedicine, and computational science. Awards: Fulbright Scholar He collaborates extensively with the Cardiovascular Institute, contributing statistical expertise to research on cardiovascular outcomes, disease risk modeling, and clinical data analysis.

Shuhab D KhanView profile

Professor

• Remote Sensing
• Geospatial Research
• Hyperspectral Imaging
• +5 more

Dr. Shuhab D Khan is a Full Professor in the Department of Earth and Atmospheric Sciences at the University of Houston's College of Natural Sciences and Mathematics. He leads the Remote Sensing and Geospatial Research Laboratory (GeoRS), focusing on advanced geospatial techniques for earth science applications. His research integrates remote sensing, geospatial analytics, and field geology to address critical geological and environmental challenges. PhD in Geoscience from The University of Texas at Dallas (1996-2001) Dr. Khan specializes in hyperspectral imaging, land subsidence analysis, tectonic studies, and rare earth element exploration. His work bridges traditional geological field methods with cutting-edge remote sensing technologies, particularly in developing innovative spectral indices for mineral identification. He has made significant contributions to understanding active tectonics in the Himalayas and hydrogeological systems in North Africa. His recent publications show a strong trend toward multi-scale analysis of geological phenomena, from microscopic mineral identification to regional tectonic studies. He has pioneered techniques using drone-based hyperspectral imaging for rare earth element detection and developed novel approaches for analyzing land subsidence in urban areas using InSAR technology. Dr. Khan actively mentors students through research projects and leads the Remote Sensing and Geospatial Research Laboratory. His laboratory work focuses on developing practical applications of geospatial technologies for resource exploration, environmental monitoring, and natural hazard assessment. He has secured research funding for projects related to coastal erosion monitoring, subsidence analysis, and mineral exploration.

• Latent Variable Modeling
• Statistical Learning
• Algorithmic Fairness
• +4 more

Dr. Gabriel Wallin is an Assistant Professor in Statistics at the School of Mathematical Sciences, Lancaster University. Previously, he held postdoctoral positions at Inria and a Research Fellow role at the London School of Economics and Political Science, where he was supervised by Dr. Yunxiao Chen and Professor Irini Moustaki. His academic journey began with a PhD from Umeå University in 2020 under Professor Marie Wiberg. Education: PhD in Statistics, Umeå University (2020) Supervisor: Marie Wiberg Dr. Wallin's research bridges latent variable modeling and statistical learning, focusing on fairness and interpretability in social, behavioral, and health sciences. Key areas include structural learning of latent models, multivariate outlier detection, model-based clustering, algorithmic fairness in education, and change-point detection for latent factors. His methodology development addresses test score equating, robustness analysis, and sparse loadings in factor models. Recent publications highlight trends in propensity score methods for test equating (2025), latent change-point modeling for educational assessment (2025), and sparse rotation techniques for exploratory factor analysis (2023). Collaborative work spans educational testing, psychometrics, and social data science. Scientific Awards: Best Reviewer Award, Psychometric Society (2025) ODA Early Career International Fellowship (2024) Dr. Wallin supervises PhD student Yawen Ma and actively collaborates with researchers like Yunxiao Chen and Marie Wiberg. He has secured grants from EPSRC, Duolingo, and the ODA, with recent projects on test security and fairness in digital learning environments. His service roles include co-editing Psychometric Society proceedings and associate editorships at Teaching Mathematics and its Applications .

Paolo OliveriView profile

Professor

• Analytical Chemistry
• Chemometrics
• Pharmaceutical Chemistry
• +7 more

Paolo Oliveri is a Full Professor at the Department of Pharmacy (DIFAR) within the University of Genoa. His work focuses on analytical chemistry, chemometrics, and their applications in pharmaceutical, forensic, and environmental sciences. He teaches courses including Analytical Chemistry (CTF), Forensic Analytical Chemistry, and Instrumentation and Quality Control. Research Highlights: He applies chemometric methods and spectroscopic techniques (NIR, TD-NMR) to analyze material properties across diverse domains—from pharmaceutical raw materials to archaeological dating. His recent work explores hyperspectral imaging for ecological studies and multivariate analysis of rare-earth element profiles in soil science. Contact: Available at the Chemistry and Pharmaceutical and Food Technologies Section of DIFAR (Viale Cembrano, 4) or via email at paolo.oliveri@unige.it. Appointments require prior coordination through his email.

• Sufficient Dimension Reduction
• Variable Selection
• High Dimensional Analysis
• +1 more

CHEN Xin is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech), where he has been employed since January 2019. He initially joined the Department of Mathematics before transitioning to his current department in August 2019. Prior to SUSTech, he held academic positions as Assistant Professor at the National University of Singapore (2011-2019) and Syracuse University (2010-2011). Education History: PhD in Statistics from University of Minnesota, USA (2005-2010) MS in Statistics and Applied Probability from National University of Singapore (2001-2003) BS in Mathematics from Nankai University, China (1994-1999) Research focuses on developing statistical methodologies for high-dimensional and complex data analysis. Primary research domains include: sufficient dimension reduction techniques for efficient data representation; variable selection methods for high-dimensional models; robust statistical approaches for complex datasets; and optimization of computational efficiency in statistical estimation. His work consistently addresses theoretical foundations while developing practical implementations for modern data challenges. Publications demonstrate sustained focus on statistical methodology development, particularly in dimension reduction and high-dimensional analysis. Recent works emphasize robust methods for ultrahigh-dimensional data, efficient computation algorithms, and model diagnostics. Research consistently appears in top-tier statistics journals including Biometrika, Journal of the American Statistical Association, and Annals of Statistics. No scientific awards, student advising relationships, or research grants are mentioned in the source materials. Similarly, there is no information available regarding laboratory affiliations or research team composition.