جست‌وجوی استاد و پژوهشگر

Toryn Schaferمشاهده پروفایل

استادیار

• Spatio-temporal statistics
• Reinforcement learning
• Bayesian statistics
• +۵ مورد دیگر

Toryn Schafer is an Assistant Professor in the Department of Statistics at Texas A&M University, holding the 2024 ConocoPhillips Data Science Faculty Fellowship. Previously, he was a postdoctoral associate at Cornell University's Department of Statistics and Data Science, contributing to the NSF-funded PRISM Institute for Trans-domain Systemic Risk. His academic journey includes a PhD in Statistics (2020) from the University of Missouri, an MA in Statistics (2018), and a BSc in Statistics & Wildlife Biology (2014) from Colorado State University. His research interests focus on spatio-temporal statistics, reinforcement learning, Bayesian methods, and machine learning, applied to ecological and environmental systems. He emphasizes interdisciplinary approaches to address challenges in energy, sports analytics, and biodiversity conservation. His recent work explores advanced statistical methods for trend analysis, changepoint detection in time series, and the integration of community science data for ecological modeling. Notable contributions include Bayesian inverse reinforcement learning frameworks for animal behavior analysis and studies on renewable energy impacts on electricity markets. Education: PhD in Statistics, University of Missouri (2020) MA in Statistics, University of Missouri (2018) BSc in Statistics & Wildlife Biology, Colorado State University (2014) Research Themes: His publications reflect a trajectory toward methodological advancements in spatio-temporal modeling, with applications ranging from animal movement patterns to energy grid resilience. Recent articles highlight critical risk indicators for power systems and biodiversity status metrics inspired by financial portfolio theory. Grants & Fellowships: 2024 ConocoPhillips Data Science Faculty Fellowship (Texas A&M University). Labs/Teams: While specific lab affiliations are not detailed, his work intersects with interdisciplinary teams focused on ecological data science and systemic risk analysis.

Eric Chiمشاهده پروفایل

دانشیار

• Statistical Learning
• Numerical Optimization
• Biological Science Applications
• +۳ مورد دیگر

Eric Chi is an Associate Professor in the Department of Statistics at Rice University, also affiliated with the Ken Kennedy Institute. He holds a B.A. in Physics from Rice University, an M.S. in Electrical Engineering from UC Berkeley, and a Ph.D. in Statistics from Rice University. His research focuses on statistical learning and numerical optimization, applied to large-scale data in biological and engineering domains. He has been recognized with awards including the NSF CAREER Award (2018) and the LeRoy and Elva Martin Teaching Excellence Award (2020). Chi's academic journey includes postdoctoral roles at UCLA and Rice, and prior faculty experience at North Carolina State University (2015-2021). He serves as an Associate Editor for the Journal of Computational and Graphical Statistics since 2016 and on the Editorial Board of Statistical Methods in Medical Research since 2011. His work emphasizes algorithmic development for complex data structures, including tensor decomposition, matrix completion, and convex clustering. Recent research trends include robust low-rank tensor decomposition, Bayesian inference via proximal MCMC, and convex-nonconvex regularization strategies. His publications span statistical computing, machine learning, and interdisciplinary applications. Chi advises current PhD students at Rice and has mentored multiple alumni now in academia and industry roles.

Wicher Bergsmaمشاهده پروفایل

استاد

• Statistical Modeling
• Machine Learning
• Bayesian Statistics
• +۷ مورد دیگر

Professor Wicher Bergsma is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on statistical modeling techniques such as I-priors, reproducing kernel methods, and empirical Bayes approaches. Notably, he co-developed the tau-star test for independence and advanced marginal models for categorical data analysis. He has held academic positions at LSE since 2005, preceded by postdoctoral work at Eurandom and Tilburg University, and a stint as an Assistant Professor at the Central European University. His applied work includes collaborations with industry partners like GlaxoSmithKline and Flextronics on clinical trials and fault detection systems. He is currently on sabbatical until Spring 2025. Education includes a Mathematics degree from Leiden University and a PhD in Social Statistics from Tilburg University. His expertise spans theoretical and applied statistics, with contributions to graphical models, conditional independence testing, and Bayesian variable selection. He has authored influential books and R packages, including Marginal Models and the cmm package for categorical marginal modeling. Research interests emphasize bridging theory and practice, particularly in developing robust statistical tools for complex data structures. His recent work explores I-prior methodologies for regression, causal inference with stochastic confounders, and scalable Gaussian process models for environmental data analysis.

Soonwoo Kwonمشاهده پروفایل

استادیار

• Econometric Theory
• Applied Econometrics
• Robust Methods
• +۴ مورد دیگر

Soonwoo Kwon is an Assistant Professor of Economics at Brown University, specializing in econometric theory and applied econometrics with a focus on robust methods. His work addresses estimation techniques in panel data models, shrinkage estimation, and measurement error correction. He has contributed to the development of the FEShR R package implementing shrinkage estimators for fixed effects models. Research spans topics like bias-aware inference, regression discontinuity designs, and parallel trends analysis, with publications in journals such as Econometrica , Review of Economic Studies , and Quantitative Economics . His research interests emphasize methodological rigor, including regularization in regression models and diagnostics for misspecified models. Collaborations include work with Timothy Armstrong, Michal Kolesár, and Sokbae Lee. Kwon's GitHub contributions reflect active development in statistical software, particularly in C++ and R for econometric applications.

Simon Shawمشاهده پروفایل

مدرس ارشد

• Bayesian Statistics
• Bayes Linear Methods
• Graphical Models
• +۵ مورد دیگر

Simon Shaw is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath and a member of the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa). His research focuses on Bayesian approaches, including Bayes linear methods, graphical models, and conditional independence analysis. He also explores operational research topics like nonparametric predictive inference for component replacement strategies. His research interests include the analysis of second-order exchangeable sequences, multivariate sampling techniques, and applications in economic activity assessment and climate modeling. He has contributed to methods for handling finite populations and separable covariance matrices in multivariate cluster sampling. His recent work includes advancements in generalized additive models for large datasets and adaptive age replacement strategies for maintenance optimization. He has published in journals such as the Journal of the Royal Statistical Society and the Journal of the Operational Research Society. While no specific awards are listed, his research has been cited over 240 times for notable contributions in statistical modeling and operational research. He has supervised one doctoral student but no names are provided.

• Statistics
• Biostatistics
• Spatial Epidemiology
• +۵ مورد دیگر

Theresa Smith is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath, affiliated with centers including the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), the Centre for Mathematics and Algorithms for Data (MAD), and the Centre for Therapeutic Innovation. Her research focuses on statistical methods for spatial and longitudinal data, with applications in health and social sciences, particularly clinical decision support tools. She holds a PhD in Statistics from the University of Washington (2014) and previously served as a Senior Research Associate at Lancaster University (2014–2016). Her research interests include spatial epidemiology, Bayesian inference, and biostatistical modeling. She leads projects such as the TULiPS study on psoriasis burden and the AI4CI Hub for collective intelligence research. Notable collaborations involve developing AI-driven solutions for public health challenges and advancing wastewater-based epidemiology for disease monitoring. Dr. Smith has secured grants from the NIHR, EPSRC, and the Royal Society, including a Short Industry Fellowship for predictive analytics in electronic health records. She advises students like N. Khaleel, whose thesis she supervised. Her work contributes to Sustainable Development Goals related to health and well-being. She is also part of interdisciplinary teams addressing environmental health risks and urban transport policy impacts.

Bo Wangمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Longitudinal Data Analysis
• Gaussian Process Modelling
• +۳ مورد دیگر

Bo Wang is an Associate Professor in Statistics at the University of Leicester, affiliated with the School of Computing and Mathematical Sciences. He earned his PhD in Applied Mathematics from Shandong University in 2000, followed by research roles at institutions including IRISA (France), University of Glasgow, Newcastle University, and University of York before joining Leicester in 2011. His research focuses on functional and longitudinal data analysis, Gaussian process modelling, mortality forecasting, and computational statistical inference. He leads the Mathematics programme at the Leicester International Institute, Dalian University of Technology. Education: PhD in Applied Mathematics, Shandong University (2000) Wang's research interests emphasize developing statistical methodologies for complex data structures, particularly in mortality and demographic forecasting using Gaussian processes. His work integrates computational techniques to address challenges in longitudinal studies and functional data analysis. Recent contributions include innovative approaches to multipopulation mortality modelling and robust non-parametric forecasting frameworks. His publication trends highlight advancements in Gaussian process regression, functional clustering, and applications to public health (e.g., parasite prevalence studies). While no specific awards are noted, his extensive peer-reviewed output demonstrates scholarly impact. Supervision and grants sections remain unspecified in the provided data. He collaborates with interdisciplinary teams and maintains academic partnerships through his program leadership role at the Leicester-Dalian partnership.

Dario Spanòمشاهده پروفایل

استاد

• Combinatorial Stochastic Processes
• Mathematical Population Genetics
• Bayesian Nonparametric Statistics
• +۳ مورد دیگر

Professor Dario Spanò is a faculty member in the Department of Statistics at the University of Warwick, serving as Deputy Head (Teaching and Learning). He holds a PhD in Mathematical Statistics (University of Pavia, 2003) and a Laurea in Economics (University of Pavia, 1998). His research focuses on combinatorial stochastic processes, Bayesian nonparametric statistics, and mathematical population genetics, with applications to genetics and evolutionary dynamics. His work includes contributions to Wright-Fisher diffusions, coalescent theory, and exact simulation methods. He has supervised numerous PhD students and co-organizes international conferences in statistics and probability. Research Interests: Stochastic processes and their applications in population genetics Bayesian nonparametric methods and their theoretical foundations Exact simulation of diffusion processes Coalescent theory and genealogical processes Recent Work Trends: Recent publications emphasize theoretical developments in diffusion models (e.g., Wright-Fisher processes), duality methods, and computational Bayesian inference for genetic data. Key themes include excursion theory, selection dynamics in random environments, and algorithmic advancements for simulating complex stochastic systems. Awards & Recognition: No scientific awards explicitly listed, though his research has been widely cited in the field. Teaching & Supervision: Current courses include ST343 and ST419 (data science topics). He has supervised over a dozen PhD students, focusing on interdisciplinary projects in statistics and mathematical genetics. His students have pursued roles in academia and industry. Labs/Teams: Active in the CRiSM research centre at Warwick, contributing to collaborative projects in statistical genetics and computational methods.

Mayer Alvoمشاهده پروفایل

استاد

• Nonparametric Statistics
• Sequential Analysis
• Spatial Statistics
• +۵ مورد دیگر

**Mayer Alvo** is a **Full Professor** in the **Department of Mathematics and Statistics** at the **University of Ottawa**. He holds an MSc from McGill University and a PhD from Columbia University. His research focuses on **Nonparametric Statistics**, **Sequential Analysis**, and **Spatial Statistics**, with applications in environmental modeling and ranking data analysis. Alvo has authored three books, including *A Parametric Approach to Nonparametric Statistics* (2018) and *Statistical Methods for Ranking Data* (2014). He has developed an **R package** *hypersampleplan* for hypergeometric distribution calculations. His research interests include analyzing trends in acid deposition in the Great Lakes, Bayesian statistics, and the application of statistical methods to big data. Alvo has supervised multiple graduate students, including **Rachid Bentoumi**, **Jingrui Mu**, and **Xiuwen Duan**. He teaches courses such as MAT 1371, MAT 2375, and MAT 4376, covering topics in probability, statistics, and applied mathematics. Alvo's recent work explores parametric embedding in nonparametric problems and change-point detection in stress-strength reliability. His contributions span theoretical advancements in ranking data analysis and practical applications in environmental science and finance. He is affiliated with the **Statistics and Probability Research Group** at the University of Ottawa.

Yanxun Xuمشاهده پروفایل

دانشیار

• Biostatistics
• Machine Learning
• Nonparametric Bayes
• +۴ مورد دیگر

Yanxun Xu is an Associate Professor of Applied Mathematics and Statistics at the Whiting School of Engineering , Johns Hopkins University, and an Adjunct Assistant Professor in the Division of Biostatistics and Bioinformatics at the Sidney Kimmel Comprehensive Cancer Center. She is also a member of the Data Science and Artificial Intelligence Institute . Her research focuses on developing Bayesian statistical methods and machine learning algorithms to address challenges in heterogeneous, large-scale datasets, particularly in clinical trials, electronic health records, cancer genomics, and HIV/AIDS studies. Key areas of expertise include nonparametric Bayes , reinforcement learning , and dynamic treatment regimes . Her work emphasizes precision medicine applications, such as optimizing antiretroviral therapies for HIV patients and analyzing cognitive outcomes in clinical trials. Funding sources include the National Science Foundation (NSF) , National Institutes of Health (NIH) , and industry partners like AstraZeneca. Xu has published over 80 peer-reviewed articles and received prestigious awards, including the 2016 Mitchell Prize from the International Society for Bayesian Analysis. Her research bridges statistical theory with real-world healthcare applications, addressing topics like hospital-level variations in COVID-19 treatment and the impact of inflammation biomarkers on neurocognitive functions in psychosis. PhD in Biostatistics (implied by academic rank) Recipient of JHU’s Center for AIDS Research Faculty Development Award Developed open-source software tools for causal inference and longitudinal data analysis

• Biostatistics
• Bayesian Statistics
• Statistical Genomics
• +۴ مورد دیگر

Anish Mukherjee is a Research Fellow at the Faculty of Economics, University of Italian Switzerland (USI) in Lugano, Switzerland, working under Professor Antonietta Mira. His role focuses on developing statistical methodologies for complex biomedical data analysis within an economics faculty context. He earned his PhD in Biostatistics from the University of Louisville under Jeremy Gaskins' supervision. His research integrates advanced statistical techniques with biomedical applications, specializing in Bayesian approaches for high-dimensional dependent data. Key methodological contributions include zero-inflated count data models, heterogeneity detection frameworks for longitudinal outcomes, and nonparametric Bayesian methods using stochastic differential equations. Applied work spans microbiome analysis related to neurological treatments, infectious disease transmission modeling, maternal health disparities, wastewater-based SARS-CoV-2 surveillance, and air pollution-COVID-19 outcome relationships.

Oke Gerkeمشاهده پروفایل

استاد

• Clinical Biostatistics
• Diagnostic and Prognostic Trials
• Molecular Imaging
• +۱۲ مورد دیگر

Oke Gerke is a Professor in Clinical Biostatistics in Diagnostic Research at the Department of Clinical Research, University of Southern Denmark, and a Biostatistician at the Department of Nuclear Medicine, Odense University Hospital. He is affiliated with the Research Unit of Clinical Physiology and Nuclear Medicine in Odense and holds a DMSc from the Faculty of Health Sciences at SDU. MSc in Mathematics and Economics, University of Hamburg (1998) PhD in Statistics and Econometrics, University of Hamburg (2001) DMSc, Faculty of Health Sciences, University of Southern Denmark (2024) Lecturer Training Programme, University of Southern Denmark (2010) His research centers on the methodological foundations of diagnostic and prognostic trials in molecular imaging. He specializes in adaptive and sequential trial designs, Bland-Altman agreement analysis, ROC curve methodology, and network meta-analysis of diagnostic accuracy studies. His work bridges biostatistics, clinical epidemiology, and nuclear medicine, with applications in oncology, cardiology, and public health. He has contributed extensively to improving reporting standards in diagnostic research and statistical methodology in clinical trials. The recent articles highlight a strong trend toward methodological innovation in diagnostic research, with a focus on adaptive and seamless trial designs, real-time evaluation frameworks during outbreaks, and advanced statistical techniques for agreement and cutpoint analysis. His clinical work integrates nuclear imaging modalities like PET/CT in cancer and cardiovascular disease, supported by rigorous meta-analytic and biostatistical approaches. He is a member of the following scientific societies: International Biometric Society (IBS) International Society for Clinical Biostatistics (ISCB) Danish Society for Theoretical Statistics (DSTS) Oke Gerke has supervised 1 PhD as main supervisor and 22 as co-supervisor, with 10 completed master’s theses under his main supervision and 4 ongoing PhD projects as co-supervisor. He has been involved in research projects such as the Neurobiological effects of work-related adjustment disorder, contributing to both statistical design and analysis. While no specific grants are listed, his extensive publication record and collaborative research indicate active grant-supported work. He frequently participates in workshops, seminars, and conferences, delivering guest lectures on topics such as network meta-analysis and diagnostic test evaluation. He is actively involved in academic and clinical research teams at SDU and OUH, particularly within the Research Unit of Clinical Physiology and Nuclear Medicine. His collaborative network spans multiple disciplines, including cardiology, oncology, and psychiatric research, reflecting a multidisciplinary approach to clinical biostatistics.

Silvia Liveraniمشاهده پروفایل

استاد

• Bayesian Modelling
• Clustering Methods
• Computational Statistics
• +۷ مورد دیگر

Dr Silvia Liverani is a Reader in Statistics and Head of the Centre for Probability, Statistics and Data Science at Queen Mary University of London's School of Mathematical Sciences. Her research focuses on Bayesian methods, clustering, and spatio-temporal modeling with applications in epidemiology, environmental health, and genetics. She collaborates with institutions like the Royal Botanical Gardens, Kew, and has advised three PhD students. She leads interdisciplinary projects including a £1,000 LMS-funded LGBTQ+ Mathematics Open Day initiative and hosts international seminars. Research interests include Dirichlet process mixtures, causal methods, and health data modeling. Notable publications (2020–2025) address causal adversarial analysis, urban health inequalities, and Bayesian graph structures. Current projects involve biodiversity modeling using spatial statistics and misaligned data techniques. She has been cited in media outlets like phys.org and The Conversation for studies on cerebral palsy health risks and biodiversity loss. Her academic leadership includes roles in Equality, Diversity & Inclusion and collaborations with charities/industry. Recent grants include a Turing Institute Data Study Group project on biodiversity prioritization. Supervised PhD topics include Bayesian epidemiological modeling and biodiversity data analysis.

Ingrid Kristine Gladمشاهده پروفایل

استاد

• Statistics and Data Science
• Machine Learning
• Anomaly Detection
• +۵ مورد دیگر

Ingrid Kristine Glad is a Professor at the Department of Mathematics, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. She serves as co-director of the Integreat Centre of Excellence and BigInsight Centre for Research-Based Innovation, and chairs the Abel Board (2022–2026). Her research focuses on statistical and machine learning methodologies for high-dimensional data, particularly in genomics, sensor systems, and maritime applications. She has pioneered methods like monotone regression, tailored graphical lasso, and Shapley-value-based explainability frameworks. Research Interests: Glad’s work integrates theoretical statistics with practical applications in anomaly detection, change-point analysis, and predictive modeling. She develops novel algorithms for analyzing large-scale datasets from genomics (e.g., gene networks) and industrial sensor streams (e.g., battery degradation in maritime batteries). Her methods emphasize interpretability and scalability, addressing challenges in both supervised and semi-supervised learning contexts. Publications: Her recent work includes advancements in Shapley-value explanations, maritime battery health monitoring, and biofouling impact analysis. Key themes across her articles are statistical methodology development, machine learning applications in engineering, and computational tools for genomic data integration. Over 50 peer-reviewed papers span journals like Expert Systems with Applications , Journal of Machine Learning Research , and BMC Bioinformatics . Grants & Leadership: Leads interdisciplinary projects funded by the Norwegian Research Council and EU initiatives. Her roles in major centers highlight her influence in shaping statistical research agendas. Supervises active PhD students focused on topics like lifetime analysis models and maritime system analytics. Labs & Collaborations: Central to the Genomic HyperBrowser platform and collaborations with maritime industry partners. Active in both theoretical statistics (e.g., penalized regression) and applied domains (e.g., autonomous ship safety modeling).

Marco Caroneمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Epidemiological Methods
• +۵ مورد دیگر

Marco Carone is a Professor of Biostatistics and Adjunct Professor of Statistics at the University of Washington, holding the Norman Breslow Endowed Faculty Fellowship. He is an Affiliate Investigator at the Fred Hutchinson Cancer Research Center’s Vaccine and Infectious Disease Division. His research focuses on causal inference, survival analysis, and nonparametric methods, with applications in vaccine science, environmental health, and public health. Carone earned his PhD in Biostatistics from Johns Hopkins University and completed postdoctoral training at UC Berkeley. Education: PhD in Biostatistics, Johns Hopkins University (2011) Hon. B.Sc. in Probability and Statistics, McGill University (2005) Diploma of College Studies in Pure and Applied Sciences, Marianopolis College (2002) Research Interests: Development of robust statistical methods for causal inference and nonparametric estimation Analysis of vaccine efficacy and infectious disease data Integration of machine learning with traditional biostatistical frameworks Applications in environmental epidemiology and aging research His publications span topics including survival analysis, immune correlates of vaccine efficacy, and statistical methodologies for high-dimensional data. Carone has advised over 15 PhD students, many of whom hold academic and industry roles. Awards: Norman Breslow Endowed Faculty Fellowship (2019) UW Teaching Award (2018) Phi Beta Kappa and Delta Omega Honor Societies (2011) Teaching & Service: Teaches courses on survival analysis, statistical inference, and biostatistical consulting Associate Editor for Biometrics and Journal of Causal Inference Peer reviewer for leading journals and grant agencies