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Lei FangView profile

Lecturer

• Bayesian Machine Learning
• Statistical Learning
• Wireless Sensor Networks
• +4 more

Dr. Lei Fang is a Lecturer in the School of Computer Science at the University of St Andrews. Their research focuses on Bayesian machine learning, statistical learning, wireless sensor networks, and adversarial machine learning. They advise PhD students Zhongliang Guo and Weiye Li. Key research interests include Bayesian nonparametrics, transfer learning, and human activity recognition through sensor data analysis. Dr. Fang's work spans interdisciplinary areas such as federated learning for healthcare applications and security in neural networks. They are affiliated with the Centre for Research into Ecological & Environmental Modelling, contributing to projects involving complex system analysis and sensor network optimization. Recent publications highlight advancements in adversarial attacks on signature verification systems, link prediction in heterogeneous social networks, and AI-driven software repository analysis. Their research emphasizes practical applications in cybersecurity, healthcare monitoring, and environmental modeling.

Yuichi KitamuraView profile

Professor

• Econometrics
• Nonparametric Statistics
• Bayesian Methods
• +4 more

Yuichi Kitamura is the Alfred Cowles Professor of Economics and Management at Yale University. He holds a Ph.D. in Economics from Yale and has held faculty positions at the University of Minnesota, University of Wisconsin, and University of Pennsylvania before returning to Yale in 2004. His research focuses on econometrics, including empirical likelihood, inverse problems, nonparametric Bayesian methods, and dynamic discrete choice models. Recently, he developed frameworks for analyzing economic theories using computational science advancements. Editor of Japanese Economic Review Associate/Co-editor for journals like Econometrica , Econometric Theory , and Journal of Business and Economic Statistics Kitamura has received prestigious awards such as the Nakahara Prize and Sloan Fellowship. He has held visiting roles at institutions including the University of Bristol, Cambridge University, and Bar-Ilan University. His research bridges computational science and economic theory, emphasizing methodological innovation. Grants include multiple NSF awards. He actively contributes to academic governance through editorial roles and conferences, including plenary speaking engagements at major international events.

Jennifer HillView profile

Professor

• Causal Inference
• Bayesian Statistics
• Machine Learning
• +3 more

Dr. Jennifer Hill is a Professor of Applied Statistics, Co-Chair of the Department of Applied Statistics, Social Science, and Humanities (ASH), and Co-Director of the PRIISM Center at New York University's Steinhardt School. She holds a PhD in Statistics from Harvard University (2000) and a post-doctoral fellowship in Child and Family Policy from Columbia University (2002). Her research focuses on causal inference methods, Bayesian nonparametric techniques, and addressing challenges in observational studies. Notable contributions include the development of the bartCause software for machine-learning-based causal analysis. Key research areas include causal modeling, policy evaluation, and data science for social impact. Hill co-founded the MS in Applied Statistics for Social Science Research (A3SR), emphasizing social impact through data science. She has been recognized with the 2021 NYU Distinguished Teaching Award and the 2016 Miller Prize for her influential work on bias in grouped data analysis. Her courses cover causal inference, missing data analysis, and data science communication. Hill’s work bridges statistical innovation with real-world applications in health, education, and social policy. The A3SR program, with its dual MPA option, reflects her commitment to training socially responsible data scientists.

Shanshan ZhaoView profile

Adjunct Associate Professor

• Biostatistics
• Environmental Health
• Cancer Research
• +7 more

Dr. Shanshan Zhao is an Adjunct Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill and a Principal Investigator at the National Institute of Environmental Health Sciences (NIEHS). Her research focuses on developing statistical methods to understand environmental and social influences on health, particularly in cancer epidemiology and risk prediction. She holds a secondary appointment in the NIEHS Epidemiology Branch and leads a group integrating statistical methodology with population-based studies. Her work emphasizes disease risk assessment using survival analysis, longitudinal data, and biomarker evaluation. Key research areas include multivariate failure time data, environmental mixture analysis, and epigenetic biomarkers. She has contributed to studies on breast, prostate, and colorectal cancers, emphasizing spatial-temporal modeling and public health applications. Zhao’s awards include the 2014 ASA David P. Byar Travel Award and recognition for her 2023 NIEHS publication on environmental breast cancer risk factors. Her team develops software tools like mhazard for multivariate survival analysis and STAFT for spatio-temporal modeling. Her lab collaborates across disciplines to advance environmental health sciences, with a focus on translating statistical innovations into actionable insights for clinicians and policymakers.

Rebecca KillickView profile

Professor

• Time Series Analysis
• Changepoint Detection
• Wavelet Methods
• +1 more

Professor Rebecca Killick is Chair of Statistics at Lancaster University's School of Mathematical Sciences. She develops statistical models for nonstationary time series with applications in environmental science, health monitoring, and econometrics. Research expertise includes: Changepoint detection algorithms Locally stationary wavelet processes Multivariate changepoint methods Environmental time series forecasting Recent publications apply changepoint methods to soil moisture dynamics, ecosystem services, and economic indicators. Work appears in statistics and interdisciplinary journals with strong computational focus. Current research group includes: 9 PhD students and postdoctoral researchers Projects funded by EPSRC, NERC, and Innovate UK Software contributions: changepoint R package (CRAN) changepoint.np for nonparametric detection EnvCpt for environmental applications

Linglong KongView profile

Professor

• Functional and Neuroimaging Data Analysis
• Statistical Machine Learning
• Robust Statistics
• +4 more

Linglong Kong is a Professor and Canada Research Chair in Statistical Learning at the University of Alberta's Department of Mathematical and Statistical Sciences. He is also a Fellow at the Alberta Machine Intelligence Institute (Amii) and holds a Canada CIFAR AI Chair. His expertise spans statistical machine learning, neuroimaging data analysis, and AI-driven healthcare solutions. Education: PhD in Statistics, University of Alberta (2009) MS in Statistics, Peking University (2002) BS in Probability and Statistics, Beijing Normal University (1999) Research Interests: Kong focuses on functional neuroimaging data analysis, robust statistical methods, and applications of machine learning in healthcare. His work emphasizes fairness, privacy, and interpretability in AI systems. Key areas include differential privacy, quantile regression, and predictive modeling for medical imaging and precision medicine. Recent Trends in Articles: His publications address cutting-edge challenges in privacy-aware machine learning, federated learning frameworks, and neuroimaging biomarkers. Recent work includes advancements in fairness for precision medicine and scalable methods for high-dimensional data analysis. Awards: Fellow of Amii (2021) Great Supervisor Award (2018) Josephine Mitchell Mentoring Award (2017) Grants & Advising: Kong leads major grants on AI ethics, neuroimaging biomarkers, and labor market equality. He has advised numerous students, including PhD candidates and postdocs, in statistical machine learning and healthcare analytics. Labs & Teams: Active in the Alberta Machine Intelligence Institute (Amii) and collaborates with interdisciplinary teams in neuroimaging, precision medicine, and AI ethics initiatives.

Hongbo XieView profile

Research Fellow

• Statistical/Bayesian Inference
• Machine Learning
• Deep Learning
• +3 more

Dr Hongbo Xie is a Vice-Chancellor’s Senior Research Fellow/Lecturer at QUT and an Associate Investigator at ACEMS. He holds a PhD in Biomedical Engineering from Shanghai Jiao Tong University. His research focuses on statistical/Bayesian inference, machine learning, biomedical signal/image processing, and matrix/tensor analysis for signal recovery. With over 70 publications (as of 2022), his work spans IEEE journals, conference proceedings, and book chapters. Education: PhD in Biomedical Engineering, Shanghai Jiao Tong University, China Research Interests: Statistical and Bayesian methodologies for complex data Machine learning applications in biomedical signal processing Image recovery via matrix and tensor analysis Advanced signal processing for healthcare and engineering Publications: His recent work includes EEG-based analysis of adolescent psychological distress, variational Bayesian models for movement control, and kernelized Bayesian matrix factorization. Articles frequently address biomedical signal/image processing, emphasizing robust algorithms for real-world applications. Affiliations: School of Mathematical Sciences (QUT), ACEMS.

Nathaniel JosephsView profile

Assistant Professor

• Networks
• Bayesian nonparametrics
• Causal inference
• +4 more

Nathaniel Josephs is an Assistant Professor in the Department of Statistics at NC State University. His research focuses on networks, Bayesian nonparametrics, and causal inference, with applications spanning social science, materials science, and biostatistics. He holds a Ph.D. in Statistics from Boston University (2021). Education Ph.D. in Statistics, Boston University, 2021 Research Interests Josephs specializes in developing statistical methodologies for network data analysis, including Bayesian approaches for community detection, hypergraph models, and applications in epidemiology, sports analytics, and microbiome studies. His work bridges theoretical statistics with practical challenges in diverse fields such as OLED material prediction and oncology outcomes research. Publications Recent work includes advancements in network scale-up methodologies, hypergraph-based scoring systems, and Bayesian classification for microbiome data. His research demonstrates a strong emphasis on interdisciplinary applications, combining rigorous statistical frameworks with real-world problems in health sciences and engineering. Awards & Grants No awards or grants explicitly listed in available materials. Advising & Labs No advising information or lab affiliations specified in the provided text.

Ryan MartinView profile

Professor

• Foundations of statistics
• Foundations of probability
• Generalized Bayes and Gibbs posterior distributions
• +5 more

Ryan Martin is a Professor in the Department of Statistics at North Carolina State University (NC State). He holds a Ph.D. in Statistics from Purdue University (2009) and a B.A. in Mathematics from Franklin College (2003). His research focuses on foundational aspects of statistics and probability, including generalized Bayes methods, imprecise probability, and high-dimensional problems. He co-founded Researchers.One, an innovative peer review platform. Education: Ph.D., Statistics, Purdue University, 2009 M.S., Statistics, Indiana University-Purdue University Indianapolis, 2005 B.A., Mathematics, Franklin College, 2003 Research Interests: His work emphasizes foundational statistical theory, with applications in imprecise probability, Bayesian methods, and high-dimensional inference. Notable contributions include inferential models (IMs), possibilistic frameworks, and regularization techniques for anytime valid inference. He actively explores computational methods for statistical efficiency and reliability. Recent Contributions: Recent work includes advancements in e-processes for efficient uncertainty quantification, variational approximations for possibilistic models, and theoretical developments in Bernstein-von Mises theorems. He also investigates Fisher's underworld and reliability in scientific inference. Awards and Honors: D. D. Mason Award (2022) Van Dantzig Seminar Speaker (2021) Sigma Xi nominee (2020) Thank-a-Teacher Award (2018) I. W. Burr Award (2010) Advising and Grants: He has mentored over 50 graduate students and secured multiple NSF grants. Current projects include Bayesian model selection in high-dimensional settings and imprecise-probabilistic inference. Labs/Teams: Co-leads the Researchers.One initiative, promoting open scholarly communication. Active in collaborative networks on imprecise probability and statistical foundations.

Ana-Maria StaicuView profile

Professor

• Functional Data Analysis
• Biostatistics
• Longitudinal Data Analysis
• +5 more

Dr. Ana-Maria Staicu is a Professor in the Department of Statistics at North Carolina State University (NCSU), where she has been since 2009, progressing from Assistant to Associate Professor. She holds a Ph.D. in Statistics (2007) from the University of Toronto and a B.Sc. in Mathematics from the University of Bucharest (2000). Her research focuses on functional data analysis, longitudinal studies, biostatistics, and applications in medical imaging and social media analytics. She leads grants funded by NIH, NSF, and industry partners, including work on social media disinformation and biomedical imaging. Education: Ph.D., University of Toronto (2007); B.Sc., University of Bucharest (2000) Affiliations: Director of the NIH T32 Biostatistics Training Grant; Associate Editor for Stat , Biometrics , and JABES Research Interests: Functional data analysis, longitudinal modeling, spatial statistics, and applications in neuroscience, environmental science, and healthcare. Notable contributions include methods for analyzing brain imaging data (e.g., DTI in MS) and wearable device data. Awards: NSF CAREER Award (2015), ASA SPES Best Paper (2021), NCSU Faculty Scholar (2016), Cavell Brownie Mentoring Award (2019). Over 60 peer-reviewed publications and 10+ grants totaling $6M+. Grants/Service: Current projects include NSF-funded social media pathway analysis and NIH-funded biostatistical training in cardiovascular research. Served on grant review panels for NSF, NIH, and international foundations. Labs/Teams: Leads the FDA Lab at NCSU; collaborates with Duke-NC State-DCRI training programs and SAMSI initiatives.

Jonathan WilliamsView profile

Assistant Professor

• Foundations of Statistics
• Bayesian Inference
• Hidden Markov Models
• +3 more

Jonathan Williams is an Assistant Professor in the Department of Statistics at NC State University . His research focuses on foundational statistical methodologies, including Bayesian inference, hidden Markov models, and applications in medical science and multivariate time-series analysis. He holds a Ph.D. in Statistics from the University of North Carolina, Chapel Hill (2019). Education: Ph.D. in Statistics, University of North Carolina, Chapel Hill (2019) Research Interests: Development of robust statistical models for complex data structures Applications of Bayesian methods in healthcare and conflict analysis Advancements in conformal prediction and uncertainty quantification Transfer learning and multivariate time-series analysis Key Research Trends in Publications: Recent works emphasize methodological innovations in generalized fiducial inference, conformal prediction for heterogeneous domains (e.g., astronomy, housing markets), and Bayesian modeling in dynamic systems like conflict dynamics and medical progression studies. Advising & Grants: No specific advising or grant details provided in the text, though his academic role suggests involvement in graduate student mentorship and research funding activities typical of his rank.

Jim BergerView profile

Professor

• Bayesian Analysis
• Decision Theory
• Hypothesis Testing
• +4 more

Jim Berger is the Arts and Sciences Professor of Statistics at Duke University. His research focuses on Bayesian analysis, decision theory, hypothesis testing, and computational statistics with applications in astronomy, geophysics, and medicine. He has directed 36 doctoral dissertations and received numerous awards including Guggenheim and Sloan Fellowships, and election to the National Academy of Sciences. Research Interests: Multiplicity problems, model selection, multivariate analysis, Bayesian computation, and interdisciplinary applications in scientific domains. Major Awards: Guggenheim Fellowship (1977-78) National Academy of Sciences Member (2003) Samuel S. Wilks Memorial Award (2015) COPSS Presidents' Award (1985) Grants: Extensive NSF funding including collaborative research on Bayesian analysis, uncertainty quantification, and hazard modeling (2010-2018).

Fazil BakshView profile

Lecturer

• Genome-wide association studies
• Gene-environment interaction methods
• Family-based association studies
• +4 more

Fazil Baksh is a Lecturer in the Department of Mathematics and Statistics at the University of Reading, part of the School of Mathematical, Physical and Computational Sciences. He holds key administrative roles including School Director of WIDE, PhD Coordinator for Statistics, and DStat Admissions/Programme Director. His research focuses on statistical methodologies in genetics, bioinformatics, and medical statistics, with emphasis on genome-wide association studies, gene-environment interactions, and Bayesian modeling of biological systems. Teaching responsibilities include advanced statistical courses such as Methods of Machine Learning (ST3MML/ST4MML), Mathematics and Statistics for Data Science (CSMMS), and Advanced Statistical Modelling (ST3ASM). His work has addressed critical areas like snakebite epidemiology, psychiatric treatment efficacy, and genomic sequence analysis, with notable contributions to statistical bioinformatics and public health research. Research affiliations include the university’s Applied Statistics group. His methodological innovations span statistical genetics (e.g., efficient score tests for association studies) and healthcare economics (e.g., cost analyses of snakebite treatments). While no formal awards are listed, his publications reflect impactful contributions to both theoretical and applied statistical research. Advising and grant activities remain unspecified, though his interdisciplinary teaching and research suggest active involvement in training the next generation of statisticians and data scientists.

• Infinite Dimensional Analysis
• Point Processes
• Noncommutative Probability
• +3 more

Professor Eugene Lytvynov is a mathematician affiliated with Swansea University's School of Mathematics and Computer Science. He holds the academic rank of Professor in the Mathematics department. His research focuses on infinite-dimensional analysis, point processes, noncommutative probability, and umbral calculus. Recent work explores non-Abelian anyon quasiparticles and basis-free umbral calculus. He is available to supervise postgraduate students and maintains active research collaborations through platforms like ORCID, ResearchGate, and Google Scholar. His expertise spans advanced mathematical fields including stochastic processes, quantum probability, and operator algebras. Key research areas include the analysis of Radon measures, quasi-free states, and commutation relations in multicomponent systems. He has contributed extensively to the study of determinantal point processes, particle-hole transformations, and Fock space representations. Professor Lytvynov's publications reflect a strong emphasis on theoretical frameworks, with notable work on Segal-Bargmann transforms, Stirling operators, and Meixner-class orthogonal polynomials. His research often bridges pure mathematics and quantum physics, addressing topics like anyon statistics and non-Gaussian white noise calculus. He is accessible via email at e.lytvynov@swansea.ac.uk and can be reached at the Computational Foundry, Bay Campus. His academic contributions include over 50 peer-reviewed articles, with a focus on advancing analytical tools in infinite-dimensional spaces and quantum systems.

Juan Carlos EscancianoView profile

Professor

• Econometric Theory
• Financial Econometrics
• Risk Management
• +3 more

Juan Carlos Escanciano holds the position of Research Chair in Economics and Full Professor (Catedrático) at the Economics Department of Universidad Carlos III de Madrid. He obtained his PhD in Economics from the same university in 2004. Prior to his current role, he served as Assistant Professor at Universidad de Navarra (2004-2006), Full Professor at Indiana University (2006-2018), and held visiting positions at Yale University, Cornell, Rochester, and MIT. His research focuses on Econometric Theory, including identification, estimation, specification testing, and applications in Financial Econometrics and Risk Management. Escanciano holds editorial roles at leading journals including Econometric Theory , Econometric Reviews , and Journal of Business and Economic Statistics , and serves as Co-Editor of Advances in Econometrics . He is a Fellow of the Journal of Econometrics and has published extensively in top-tier journals such as the Journal of the American Statistical Association and The Annals of Statistics . His research interests emphasize semiparametric/nonparametric methods, specification testing, and empirical asset pricing. Notable contributions include work on backtesting financial risk measures, conditional moment restrictions, and the development of robust estimation techniques. His recent work explores machine learning applications in econometrics and systemic risk analysis. Scientific Awards: Fellow of the Journal of Econometrics Grants & Advising: Extensive record in grant-funded research; has advised numerous PhD candidates through the university's Economics Doctoral Program. Labs/Teams: Leads the Quantitative Economics Research Group at UC3M, collaborating with global institutions on structural econometric modeling.