Find Academic Supervisors

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.

Huixia Judy WangView profile

Professor

• Quantile regression
• Extreme value theory
• Bioinformatics
• +4 more

Huixia Judy Wang is a Professor in the Department of Statistics at The George Washington University since 2017. Previously, she held positions at North Carolina State University from 2006 to 2017, advancing from Assistant to Associate Professor. Her expertise spans quantile regression, extreme value theory, bioinformatics, and longitudinal data analysis with applications in environmental science, health, and finance. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2006 M.S., Statistics, Fudan University, 2002 B.S., Statistics, Fudan University, 1999 Research focuses on developing robust statistical methods for analyzing extremes, high-dimensional data, and complex dependencies. Key themes include quantile-based approaches for spatial/temporal data, survival analysis with detection limits, and subgroup identification in clinical trials. She has contributed to methodological advancements in copula models, threshold regression, and Bayesian quantile inference. Her publications address applied problems such as climate variability impacts, biomedical data interpretation, and financial risk modeling. She maintains an active record of interdisciplinary collaboration across environmental science, genomics, and public health.

• Functional Data Analysis
• Biostatistics
• Bayesian Methodologies
• +3 more

Mark J. Meyer is an Associate Professor in the Department of Mathematics and Statistics at Georgetown University, affiliated with the statistics group and the Center for Global Health Science and Security. He contributes to the Global Infectious Disease Program and collaborates with researchers across disciplines including epidemiology, neurology, and environmental health. Research Interests: Dr. Meyer specializes in functional data analysis, focusing on function-on-function regression, historical functional linear models, and functional inference. His work bridges Bayesian and nonparametric methods, emphasizing accessibility through simplified statistical testing procedures for complex data. He also develops Bayesian techniques for categorical data, including binary, ordinal, and count data. Scientific Collaborations: His interdisciplinary collaborations span epidemiology, development psychology, political science, and animal behavior, reflecting his commitment to applying statistical methods to real-world challenges.

Denis KojevnikovView profile

Assistant Professor

• Econometric Theory
• Network Dependence
• High-Dimensional Models
• +2 more

Denis Kojevnikov is an Assistant Professor of Econometrics at Tilburg University specializing in theoretical econometrics with focus on network dependence, cluster inference, and large Bayesian games. His research develops novel methods for analyzing cross-sectional dependence structures in complex data environments, including network-linked observations and clustered data with large group sizes. Recent publications establish impossibility results for cluster-robust inference, derive central limit theorems for network-dependent processes, and propose estimation techniques for Bayesian games with heterogeneous beliefs. Methodologically, his work bridges probability theory, statistical inference, and economic applications. Kojevnikov teaches graduate courses in econometric theory, panel data analysis, and nonparametric methods. His research contributes to foundational understanding of dependence structures in econometrics with applications to industrial organization and social network analysis.

Anirban BhattacharyaView profile

Professor

• Bayesian Statistics
• High-dimensional Data Analysis
• Bayesian Nonparametrics
• +3 more

Anirban Bhattacharya is a Professor and Patricia R. Smith and Dr. William B. Smith Faculty Fellow in the Department of Statistics at Texas A&M University, within the College of Arts & Sciences. His research focuses on Bayesian asymptotics, Bayesian nonparametrics, and high-dimensional data analysis. He holds a Ph.D. in Statistics from Duke University (2012) and Master's and Bachelor's degrees in Statistics from the Indian Statistical Institute (2008 and 2006). His academic journey includes advanced studies at prestigious institutions, blending theoretical rigor with computational innovation. Research interests emphasize robust statistical methods, scalable Bayesian computation, and applications in machine learning. Recent work explores challenges in high-dimensional inference, optimal transport, and fair clustering algorithms. Notable contributions include advancements in Gibbs posterior frameworks, constrained Gaussian processes, and variational inference techniques. He has been recognized with the prestigious Smith Fellowship, highlighting his impact in statistical methodology. His publications span foundational Bayesian theory to applied problems in physics and engineering. Bhattacharya’s work often bridges computational efficiency and statistical accuracy, addressing modern challenges in big data analysis. Ongoing research includes developing robust estimators for complex metric spaces and exploring fair machine learning methodologies.

Toryn SchaferView profile

Assistant Professor

• Spatio-temporal statistics
• Reinforcement learning
• Bayesian statistics
• +5 more

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.