About Me

Welcome to my website! My name is Xinyuan Tian. I am currently a Ph.D. candidate in the Department of Biostatistics at Yale School of Public Health, advised by Dr. Yize Zhao and Dr. Denise Esserman.

Prior to this, I received my M.Sc. in Biostatistics from Yale University in 2022, and my dual B.Sc. degrees in Applied Mathematics and Statistics from the University of California, Los Angeles (UCLA) in 2020.

My research focuses on developing novel statistical and machine learning methods for complex biomedical data, with a particular emphasis on brain imaging and genetics. Motivated by the need to understand brain functional and structural connectivity and their links to cognition and disease, I develop Bayesian inference frameworks, deep learning models, and semiparametric methods for high-dimensional neuroimaging data. My methodological interests span Bayesian inference, deep learning, mediation analysis, semiparametric and nonparametric methods, and imaging genetics. Please visit the Research page for more details.

News

• August 2026 Presenting A Bayesian deep learning framework for brain dynamic functional connectivity linked with cognition at the Joint Statistical Meetings (JSM) 2026 in Boston, highlighting new approaches for connecting dynamic brain networks with cognitive outcomes.
• June 2026 Presented Bidirectional graph autoencoder with shared latent graph for structural–functional connectivity backbone inference at Statistical Methods in Imaging (SMI) 2026 in Ann Arbor, introducing a shared latent graph approach to structural–functional connectivity.
• May 2026 Our paper, Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference, was selected for an oral presentation at the International Conference on Machine Learning (ICML 2026). Motivated by the need to reveal complex risk mechanisms that pairwise models may miss, the work uses Bayesian hypergraphs to disentangle higher-order latent pathways.
• January 2026 Honored to receive the JSM Student Paper Award from the American Statistical Association’s Statistics in Imaging Section for my work on dynamic functional connectivity and cognition.
• December 2025 Our paper, Latent space-based network analysis for brain–behavior linking in neuroimaging, was published online in Nature Methods. Motivated by low power and limited replicability in conventional brain–behavior studies, LatentSNA preserves network topology to improve imaging biomarker detection and prediction.
• June 2025 Our paper, Supervised brain node and network construction under voxel-level functional imaging, was published in Imaging Neuroscience. Motivated by the limitations of fixed brain atlases, the method learns outcome-informed brain nodes that strengthen brain–behavior prediction.
• June 2025 Presented A Bayesian deep learning framework for brain dynamic functional connectivity linked with cognition at the International Chinese Statistical Association (ICSA) Applied Statistics Symposium in Storrs, Connecticut.
• April 2025 Our paper, Bayesian longitudinal network regression with application to brain connectome genetics, was published in Statistics in Medicine. Motivated by the need to understand how genetic variation shapes evolving brain networks, the framework jointly models longitudinal connectomes and genetic effects.
• October 2024 Our paper, Bayesian pathway analysis over brain network mediators for survival data, was published in Biometrics. Motivated by the information lost when networks are reduced to isolated edges, the method models whole-brain mediation pathways linking exposures to disease onset.
• February 2024 Our paper, Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event, was published in the Journal of the Royal Statistical Society: Series C. Motivated by clinical studies complicated by clustering, excess zeros, and terminal events, the model delivers flexible and robust Bayesian inference for recurrent outcomes.

Education

• Current
  Yale University Ph.D. Candidate in Biostatistics Yale School of Public Health · Advisors: Yize Zhao and Denise Esserman
• 2022
  Yale University M.Sc. in Biostatistics Yale School of Public Health
• 2020
  University of California, Los Angeles B.Sc. in Applied Mathematics and B.Sc. in Statistics Dual bachelor’s degrees