Jianbin Tan
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I am currently a Postdoctoral Associate in the Department of Biostatistics & Bioinformatics at Duke University, working with Prof. Anru Zhang and Prof. Pixu Shi. I received my Ph.D. in Statistics from Sun Yat-sen University in 2023 under the supervision of Prof. Hui Huang.
I won the Outstanding Doctoral Thesis Award (2023), the IMS New Researcher Travel Award (2026), and the ICSA Travel Award (2026).
I am interested in developing methods that leverage statistical principles, mathematical structures, and application-specific characteristics intrinsic to complex data, supporting analysis in biomedical, public health, and environmental sciences.
We develop statistical and generative AI methods for complex health and biomedical data, including electronic health records and longitudinal biomedical trajectories, with an emphasis on modeling heterogeneity and missingness, synthesizing realistic data, and enabling reliable prediction and inference.
We develop statistical methods for dynamic epidemiologic and public health data, with an emphasis on causal inference, disease transmission modeling, and longitudinal association discovery, to evaluate interventions, understand population-level health dynamics, and support evidence-based public health decisions.
From a methodological perspective, my research centers on developing flexible, theoretically grounded, and structure-aware methods tailored to functional, longitudinal, and dynamic data, as well as other data with temporal or spatial structure.
I focus on leveraging statistical principles and mathematical structures intrinsic to complex data to uncover dependence patterns, causal relationships, and latent mechanisms, particularly when the data are noisy, dependent, incompletely observed, or high-dimensional. My contributions span dimension reduction, generative modeling, association discovery, causal inference, operator learning, and differential-equation-based learning.
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