“Everything we hear is an opinion, not a fact. Everything we see is a perspective, not the truth.”
—Marcus Aurelius, Meditations
Welcome to Fan’s webpage!
I am a postdoctoral research fellow at University of California, Los Angeles, working with Dr. Marc Suchard to develop Bayesian statistical methods for analyzing large-scale observational health data. I obtained my Ph.D. degree in Statistics from Duke University under the supervision of Dr. Alexander Volfovsky in Fall 2021, with a dissertation on stochatic processes models on and of dynamic networks.
I am joining the Department of Biostatistics at the University of Michigan as a tenure-track Assistant Professor in January 2024.
My research interests include:
- Bayesian statistics and statistical computation for complex and large-scale datasets
- Stochastic processes and dynamic models
- Health data science and informatics
- Computational social science
I also enjoy writing, sports, and music. I have an occasionally updated blog here (mostly written in Chinese).
Select recent first-author work
- Bayesian Safety Surveillance with Adaptive Bias Correction (2022+). (Manuscript pending funders’ approval; study protocol)
- Inferring HIV Transmission Patterns from Viral Deep-Sequence Data via Latent Spatial Poisson Processes (2022). arXiv:2302.11567. (Under revisions)
- Likelihood-based Inference for Partially Observed Stochastic Epidemics with Individual Heterogeneity (2021). arXiv:2112.07892. (Under revisions)
- Likelihood-based Inference for Partially Observed Epidemics on Dynamic Networks (2020). Journal of the American Statistical Association. (Winner of 2020 SBSS Student Paper Award)
Click Research for more details on my methods research and collaborative work.
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