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Leo Li Duan

I’m a statistician interested in Robust Bayes and Pseudo-Likellihood. The current push is to use pseudo-likelihood to build more flexible and robust models for complex data arising from machine learning, biomedical research and neuroscience.

I work as an Assistant Professor in the Department of Statistics at University of Florida. Previously I was a postdoc associate in Duke University, working with Prof. David Dunson on Bayesian modeling for complex data. I obtained my PhD from Department of Mathematics at University of Cincinnati in 2015, studying non-parametric Bayes and working with Profs. Rhonda Szczesniak and Xia Wang.

Email: *li dot duan at ufl dot edu*

Spring 2019 Link to STA4321 / STA5325.

Fall 2019 Link to STA4322 / STA5328.

Research Interests

Bayes Pseudo-Likelihood

Conventionally, Bayesian inference requires a correctly specified model, describing the full generating process for the data. This can create problems for modern machine learning tasks: i. Modeling everything has a high modeling and computing cost; ii. Usually the data have some deviation from the model assumption, which can lead to (often unbounded) deterioration of performance.

I’m interested in tackling these problems through the new framework called “Bayesian Pseudo-Likelihood”. This may involve:

  1. Replacing the complicated prior/likelihood with a simple approximate (but having proper density).
  2. Assigning a probabilistic model on some transform of the source data, such as some robust summary statistics, or a lower-dimensional representation.

The “pseudo-nesss” got its name as these probabilitistic models may not correspond exactly with the unobserved “truth”, but it substantially improves the overall model robustness and reduces the model complexity.



Statistical Methodology:
Computational Physics: