Yan Sun

Postdoctoral Researcher,
Department of Statistics and Data Science,
University of Pennsylvania,
Philadelphia, USA.
E-mail: sunyan1@wharton.upenn.edu

About me

I am currently a Postdoctoral Researcher in the Department of Statistics and Data Science at the University of Pennsylvania, jointly mentored by Prof. Edgar Dobriban, Prof. Ian Barnett, and Prof. Pratik Chaudhari, working on model calibration and uncertainty quantification of machine learning models. Before that, I was an applied scientist at Amazon.

I obtained my Ph.D. in 2022 from the Department of Statistics at Purdue University, supervised by Prof. Faming Liang. I received my bachelor’s degree in 2017 from the Department of Mathematics in Chu Kochen Honors College, Zhejiang University.

Research Interests

I am interested in connecting statistical theories with advanced Machine Learning models, and with theoretical guidance, develop effective methods to improve practice. Some topics that I have worked on include:

Sparse Deep Learning: Consistency, High Dimensional Variable Selection, Network Pruning.
Bayesian Statistics: Bayesian Neural Network, Posterior Concentration, Markov Chain Monte Carlo.
Uncertainty Quantification: Model Calibration, Asymptotic Distribution.

Publications

Extended fiducial inference: toward an automated process of statistical inference

Faming Liang, Sehwan Kim, Yan Sun

Journal of the Royal Statistical Society: Series B (Statistical Methodology) (2024).

[PDF]
Deep network embedding with dimension selection

Tianning Dong, Yan Sun, Faming Liang

Neural Networks (2024).

[PDF]
Sparse Deep Learning for Time Series Data: Theory and Applications

Mingxuan Zhang, Yan Sun, Faming Liang

Advances in neural information processing systems (2023).

[PDF]
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network

Siqi Liang, Yan Sun, Faming Liang

Advances in neural information processing systems (2022).

[PDF], [Code]
A Kernel-Expanded Stochastic Neural Network.

Yan Sun, Faming Liang

Journal of the Royal Statistical Society: Series B (Statistical Methodology) (2022).

[PDF], [Code]
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration.

Yan Sun, Wenjun Xiong, Faming Liang

Advances in neural information processing systems (2021).

[PDF], [Code]
Learning Sparse Deep Neural Networks with Spike-and-Slab Priors.

Yan Sun, Qifan Song, Faming Liang

Statistics & Probability Letters (2021).

[PDF], [Code]
Consistent Sparse Deep Learning: Theory and Computation.

Yan Sun*, Qifan Song*, Faming Liang

Journal of the American Statistical Association (2021).

[PDF], [Code]
Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection.

Qifan Song, Yan Sun, Mao Ye, Faming Liang

Biometrika, (2020)

[PDF]
Variable selection via penalized neural network: a drop-out-one loss approach.

Mao Ye*, Yan Sun*

International Conference on Machine Learning (2018).

[PDF]