生物统计、高维统计推断、大数据分析、统计机器学习、因果推断

1. 国家自然科学基金面上项目, 文本挖掘的统计建模, 2016/01-2019/12 , 主持。

2. 国家自然科学基金青年科学项目，稀疏方差分析与稀疏高维贝叶斯网络学习, 2012/01-2014/12 主持。

3. 自然科学基金创新群体项目, 高逼真度试听系统的理论与方法, 2012/01 -2014/12,参加。

4. 科技部重点研发计划， 大气污染对呼吸和心血管系统健康影响的早期识别技术 2017.07-2020.12，参加

1.Huizhuo Yuan, Jinzhu Jia, Zhanxing Zhu (2018). SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction. ISBI 2018: 1521-1524

2.Li Yanfang and Jinzhu Jia (2017) . L1 least squares for sparse highdimensional LDA. Electronic Journal of Statistics, 2017, 11(1):2499-2518.

3.Junyang Qian and Jinzhu Jia (2016). On Piecewise Pattern Recovery of Fused Lasso. Computational Statistics and Data Analysis 94 (2016), pp 221-237.

4.Jinzhu Jia and Karl Rohe (2015). Preconditioning the Lasso for sign consistency. Electronic Journal of Statistics 2015, pp 1150-1172.

5.Yangbo He, Jinzhu Jia and Bin Yu (2015). Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs. Journal of Machine Learning Research16 (Dec), pp: 2589-2609.

6.Jinzhu Jia, Luke Miratrix, Bin Yu, Brian Gawalt, Laurent El Ghaoui, Luke Barnesmoore and Sophie Clavier (2014). Concise Comparative Summaries of Large Text Corpora with Human Experiment. The Annals of Applied Statistics, 499-529.

7.Yangbo He, Jinzhu Jia and Bin Yu (2013). Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs. Annals of Statistics 2013, pp 1742-1779.

8.Jinzhu Jia, Karl Rohe and Bin Yu (2013). The Lasso under Poisson-like Heteroscedasticity. Staitstica Sinica 2013, pp 99-118

9.Jinzhu Jia and Bin Yu (2010). On model Selection Consistency of the Elastic Net when p n. Statistica Sinica. 20(2), 595-611.

10.Hua Chen, Zhi Geng and Jinzhu Jia (2007). Criteria for surrogate end points. Journal of the Royal Statistical Society: Series B (Statistical Methodology ) 69 (5) , 919-932.