賈金柱

生物統計💂🏿‍♀️、高維統計推斷、大數據分析👨🏽‍🦰、統計機器學習、因果推斷

1. 國家自然科學基金面上項目, 文本挖掘的統計建模, 2016/01-2019/12 , 主持。

2. 國家自然科學基金青年科學項目，稀疏方差分析與稀疏高維貝葉斯網絡學習, 2012/01-2014/12 主持。

3. 自然科學基金創新群體項目, 高逼真度試聽系統的理論與方法, 2012/01 -2014/12,參加。

4. 科技部重點研發計劃， 大氣汙染對呼吸和心血管系統健康影響的早期識別技術 2017.07-2020.12，參加

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.