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Zuofeng Shang



University of Wisconsin, Madison, WI - Statistics, PhD in 2011

Courses Taught / Teaching

2017 Fall: STAT 522, STAT 416/516

2018 Spring: STAT 350

2018 Fall: STAT 522, STAT 416/516

2019 Spring: STAT 533


My general research aim focuses on statistical foundation of modern data science fields, primarily in developing statistical frameworks for data science objects, efficient learning algorithms for scientific tasks and theoretical understanding on the nature of the problems. It spans statistics,
probability, machine learning, combinatorics, etc.

Publications & Professional Activities

1. Xu, G., Shang, Z. and Cheng, G. (2018). Optimal Tuning for Divide-and-Conquer Kernel Ridge Regression with Massive Data. International Conference on Machine Learning (ICML).

2. Gao, Z., Shang, Z., Du, P. and Robertson, J. (2018). Variance Change Point Detection under A Smoothly-changing Mean Trend with Application to Liver Procurement. Journal of American Statistical Association. In press.

3. Shang Z. and Cheng, G. (2018). Gaussian Approximation of General Nonparametric Posterior Distributions. Information and Inference, A Journal of the IMA. 7, 509-529.

4.Shang, Z. and Cheng, G. (2017). Computational Limits of A Distributed Algorithm for Smoothing Spline. Journal of Machine Learning Research, 18, 1-37.

5.Shang, Z. and Cheng, G. (2015). Nonparametric Inference in Generalized Functional Linear Models. Annals of Statistics, 43, 1742--1773.

6. Cheng, G. and Shang. Z. (2015). Joint Asymptotics for Semi-Nonparametric Regression Models with Partially Linear Structure. Annals of Statistics, 43, 1351--1390.

7. Cheng, G., Zhang, H.H. and Shang, Z. (2015). Sparse and Efficient Estimation for Partial Spline Models with Increasing Dimension. Annals of Institute of Statistical Mathematics, 67, 93--127.

8. Shang, Z. and Cheng, G. (2013). Local and Global Asymptotic Inference in Smoothing Spline Models. Annals of Statistics, 41, 2608--2638.

9. Shang, Z. and Zhou, X. (2007). Dual Generators for Weighted Irregular Wavelet Frames and Reconstruction Error. Applied and Computational Harmonic Analysis, 22, 356--367.

Honors, Awards and Grants

NSF DMS-1821157. Principal Investigator on Collaborative Research: Scalable Nonparametric Learning for Massive Data with Statistical Guarantees. Duration: 09/01/2018-08/31/2021.

NSF DMS-1764280. Principal Investigator on Collaborative Research: Nonparametric Bayesian Aggregation for Massive Data. Duration: 09/01/2017-08/31/2020.