Zhiling Gu


Zhiling Gu is a Ph.D. candidate in Statistics at Iowa State University (ISU), supervised by Professor Lily Wang and Professor Dan Nettleton

Her research interests include non-/semi-parametric regression methods, epidemic modeling,  spatial/spatiotemporal data analysis, and high dimensional data analysis.


[GoogleScholar] [GitHub] [ISU]

Email: zlgu [at] iastate [dot] edu

Papers/Preprints

[7] Gu, Z., Yu, S., Wang, G., Wang, L. TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-based Imaging. (In preparation). [Runner-up, SMI2023 Student Paper Competition]


[6] Gu, Z., Li, X., Wang, G., Wang, L. Structure Identification of Space-time Epidemic Models. (In preparation).

 

[5] Cramer, E. Y., Ray, E. L., Lopez, V. K., Bracher, J., Brennen, A., Castro Rivadeneira, A. J., ..., Gu, Z., & Georgescu, A. (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences, 119(15), e2113561119 [link]

 

[4] Kim, M., Gu, Z., Yu, S., Wang, G., Wang, L. (2021). Methods, Challenges, and Practical Issues of COVID-19 Projection: A Data Science Perspective. Journal of Data Science, 19(2), 219-242. doi: 10.6339/21-JDS1013 [link]

 

[3] Wang, G., Gu, Z., Li, X., Yu, S., Kim, M., Wang, Y., Gao, L., Wang, L. (2021). Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing. Journal of Applied Statistics. doi: 10.1080/02664763.2021.1928016 [link]

 

[2] Wang, L., Wang, G., Li, X., Yu, S., Kim, M., Wang, Y., Gu, Z., Gao, L. (2021). Modeling and forecasting COVID-19. AMS: Notices of The American Mathematical Society, 68, 585-595 [link

 

[1] Wang, L., Wang, G., Gao, L., Li, X., Yu, S. Kim, M., Wang, Y. Gu, Z. (2020). Spatiotemporal dynamics, nowcasting and forecasting of COVID-19 in the United States. arXiv: 2004.14103 [link]


Teaching

Courses Taught @Iowa State University



Courses TA’ed @Iowa State University


Presentations

[6] “TSSS: A novel triangulated spherical spline smoothing for data distributed on complex surfaces”. EcoSta 2023, Waseda University, Japan - Aug 2023 (upcoming) 

[5] “TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-based Imaging”. ICSA 2023 Applied Statistics Symposium, University of Michigan  - June 2023 (upcoming) 

[4] “TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-based Imaging”. SMI 2023, University of Minnesota - May 2023 

[3] “Structure Identification for Space-time Epidemic Models”. CMStatistics 2022, King’s College London - Dec 2022

[2] “Structure Identification for Space-time Epidemic Models”. The 35th New England Statistics Symposium, University of Connecticut - May 2022

[1] “Discussion of ‘Adaptive Conformal Inference under Distribution Shift’ by Isaac Gibbs and Emmanuel J. Candès”. TrAC Journal Club, Iowa State University - April 2022