Zhiling Gu


I am a 4th year Ph.D. student in Statistics at Iowa State University (ISU), where I am supervised by Professor Lily Wang and Professor Dan Nettleton. Before joining ISU, I completed M.Phil. in Risk Management Science advised by Professor Hoi-Ying Wong at The Chinese University of Hong Kong.

I am interested in 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

  1. 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.

  2. 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]

  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]

  4. 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. [PDF]

  5. 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]


Teaching

Courses Taught @Iowa State University

  • STAT 226: Introduction to Business Statistics I, Fall 2021, Fall 2020

  • STAT 305: Engineering Statistics, Summer 2021


Courses TA’ed @Iowa State University

  • STAT 486/586: Introduction to Statistical Computing, Spring 2021

  • STAT 507X: Statistical Learning of Infectious Disease Analytics, Spring 2021

  • STAT 101: Principles of Statistics, Spring 2020

  • STAT 226: Introduction to Business Statistics I, Spring 2020

  • STAT 231: Probability and Statistical inference for engineers, Fall 2019


Presentations

  • "Structure Identification for Space-time Epidemic Models", King's College London. Dec, 2022. The 15th International Conference of the ERCIM WG on Computational and Methodological Statistics, King's College London.

  • "Structure Identification for Space-time Epidemic Models". May, 2022. The 35th New England Statistics Symposium, University of Connecticut.

  • Discussion of the paper "Adaptive Conformal Inference under Distribution Shift" by Isaac Gibbs and Emmanuel J. Candès, April, 2022. @TrAC Journal Club, Iowa State Univeristy. [PDF]