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Welcome to my website! My name is Zhiling Gu. I am currently a postdoctoral associate at Yale School of Public Health, advised by Professor Yize Zhao.
Prior to that, I obtained my Ph.D. degree in Statistics at Iowa State University (ISU), advised by Professor Lily Wang and Professor Dan Nettleton.
My research focus is developing interpretable and scalable statistical methods for data arising from public health and medicine, including mental health, aging, and Alzheimer’s Disease. My work involves theory development in functional data analysis, network analysis, spatiotemporal data analysis, statistical foundations of artificial intelligence, and nonparametric learning, with applications in neuroimaging, electronic health records, and environmental studies.
Email: zhiling.gu [at] yale [dot] edu
Address: 300 George St., New Haven, CT 06511
Links: Google Scholar, NCBI Bibliography, ORCID, GitHub
CV: Available on request
Peer-Reviewed Articles
Peer-Reviewed Articles
9. Gu, Z., Li, X., Wang, G., Wang, L. (2025+, To appear at Journal of Time Series Analysis). Spatiotemporal Heterogeneity Learning: Generalized SpatioTemporal Mixed Coefficient Models with Structure Identification.
8. Gu, Z., Yu, S., Wang, G., Lai, M. J., & Wang, L. (2025). TSSS: a novel triangulated spherical spline smoothing for surface-based data. Journal of Nonparametric Statistics, 1–30.
DOI: https://doi.org/10.1080/10485252.2025.2449886
[An earlier version won runner-up of Statistical Methods in Imaging Conference 2023 (SMI 2023) Student Paper Competition, theory track]
7. Lopez, V. K., Cramer, E. Y., Pagano, R., Drake, J. M., O’Dea, E. B., Adee, M., [et al, including Gu, Z.] (2024). Challenges of COVID-19 Case Forecasting in the US, 2020–2021. PLoS Computational Biology, 20(5).
DOI: https://doi.org/10.1371/journal.pcbi.1011200
6. Ananya, A., Holden, K. G., Gu, Z., Nettleton, D., Mallapragada, S. K., Wannemuehler, M. J., Kohut, M. L., Narasimhan. B. (2023). “Just right” combinations of adjuvants with nanoscale carriers activate aged dendritic cells without overt inflammation. Immunity & Ageing. 20, 10.
DOI: https://doi.org/10.1186/s12979-023-00332-0
5. Cramer, E. Y., Ray, E. L., Lopez, V. K., [et al, including Gu, Z.] (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.
DOI: https://doi.org/10.1073/pnas.2113561119
4. Cramer, E., Ray, E., Lopez, V. K., [et al, including Gu, Z.] (2022). The United States COVID-19 Forecast Hub dataset. Scientific Data. 9(1), 462.
DOI: https://doi.org/10.1038/s41597-022-01517-w
3. 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: https://doi.org/10.6339/21-JDS1013
2. 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, 50(11-12), 2408-2434.
DOI: https://doi.org/10.1080/02664763.2021.1928016
1. 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.
DOI: https://www.ams.org/journals/notices/202104/rnoti-p585.pdf
Teaching
Teaching
Instructor @Iowa State University
STAT 305: Engineering Statistics, Summer 2021
STAT 226: Introduction to Business Statistics I, Fall 2021, Fall 2020
Teaching Assistant @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 & Activities
Presentations & Activities
Contributed poster, “Challenges in Neuroimaging Data Analysis’’, Institute for Mathematical and Statistical Innovation, Chicago - Aug 2024
Invited talk, SMI 2024, Indiana University School of Medicine - May 2024
Contributed discussion of “Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis”, ISU Causal Inference Working Group - Nov 2023
Contributed poster, ISU Statistics 75th Anniversary Research Conference - Sept 2023
Participant, “Invitation to Algebraic Statistics and Applications”. Institute for Mathematical and Statistical Innovation, Chicago - Sept 2023
Invited talk, EcoSta 2023, Waseda University, Japan - Aug 2023
Invited talk, ICSA 2023 Applied Statistics Symposium, University of Michigan - June 2023
Contributed discussion of “Parameterizing and Simulating from Causal Models”. ISU Causal Inference Working Group - May 2023
Invited talk, SMI 2023, University of Minnesota - May 2023
Invited talk, CMStatistics 2022, King’s College London - Dec 2022
Contributed poster, The 35th New England Statistics Symposium, University of Connecticut - May 2022
Contributed discussion of “Adaptive Conformal Inference under Distribution Shift” by Isaac Gibbs and Emmanuel J. Candès. TrAC Journal Club, Iowa State University - April 2022
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