• Greiffenstein, E., Harris, T., Smith, B. (2025). Forecasting West Nile Virus with Deep Graph Encoders. Annals of Applied Statistics. In review.

  • Harris, T., Liu, Y. (2025). Locally Adaptive Conformal Inference for Operator Models. International Conference on Learning Representations 14 (ICLR). In review.

  • Garrett, R., Harris, T., Wang, Z., & Li, B. (2025). Sliced Elastic Distance for Evaluating Amplitude and Phase Differences in Precipitation Models. Technometrics. In review.

  • Wang, M., Yan, S., Harris, T., Shand, L., Li, B. (2025). Evaluating Fingerprint of Mt. Pinatubo Eruption on Stratospheric Temperatures with Spatial Functional Changepoints. Annals of Applied Statistics. In review.

  • Abdi, A., Juarez, J., Harris, T., Magalhaes, T., Hamer, G. (2025). Systematic review of Aedes aegypti control trials reveals publication bias related to author disclosure of conflicts of interest. Emerging Infectious Diseases. In review.

  • Harris, T., Sriver, R. (2024). Quantifying uncertainty in climate projections with conformal ensembles. Annals of Applied Statistics. Minor revision, submitted.

  • Garrett, R., Harris, T., & Li, B. (2024). Validating Climate Models with Spherical Convolutional Wasserstein Distance. Advances in Neural Information Processing Systems 37 (Spotlight).

  • Tonks, A., Harris, T., Li, B., Brown, W., & Smith, R. (2024). Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data. GeoHealth, 8(7), e2023GH000784.

  • Harris, T., Li, B., & Sriver, R. (2023). Multimodel ensemble analysis with neural network Gaussian processes. Annals of Applied Statistics, 17(4), 3403–3425.

  • Fimbres-Macias, J., Harris, T. A., Hamer, S., & Hamer, G. (2023). Phenology and environmental predictors of Triatoma sanguisuga dispersal in east-central Texas, United States. Acta Tropica, 240, 106862.

  • Wang, M., Harris, T., & Li, B. (2023). Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series. Journal of Agricultural, Biological and Environmental Statistics, 28(1), 157–176.

  • Ringer, R. J., Yoon, H., Kadeethum, T., & Harris, T. (2022). Machine learning applications for estimation of greenhouse gas emissions using multiple satellite images. Sandia National Lab. (SNL-NM), Albuquerque, NM, No. SAND2022-16609C.

  • Carmody, D., Mazzarello, M., Santi, P., Harris, T., Lehmann, S., Abbiasov, T., Dunbar, R., & Ratti, C. (2022). The effect of co-location on human communication networks. Nature Computational Science, 2(8), 494–503.

  • Harris, T., Li, B., & Tucker, J. D. (2022). Scalable multiple changepoint detection for functional data sequences. Environmetrics, 33(2), e2710.

  • Harris, T., Li, B., Steiger, N. J., Smerdon, J. E., Narisetty, N., & Tucker, J. D. (2021). Evaluating proxy influence in assimilated paleoclimate reconstructions—Testing the exchangeability of two ensembles of spatial processes. Journal of the American Statistical Association, 116(535), 1100–1113.

  • Harris, T., Tucker, J. D., Li, B., & Shand, L. (2021). Elastic depths for detecting shape anomalies in functional data. Technometrics, 63(4), 466–476.

  • Harris, T., & Li, B. (2014). Kriging. Wiley StatsRef: Statistics Reference Online, 1–11.