New preprints:
Wikle, C. K., North, J., Gopalan, G., & Yoo, M. (2025). A Statistician's Overview of Mechanistic-Informed Modeling. arXiv preprint arXiv:2507.14336. LINK
Volkoff, T., & Gopalan, G. (2025). Length scale estimation of excited quantum oscillators. arXiv preprint arXiv:2501.18673. LINK
Published papers (with links):
Gopalan, G., Vrtilek, S. D., & Bornn, L. (2015). Classifying X-ray Binaries: A Probabilistic Approach. The Astrophysical Journal, 809(1), 40. LINK
Gopalan, G., Plavchan, P., van Eyken, J., Ciardi, D., von Braun, K., & Kane, S. R. (2016). Application of the Trend Filtering Algorithm for Photometric Time Series Data. Publications of the Astronomical Society of the Pacific, 128(966), 084504. LINK
Gopalan, G., Hrafnkelsson, B., Aðalgeirsdóttir, G., Jarosch, A. H., & Pálsson, F. (2018). A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions. The Cryosphere, 12(7), 2229-2248. LINK
Gopalan, G., Hrafnkelsson, B., Wikle, C. K., Rue, H., Aðalgeirsdóttir, G., Jarosch, A. H., & Pálsson, F. (2019). A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology. Journal of Agricultural, Biological and Environmental Statistics, 24(4), 669-692. LINK
2019 Laplace Award from the American Statistical Association, Section on Bayesian Statistical Science.
Gopalan, G., Hrafnkelsson, B., Aðalgeirsdóttir, G., & Pálsson, F. (2021). Bayesian inference of ice softness and basal sliding parameters at Langjökull. Frontiers in Earth Science, 9, 314. LINK
Gopalan, G., & Wikle, C. K. (2021). A Higher-Order Singular Value Decomposition Tensor Emulator for Spatiotemporal Simulators. Journal of Agricultural, Biological and Environmental Statistics, 27, 22–45. LINK
Selected "best paper" of Journal of Agricultural, Biological and Environmental Statistics in 2021 by journal editors.
de Beurs, Z. L., Islam, N., Gopalan, G., & Vrtilek, S. D. (2022). A Comparative Study of Machine-learning Methods for X-Ray Binary Classification. The Astrophysical Journal, 933(1), 116. LINK
Skurikhin, A.N., Gopalan, G., Klein, N., & Casleton, E. (2023). Post-hoc Uncertainty Quantification of Deep Learning Models Applied to Remote Sensing Image Scene Classification. ITEA Journal, 44(3). LINK
Gopalan, G., Zammit-Mangion, A., & McCormack, F. (2023). A Review of Bayesian Modelling in Glaciology. LINK. Book chapter in Statistical Modeling Using Bayesian Latent Gaussian Models With Applications in Geophysics and Environmental Sciences. Published by Springer. Edited by Birgir Hrafnkelsson. arXiv
Mathews, J., Gopalan, G., Gattiker, J., Smith, S., & Francom, D. (2024). Sequential Monte Carlo for Cut-Bayesian Posterior Computation. Computational Statistics. LINK
Yoo, M., Gopalan, G., Hoffman, M., Coulson, S., Han, H. K., Wikle, C. K., & Hillebrand, T. (2025). Emulation with uncertainty quantification of regional sea-level change caused by the Antarctic Ice Sheet. Journal of Geophysical Research: Machine Learning and Computation. LINK
Software (with links):
Gopalan, G. & Bornn, L. (2015). FastGP: An R Package for Gaussian Processes. Comprehensive R Archive Network (CRAN). LINK
Workshop papers:
Longjohn, R., Gopalan, G., & Casleton, E. (2025). Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks. arXiv preprint arXiv:2501.04234. Presented at NeurIPS 2024 Workshop on Statistical Frontiers in LLMs and Foundation Models.
Unpublished (with links):
Gopalan, G. (2017). Admissibility of a posterior predictive decision rule. arXiv
Gopalan, G. (2017). Quantification of observed prior and likelihood information in parametric Bayesian modeling. arXiv; MaxEnt2018 poster
Articles: