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Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets (2024)
Journal Article
Cheng, S., Konomi, B. A., Karagiannis, G., & Kang, E. L. (2024). Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets. Environmetrics, 35(4), Article e2844. https://doi.org/10.1002/env.2844

Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co‐kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associate... Read More about Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets.

Ice Model Calibration using Semi-continuous Spatial Data (2022)
Journal Article
Chang, W., Konomi, B., Karagiannis, G., Guan, Y., & Haran, M. (2022). Ice Model Calibration using Semi-continuous Spatial Data. Annals of Applied Statistics, 16(3), 1937-1961. https://doi.org/10.1214/21-aoas1577

Rapid changes in Earth’s cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and projecting the future of Arctic and Antarctic ice sheets. However, th... Read More about Ice Model Calibration using Semi-continuous Spatial Data.

Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework (2022)
Journal Article
Karagiannis, G., Hou, Z., Huang, M., & Lin, G. (2022). Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework. Computation, 10(5), Article 72. https://doi.org/10.3390/computation10050072

In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great... Read More about Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework.

Multifidelity computer model emulation with high‐dimensional output: An application to storm surge (2022)
Journal Article
Ma, P., Karagiannis, G., Konomi, B., Asher, T., Toro, G., & Cox, A. (2022). Multifidelity computer model emulation with high‐dimensional output: An application to storm surge. Journal of the Royal Statistical Society: Series C, 71(4), 861-883. https://doi.org/10.1111/rssc.12558

Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm... Read More about Multifidelity computer model emulation with high‐dimensional output: An application to storm surge.

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction (2021)
Presentation / Conference Contribution
Deng, W., Feng, Q., Karagiannis, G., Lin, G., & Liang, F. (2021, December). Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. Paper presented at International Conference on Learning Representations (ICLR'21), Virtual Event

Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction for avoiding biases from noisy energy estimators has limited the potent... Read More about Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction.

Introduction to Bayesian Statistical Inference (2021)
Book Chapter
Karagiannis, G. (2022). Introduction to Bayesian Statistical Inference. In L. Aslett, F. Coolen, & J. De Bock (Eds.), Uncertainty in Engineering: Introduction to Methods and Applications (1-13). (1). Springer Verlag. https://doi.org/10.1007/978-3-030-83640-5_1

We present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We pre... Read More about Introduction to Bayesian Statistical Inference.

Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs (2021)
Presentation / Conference Contribution
Alamaniotis, M., Martinez-Molina, A., & Karagiannis, G. (2023, June). Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs. Presented at 2021 IEEE Madrid PowerTech, Madrid, Spain

One of the pillars in developing smart power systems is the use of load forecasting methods. In particular load forecasting accommodates decision making pertained to the operation of power market. In this paper, a new method for real-time updating ve... Read More about Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs.

Hierarchical Bayesian Nearest Neighbor Co-Kriging Gaussian Process Models; An Application to Intersatellite (2021)
Journal Article
Cheng, S., Konomi, B., Matthews, J., Karagiannis, G., & Kang, E. (2021). Hierarchical Bayesian Nearest Neighbor Co-Kriging Gaussian Process Models; An Application to Intersatellite. Spatial Statistics, 44, Article 100516. https://doi.org/10.1016/j.spasta.2021.100516

Recent advancements in remote sensing technology and the increasing size of satellite constellations allow for massive geophysical information to be gathered daily on a global scale by numerous platforms of different fidelity. The auto-regressive co-... Read More about Hierarchical Bayesian Nearest Neighbor Co-Kriging Gaussian Process Models; An Application to Intersatellite.