Georgios Karagiannis

Research

My research interests lie in Bayesian statistics, uncertainty quantification, machine learning, and statistical computing. My work focuses on the development of both Bayesian models to address UQ and machine learning problems, as well as computational algorithms to facilitate inference in complex statistical models.

- Bayesian methods

- Statistical machine learning, & Uncertainty quantification

- Monte Carlo methods

stochastic approximation Monte Carlo

- Applications: Climate, and Engineering,

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Results:

References:

- Ma, P., Karagiannis, G., Konomi, B.A., Asher, T.G., Toro, G.R. & Cox, A.T. (2022) Multifidelity computer model emulation with high-dimensional output: An application to storm surge. Journal of the Royal Statistical Society: Series C, 1–23
- Karagiannis, G., Konomi, B. A., and Lin, G. (2015). A Bayesian
mixed shrinkage prior procedure for spatial-stochastic basis
selection and evaluation of gPC expansions: Applications to
elliptic SPDEs.
*Journal of Computational Physics*, 284:528 - 546. - Konomi, B. A., Karagiannis, G., and Lin, G. (2015). On the
Bayesian treed multivariate Gaussian process with linear model of
coregionalization.
*Journal of Statistical Planning and Inference*, 157-158:1 - 15. - Zhang, B., Konomi, B. A., Sang, H., Karagiannis, G., and Lin, G.
(2015). Full scale multi-output Gaussian process emulator with
nonseparable auto-covariance functions.
*Journal of Computational Physics*, 300:623 - 642. - Karagiannis, G. and Lin, G. (2014). Selection of polynomial
chaos bases via Bayesian model uncertainty methods with
applications to sparse approximation of PDEs with stochastic
inputs.
*Journal of Computational Physics*, 259:114 - 134. - Konomi, B. A., Karagiannis, G., Sarkar, A., Sun, X., and Lin, G.
(2014). Bayesian treed multivariate Gaussian process with adaptive
design: Application to a carbon capture unit.
*Technometrics*, 56(2):145- 158.

Description:

References:

- Chang, W., Konomi, B. A., Karagiannis, G., Guan, Y., & Haran, M. (2022). Ice Model Calibration Using Semi-continuous Spatial Data. Annals of Applied Statistics.
- Cheng, S., Konomi, B. A., Matthews, J. L., Karagiannis, G., & Kang, E. L. (2021). Hierarchical Bayesian nearest neighbor co-kriging Gaussian process models; an application to intersatellite calibration. Spatial Statistics, 100516.
- Karagiannis, G., Konomi, B. A., and Lin, G. (2019). On the
Bayesian calibration of expensive computer models with input
dependent parameters,
*Spatial Statistics* - Konomi, B. A., Karagiannis, G., Lai, K., and Lin, G. (2017).
Bayesian treed calibration: An application to carbon capture with
AX sorbent.
*Journal of the American Statistical Association*, 112(517):37-53.

**Selection, and combination of computer models
**

**Description:** For many real systems, several computer
models (simulators) may exist with different physics and
predictive abilities. To achieve more accurate
simulations/predictions, it is desirable for these models to be
properly combined and calibrated. In the same context, are the
problems that involve fast & slow simulators that is, when one
simulator is slow to run but very accurate while the other is slow
to run but less accurate. In other cases, the simulator may
require the selection of a sub-model (from a set of available
ones) in order to run, and hence it is desirable to select the
`best' one.

**Results:** The development of Bayesian procedures able to
combine the different simulators, such that the contribution of
each simulator is different at different input values. Moreover,
we have worked on the development of procedures able to select the
`best' sub-model, which may be different at different inputs, from
a set of available ones in the Bayesian framework.

**References:**

- Ma, P., Karagiannis, G., Konomi, B.A., Asher, T.G., Toro, G.R. & Cox, A.T. (2022) Multifidelity computer model emulation with high-dimensional output: An application to storm surge. Journal of the Royal Statistical Society: Series C, 1–23
- Konomi, B. A., & Karagiannis, G. (2020). Bayesian analysis
of multifidelity computer models with local features and
non-nested experimental designs: Application to the WRF model.
*Technometrics.*, 1-31. - Karagiannis, G., Konomi, B. A., and Lin, G. (2019). On the
Bayesian calibration of expensive computer models with input
dependent parameters,
*Spatial Statistics* - Karagiannis, G. and Lin, G. (2017). On the Bayesian
calibration of computer model mixtures through experimental
data, and the design of predictive models.
*Journal of Computational Physics*, 342:139 - 160.

- Deng, W., Feng, Q., Karagiannis, G., Lin, G., & Liang, F. (2021). Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. International Conference on Learning Representations (ICLR'21).
- Karagiannis, G., Konomi, B. A., Lin, G., and Liang, F. (2017).
Parallel and interacting stochastic approximation annealing
algorithms for global optimisation.
*Statistics and Computing*, 27(4):927–945. [arXiv] [Supplementary material] - Karagiannis, G. and Andrieu, C. (2013). Annealed importance
sampling reversible jump MCMC algorithms.
*Journal of Computational and Graphical Statistics*, 22(3):623-648.

**Interdisciplinary Research
**

- 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), 72.
- Alamaniotis, M., Martinez-Molina, A., & Karagiannis, G. (2021, June). Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs. In 2021 IEEE Madrid PowerTech (pp. 1-6). IEEE.
- Karagiannis, G., Hao, W., & Lin, G. (2020) Calibrations and validations of biological models with an application on the renal fibrosis. International Journal for Numerical Methods in Biomedical Engineering, e3329.
- Alamaniotis, M., and Karagiannis, G. (2017). Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 5(3), 1-14.
- Alamaniotis, M., & Karagiannis, G. (2018), Genetic Driven Multi-Relevance Vector Regression Forecasting of Hourly Wind Speed in Smart Power Systems, IEEE PES Innovative Smart Grid Technologies – North America, pp. 1-5.
- Nasiakou, A., Alamaniotis, M., Toukalas, L.H. & Karagiannis, G. (2017), A Three-Stage Scheme for Consumers' Partitioning Using Hierarchical Clustering Algorithm, 8th International Conference on Information, Systems and Applications (IISA). Larnaca, Cyprus, 6.