Staff profile

I am an Associate Professor in Statistics at the Department of Mathematical Sciences at Durham University in UK. 

I have worked as a postdoctoral researcher in the Department of Mathematics of the Purdue University, and as a postdoctoral researcher in the Uncertainty Quantification group in the Pacific Northwest National Laboratory in USA. 

I hold a PhD degree in Mathematics (Statistics) from the School of Mathematics at the University of Bristol, and a BSc degree in Statistics from the Department of Statistics at the Athens University of Economical and Business studies. 

I am a Bayesian statistician with particular research interests in the development of methods for (i.) statistical modelling to address Bayesian computer model calibration and uncertainty quantification (UQ) problems; (ii.) statistical computing to facilitate inference in complex statistical models; and (iii.) machine learning. 

A number of my recent research projects/developments address modern statistical challenges such as `Big Data' and High-Dimensional problems one can meet in real applications, while they can be implemented in parallel computing environments.

I am teaching "MATH4341: Spatio-Temporal Statistics IV" and "MATH3431: Machine Learning and Neural Networks III".

Publications: https://www.maths.dur.ac.uk/~mffk55/publications.html

Some areas: https://www.maths.dur.ac.uk/~mffk55/research.html

Teaching: https://www.maths.dur.ac.uk/~mffk55/teaching.html

• Bayesian statistics
• Machine learning, and Big-data analysis
• Computational statistics, and Markov chain Monte Carlo
• Uncertainty Quantification

• 2020: IEEE, International Conference on Tools with Artificial Intelligence (ICTAI): Financial Chair (Organ.), Registration Chair, Program Area Chair

Chapter in book

• Alamaniotis, M., & Karagiannis, G. (2023). Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting. In I. Hatzilygeroudis, G. Tsihrintzis, & L. Jain (Eds.), Fusion of Machine Learning Paradigms (119-127). Springer International Publishing
• 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

Conference Paper

• Alamaniotis, M., Martinez-Molina, A., & Karagiannis, G. (2021). Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs. . https://doi.org/10.1109/powertech46648.2021.9494757
• Alamaniotis, M., & Karagiannis, G. (2019). ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets. . https://doi.org/10.1109/eem.2019.8916549
• Alamaniotis, M., & Karagiannis, G. (2019). Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation. In 2019 IEEE Milan PowerTech. https://doi.org/10.1109/ptc.2019.8810415
• Alamaniotis, M., & Karagiannis, G. (2018). Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes. . https://doi.org/10.1049/cp.2018.1888
• Nasiakou, A., Alamaniotis, M., Tsoukalas, L. H., & Karagiannis, G. (2017). A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm. In 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). https://doi.org/10.1109/iisa.2017.8316375

Doctoral Thesis

• Karagiannis, G. AISRJMCMC - Annealed Importance Sampling within Reversible Jump Markov Chain Monte Carlo algorithm : a pseudo-marginal reversible jump MCMC algorithm. (Thesis). [Awarding Organisation: Unknown]. Retrieved from https://durham-repository.worktribe.com/output/1618394

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, https://doi.org/10.1002/env.2844
• 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
• 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
• 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
• 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
• Konomi, B., & Karagiannis, G. (2021). Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model. Technometrics, 63(4), 510-522. https://doi.org/10.1080/00401706.2020.1855253
• 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, 36(5), Article e3329. https://doi.org/10.1002/cnm.3329
• Alamaniotis, M., & Karagiannis, G. (2020). Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed. IET Renewable Power Generation, 14(1), 100-109. https://doi.org/10.1049/iet-rpg.2019.0538
• Karagiannis, G., Konomi, B., & Lin, G. (2019). On the Bayesian calibration of expensive computer models with input dependent parameters. Spatial Statistics, 34, Article 100258. https://doi.org/10.1016/j.spasta.2017.08.002
• Karagiannis, G., & 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. https://doi.org/10.1016/j.jcp.2017.04.003
• Alamaniotis, M., & 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, 5(3), 1-14. https://doi.org/10.4018/ijmstr.2017070101
• Konomi, B., Karagiannis, G., Lai, C., & Lin, G. (2017). Bayesian Treed Calibration: an application to carbon capture with AX sorbent. Journal of the American Statistical Association, 112(517), 37-53. https://doi.org/10.1080/01621459.2016.1190279
• Karagiannis, G., Konomi, B., Lin, G., & Liang, F. (2016). Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation. Statistics and Computing, 27(4), 927-945. https://doi.org/10.1007/s11222-016-9663-0
• Zhang, B., Konomi, B., Sang, H., Karagiannis, G., & Lin, G. (2015). Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. Journal of Computational Physics, 300, 623-642. https://doi.org/10.1016/j.jcp.2015.08.006
• Karagiannis, G., Konomi, B., & 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. https://doi.org/10.1016/j.jcp.2014.12.034
• Konomi, B., Karagiannis, G., & 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. https://doi.org/10.1016/j.jspi.2014.08.010
• Konomi, B., Karagiannis, G., Sarkar, A., Sun, X., & Lin, G. (2014). Bayesian treed multivariate Gaussian process with adaptive design: Application to a carbon capture unit. Technometrics, 56(2), 145-158. https://doi.org/10.1080/00401706.2013.879078
• Karagiannis, G., & 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. https://doi.org/10.1016/j.jcp.2013.11.016
• Karagiannis, G., & Andrieu, C. (2013). Annealed Importance Sampling Reversible Jump MCMC Algorithms. Journal of Computational and Graphical Statistics, 22(3), 623-648. https://doi.org/10.1080/10618600.2013.805651

Presentation

• 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