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

• Statistics

• Bayesian statistics III/IV (MATH3341/MATH4031)
• Machine Learning and Neural Networks III (MATH3431)
• Topics in Statistics III/IV (MATH3361/MATH4071)

• 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. (Published). Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting. In Fusion of Machine Learning Paradigms. Hatzilygeroudis, I. K. Tsihrintzis, G. A. & Jain, L. C. Springer International Publishing. 236: 119-127.
• Karagiannis, G. P. (2022). Introduction to Bayesian Statistical Inference. In Uncertainty in Engineering: Introduction to Methods and Applications. Aslett, L. J. M., Coolen, F. P. A. & De Bock, J. Cham: Springer. 1-13.

Conference Paper

• Alamaniotis, Miltiadis, Martinez-Molina, Antonio & Karagiannis, Georgios (2021), Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs, 2021 IEEE Madrid PowerTech. Madrid, Spain, IEEE.
• 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). Virtual Event.
• Alamaniotis, Miltiadis & Karagiannis, Georgios (2019), ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets, 2019 16th International Conference on the European Energy Market (EEM). Ljubljana, Slovenia, IEEE.
• Alamaniotis, Miltiadis & Karagiannis, Georgios (2019), Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation, 2019 IEEE Milan PowerTech. 1.
• Alamaniotis, M. & Karagiannis, G. (2018), Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes, Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018). Dubrovnik, Croatia, IET.
• Nasiakou, Antonia, Alamaniotis, Miltiadis, Tsoukalas, Lefteri H. & Karagiannis, Georgios (2017), A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm, 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). 1.

Doctoral Thesis

• Karagiannis, Georgios (2011). AISRJMCMC - Annealed Importance Sampling within Reversible Jump Markov Chain Monte Carlo algorithm a pseudo-marginal reversible jump MCMC algorithm. PhD.

Journal Article

• Chang, W. Konomi, B. A. Karagiannis, G. Guan, Y. & Haran, M. (2022). Ice Model Calibration using Semi-continuous Spatial Data. Annals of Applied Statistics 16(3): 1937-1961.
• 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.
• 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 71(4): 861-883.
• 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. Spatial Statistics 44: 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.
• 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): e3329.
• 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.
• Karagiannis, G., Konomi, B. A. & Lin, G. (2019). On the Bayesian calibration of expensive computer models with input dependent parameters. Spatial Statistics 34: 100258.
• 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.
• 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.
• 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.
• Karagiannis, G., Konomi, B., Lin, G. & Liang, F. (2017). Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation. Statistics and Computing 27(4): 927-945.
• 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.
• 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.
• 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.
• 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.
• 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.
• Karagiannis, G. & Andrieu, C. (2013). Annealed Importance Sampling Reversible Jump MCMC Algorithms. Journal of Computational and Graphical Statistics 22(3): 623-648.