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Durham University

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Staff Profile

Georgios Karagiannis, PhD University of Bristol

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Assistant Professor, Statistics in the Department of Mathematical Sciences
Telephone: +44 (0) 191 33 42718
Room number: CM126b

(email at

Research interests

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.


Some areas:

Research Groups

Department of Mathematical Sciences

  • Probability & Statistics: Statistics
  • Probability & Statistics: Statistics
  • Probability and Statistics

Research Interests

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

Teaching Areas

  • Bayesian statistics III/IV --2017, 2019 (25 hours/year.)
  • Statistical methods III --2021 (21 hours/year.)
  • Statistics I --2017, 2019 (21 hours/year.)
  • Topics in statistics III/IV --2018, 2020 (25 hours/year.)

Selected Publications

Journal Article

  • 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.
  • Konomi, B. & Karagiannis, G. (2020). Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model. Technometrics
  • 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.
  • Karagiannis, G., Konomi, B. A. & Lin, G. (2019). On the Bayesian calibration of expensive computer models with input dependent parameters. Spatial Statistics 34: 100258.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Conference Paper

  • Deng, Wei, Feng, Qi, Karagiannis, Georgios, Lin, Guang & Liang, Faming (2021), Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction, International Conference on Learning Representations (ICLR'21).
  • 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). 1.
  • Alamaniotis, Miltiadis & Karagiannis, Georgios (2019), Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation, 2019 IEEE Milan PowerTech. 1.
  • 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.

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