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

• Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting
  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
• Introduction to Bayesian Statistical Inference
  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

• Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
  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
• Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs
  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
• Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation
  Alamaniotis, M., & Karagiannis, G. (2019, December). Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation. Presented at 2019 IEEE Milan PowerTech
• ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets
  Alamaniotis, M., & Karagiannis, G. (2019, September). ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets. Presented at 2019 16th International Conference on the European Energy Market (EEM), Ljubljana, Slovenia
• Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes
  Alamaniotis, M., & Karagiannis, G. (2018, November). Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes. Presented at Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018), Dubrovnik, Croatia
• A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm
  Nasiakou, A., Alamaniotis, M., Tsoukalas, L. H., & Karagiannis, G. (2017, December). A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm. Presented at 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA)

Conference Proceeding

• Proceedings of the 38th International Workshop on Statistical Modelling
  (2024, July). Proceedings of the 38th International Workshop on Statistical Modelling. Presented at 38th International Workshop on Statistical Modelling (IWSM), Durham, UK

Doctoral Thesis

• AISRJMCMC - Annealed Importance Sampling within Reversible Jump Markov Chain Monte Carlo algorithm : a pseudo-marginal reversible jump MCMC algorithm
  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]. https://durham-repository.worktribe.com/output/1618394

Journal Article

• Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets
  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
• Ice Model Calibration using Semi-continuous Spatial Data
  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
• Multifidelity computer model emulation with high‐dimensional output: An application to storm surge
  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
• Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework
  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
• Hierarchical Bayesian Nearest Neighbor Co-Kriging Gaussian Process Models; An Application to Intersatellite
  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
• Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model
  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
• Calibrations and validations of biological models with an application on the renal fibrosis
  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
• Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed
  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
• On the Bayesian calibration of expensive computer models with input dependent parameters
  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
• On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models
  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
• Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power
  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
• Bayesian Treed Calibration: an application to carbon capture with AX sorbent
  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
• Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation
  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
• Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions
  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
• On the Bayesian treed multivariate Gaussian process with linear model of coregionalization
  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
• A Bayesian mixed shrinkage prior procedure for spatial–stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs
  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
• Bayesian treed multivariate Gaussian process with adaptive design: Application to a carbon capture unit
  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
• Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs
  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
• Annealed Importance Sampling Reversible Jump MCMC Algorithms
  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