
Bledar Alex Konomi
Associate Professor, Statistics
French Hall
5043
A&S Mathematical Sciences - 0025
Education
PhD in Statistics, Texas A&M University College Station, 2011
BS, Athens University of Economics and Business Greece, 2005 (Statistics)
Positions and Work Experience
10-20-2011 -06-20-2014 Postdoctoral Research Assistant , Pacific Northwest National Laboratory, Richland, WA
Research and Practice Interests
My research area is in Bayesian statistics, computational methods of large spatial and computer simulation data sets ("big data"), and uncertainty quantification. My primary applications have been in spatial and spatio-temporal statistics, sequential design, computer model calibration and image analysis.
Research Support
Grant: #Sub #1650478 Investigators:Kang, Lei; Konomi, Bledar 05-01-2020 -12-31-2020 California Institute of Technology Jet Propulsion Laboratory (JPL) LARGE-SCALE MULTIVARIATE SPATIAL MODLEING FOR UNCERTIANTY QUANTIFICATION FOR AIRS MISSION Role:Collaborator $48,171.00 Awarded Level:Institution of Higher Education
Publications
Peer Reviewed Publications
Konomi, B. A., Hanandeh, A. A., Ma, P., Kang, E. L. (2019. ) Computationally Efficient Nonstationary Nearest Neighbor Gaussian Process Models Using Data-driven Techniques .Environmetrics, , e2571
Ma, P., Konomi, B. A., Kang, E. L. (2019. ) An Additive Approximate Gaussian Process for Large Spatio-Temporal Data .Environmetrics, , e2569 ,
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., 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. A., and Lin, G (2019. ) On the Bayesian calibration of expensive computer models with input dependent parameters.Spatial Statistics, , 34 (December ) ,100258
Shi H., Kang E.L., Konomi B.A., Vemaganti K., Madireddy S. (2017. ) Uncertainty Quantification Using the Nearest Neighbor Gaussian Process. New Advances in Statistics and Data Science. ICSA Book Series in Statistics. Springer, Cham, ,
Konomi, B. A. and Lin, G. (2015. ) Low-Cost Multi-dimensional Gaussian Process with Application to Uncertainty Quantification .International Journal for Uncertainty Quantification, , 5 (4 ) ,375-392
Konomi, B. A. , Karagiannis, G., Lin, G. (2015. ) On the Bayesian Treed Multivariate Gaussian Process with Linear Model of Coregionalization .Journal of Statistical Planning and Inference, , 157 (3 ) ,1–15
Karagiannis, G. , Konomi, B. A., & 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 (1 March ) ,528–546
Zhang, B., Konomi, B. A., Sang, H., Karagiannis, G., & Lin, G. (2015. ) Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions .Journal of Computational Physics, , 300 (November ) ,623–642
Konomi*, B. A., Sang, H. , Mallick, B. K (2014. ) Adaptive Bayesian nonstationary modeling for large spatial datasets using covariance approximations .Journal of Computational and Graphical Statistics, , 23 (3 ) ,802-829
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
Bilionis, I, Zabaras, N, Konomi, B, Lin, G (2013. ) Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification .Journal of Computational Physics, , 241 (1 ) , 212–239
Konomi,B., S. Dhavala, J. Huang, S. Kundu, D. Huitink, H. Liang, Y. Ding, and B. K. Mallick, (2013. ) Bayesian object segmentation and classification of gold nano-particles . Annals of Applied Statistics , , 7 (2 ) ,640-668
Konomi, B. A., and Karagiannis, G. (2020. ) Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model.Technometrics , ,
Pulong Ma, Anirban Mondal, Bledar A. Konomi, Jonathan Hobbs, Joon JinSong, & Emily L. Kang (2021. ) Computer Model Emulation with High-Dimensional Functional Output in Large-ScaleObserving System Uncertainty Experiments.Technometrics, ,
Other Publications
P Ramuhalli, G Lin, SL Crawford, B Konomi, BG Braatz (2014. ) Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants . Pacific Northwest National Laboratory
Student Advising
Si Cheng (Doctoral ) Advisor Status:In Progress
Seth Bennett (Doctoral ) Advisor Status:In Progress
Pulong Ma (Doctoral ) Co-Chair Status:Completed 06-01-2018
Ahmad A. Hanandeh (Doctoral ) Chair Status:Completed 08-2017
Courses Taught
-STAT-6021 MATH STATS I Level:Graduate
-STAT-3038 Probability & Statistics II Level:Undergraduate