Bledar Alex Konomi

PhD in Statistics: Texas A&M University College Station, 2011

BS: Athens University of Economics and Business Greece, 2005 (Statistics)

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.

10-20-2011 -06-20-2014 Postdoctoral Research Assistant , Pacific Northwest National Laboratory, Richland, WA

08-2014 -08-2020 Assistant Professor, University of Cincinnati, Cincinnati, Ohio

08-2020 -To Present Associate Professor, University of Cincinnati, Cincinnati, Ohio

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

Grant: #DMS-2053668 Investigators:Kang, Lei; Konomi, Bledar 09-01-2021 -08-31-2024 National Science Foundation Collaborative Research: Inference and Uncertainty Quantification for High Dimensional Systems in Remote Sensing: Methods, Computation, and Applications Role:Collaborator 48261.00 Awarded Level:Federal

Peer Reviewed Publications

W. Chang, B. A. Konomi, G. Karagiannis, Y. Guan, M. Haran (2022. ) Ice Model Calibration Using Semi-continuous Spatial Data .Accepted to Annals of Applied Statistics, ,

P. Ma*, G. Karagiannis, B. A. Konomi, T. Gasher, G. Toro, A. Cox, (2022. ) Multifidelity Computer Model Emulation with High-Dimensional Output: An Application to Storm Surge .Accepted to Journal of the Royal Statistical Society, Series C (Applied Statistics), ,

Joshua H. Miller, Daniel C. Fisher, Brooke E. Crowley, and Bledar A. Konomi (2022. ) Male mastodon landscape use changed with maturation (late Pleistocene, North America).Proceedings of the National Academy of Sciences (PNAS), , 119 (25 ) , e2118329119 More Information

B. A. Konomi and G. Karagiannis (2021. ) Bayesian analysis of Multifidelity Computer Models with Local Features and Non-nested Experimental Designs: Application to the WRF model.Technometrics, , 63 , 510-522 More Information

P. Ma*, A. Mondal, B. A. Konomi, J. Hobbs, J. Song, and E. L. Kang (2021. ) Statistical Emulation for High-Dimensional Functional Outputs in Large-Scale Observing System Uncertainty Experiments.Technometrics, , More Information

Si Cheng*, B. A. Konomi, J. Matthews, G. Karagiannis, and E. Kang (2021. ) Hierarchical Bayesian Multifidelity Nearest Neighbor Gaussian Process Models; An Application to Intersatellite Calibration.Spatial Statistics, , 44 ,100516 More Information

G. Karagiannis, B. A. Konomi, and G. Lin (2019. ) On the Bayesian calibration of expensive computer models with input- dependent parameters.Spatial Statistics, , 34 ,100258 More Information

P. Ma*, B. A. Konomi, and E. L. Kang (2019. ) An Additive Gaussian Process Approximation for Large Spatio-Temporal Data.Environmetrics, , (env.2571 ) , More Information

B. A. Konomi, A. A. Hanandel*, P. Ma*, and E. L. Kang (2019. ) Computationally Efficient Nonstationary Nearest Neighbor Gaussian Process Models Using Data-driven Techniques.Environmetrics, , (env.2571 ) , More Information

G. Karagiannis, B. A. Konomi, and G. Lin (2017. ) Parallel and interacting stochastic approximation annealing algorithms for global optimisation.Statistics and Computing, , 27 ,927--945 More Information

B. A. Konomi, G. Karagiannis, K. Lai, and G. Lin (2017. ) Bayesian Treed Calibration: an application to carbon capture with AX sorbent.Journal of American Statistical Association (JASA), , 112 ,37--53 More Information

B. A. Konomi and G. Lin (2015. ) Low-Cost Multi-output Gaussian Process with Application to Computer Codes.International Journal for Uncertainty Quantification, , 5 ,375-392 More Information

G. Karagiannis, B. A. Konomi, and G. Lin (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 More Information

B. A. Konomi, G. Karagiannis and G. Lin (2015. ) On Bayesian Treed Multivariate Gaussian Process with Linear Model of Coregionalization.Journal of Statistical Planning and Inference, , 157 ,1-15 More Information

B. Zhang, B. A. Konomi, H. Sang and G. Lin (2015. ) Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions .Journal of Computational Physics, , 300 ,623--642

B. A. Konomi, H. Sang and B. K. Mallick (2014. ) Adaptive Bayesian nonstationary modeling for large spatial datasets using covariance approximations.Journal of Computational and Graphical Statistics, , 23 , 802--829 More Information

B. A. Konomi, G. Karagiannis, A. Sarkar, X. Sun and G. Lin (2014. ) Bayesian Treed Multivariate Gaussian Process with Adaptive Design: Application to a Carbon Capture Unit.Technomterics, , 56 ,145--158 More Information

B. A. Konomi, 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 ,640--668 More Information

I. Bilionis , N. Zabaras, B. A. Konomi, and G. Lin (2013. ) Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification.Journal of Computational Physics, , 241 ,212 -239More Information

Miller, Joshua H; Fisher, Daniel C; Crowley, Brooke E; Secord, Ross; Konomi, Bledar A (2022. ) Male mastodon landscape use changed with maturation (late Pleistocene, North America).Proceedings of the National Academy of Sciences of the United States of America, , 119 (25 ) ,e2118329119 More Information

Other Publications

J. Coble, G. Lin, B. A. Konomi, P. Ramuhalli (2013. ) Accurate uncertainty quantification to support online sensor calibration monitoring .Transactions of the American Nuclear Society, 109 ,429--431

Book Chapter

H. Shi, E. L. Kang, B. A. Konomi, K. Vemaganti, and S. Madireddy, (2017 ) Uncertainty Quantification Using the Nearest Neighbor Gaussian Process. New Advances in Statistics and Data Science. ICSA Book Series in Statistics, Springer, Cham .

Technical Reports

P. Ramuhalli, G. Lin, SL. Crawford, B. A. Konomi, B. Braatz, J. Coble, B. Shumaker, and H. Hashemian. (2013. ) Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants. WA .Rev. 0, Pacific Northwest National Laboratory, (PNNL-22847 ) ,

Invited Presentations

Bledar Alex Konomi (03-19-2021. ) Computer Model Emulation with High-dimensional Functional Output in Large-scale Observing System Uncertainty Experiments: An Application to NASA’s Orbiting Carbon Observatory-2 Mission .Department of Mathematical and Statistical Sciences, Marquette University, Marquette University , US.

Bledar Alex Konomi (09-23-2021. ) Bayesian Latent Variable Co-kriging Model for Different Quality Flagged Measurements in Remote Sensing .International Chinese Statistical Association 2021 Applied Statistics Symposium, Chicago, Illinois, US.

Bledar Alex Konomi (11-2020. ) On the Bayesian Analysis of Multifidelity Computer Models .Department of Mathematical Sciences, Durham University, UK, Virtual Presentation.

Bledar Alex Konomi (10-2020. ) Modeling AIRS temperature datasets .Remote Sensing UQ Virtual Breakout Meeting, organized by NASA JPL, Pasadena, Virtual Presentation.

Bledar Alex Konomi (07-2020. ) Sequential Design of High-Dimensional Multifidelity Computer Models .Joint Statistical Meetings, Virtual Conference.

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

-STAT-3038 Probability & Statistics II Level:Undergraduate

-STAT-6032/5132 Applied Statistics II Level:Both

-STAT-6045/5145 Statistics Computing Level:Both

-STAT-8022 Advance Bayesian Analysis Level:Graduate

-STAT-8045 Advance Statistical Modeling Level:Graduate

-STAT-6071/5171 Statistical Machine Learning Level:Both

-STAT-1034 ELEMENTARY STAT I Level:Undergraduate

-STAT-2037 PROB & STATS I Level:Undergraduate

-STAT-6021 MATH STATS I Level:Graduate

-STAT-6043 APPLIED BAYESIAN

-STAT-7020 TOPICS IN APP STAT