Justin Krometis joined ARC in August 2011. Prior to joining Virginia Tech, he spent seven years doing traffic simulations and natural disaster modeling for an emergency management company and a state Department of Transportation.
My research is in development of theoretical and computational frameworks to address a number of data analytics problems, such as how to incorporate and balance data and expert opinion into decision-making, and how to estimate model parameters, including high- or even infinite-dimensional quantities, from noisy data. Areas of interest include: Bayesian Inference, Parameter Estimation, Optimal Control, Uncertainty Quantification, Numerical Analysis, Scientific Computing.
From my previous work experience in the public and private sectors, I also have significant experience in emergency preparedness, evacuation and other emergency planning, and transportation modeling for both planning and evacuation applications.
Jeff Borggaard, Nathan Glatt-Holtz, and Justin Krometis. A Bayesian Approach to Estimating Background Flows from a Passive Scalar. arXiv preprint arXiv:1808.01084 (Submitted for publication), 2018.
Jeff Borggaard, Nathan Glatt-Holtz, and Justin Krometis. On Bayesian consistency for flows observed through a passive scalar. arXiv preprint arXiv:1809.06228 (Submitted for publication), 2018.
Jeff Borggaard, Nathan Glatt-Holtz, and Justin Krometis. GPU-accelerated particle methods for evaluation of sparse observations for inverse problems constrained by diffusion PDEs. Journal of Computational Physics, 2019, DOI: 10.1016/j.jcp.2019.04.034.
Justin Krometis. A Bayesian Approach to Estimating Background Flows from a Passive Scalar. PhD thesis, Virginia
Polytechnic Institute and State University, 2018. (link)
Sid Baccam, David Willauer, J. Krometis, Yongchang Ma, Atri Sen, Mike Boechler. Mass Prophylaxis Dispensing Concerns: Traffic and Public Access to PODs. Biosecurity and Bioterrorism, 9(2):139-51, June 2011.
Yongchang Ma, David Willauer, Justin Krometis, Atri Sen, Sid Baccam. “Site Considerations for Points of Dispensing After Biological Terrorist Attack.” Transportation Research Record: Journal of the Transportation Research Board 2234.1 (2011): 51-61.
Yongchang Ma, Justin Krometis, and Atri Sen. “Radiological emergency evacuation trip generation model developed from telephone survey.” Transportation Research Board 88th Annual Meeting. No. 09-2264. 2009.
Beate Schmittmann, Justin Krometis, and Royce Zia. Will jams get worse when slow cars move over? Europhysics Letters, Vol. 70 (3), pp. 299-305, 2005. arXiv:cond-mat/0503413
Ph.D., Mathematics, May 2018, Virginia Tech, Blacksburg, VA
Committee: Dr. Jeff Borggaard, Dr. Nathan Glatt-Holtz, Dr. Lizette Zietsman, Dr. Matthias Chung
Dissertation: A Bayesian Approach to Estimating Background Flows from a Passive Scalar
B.S., Physics, May 2004, Virginia Tech, Blacksburg, VA
B.S., Mathematics, May 2002, Virginia Tech, Blacksburg, VA
Computational Scientist, Advanced Research Computing, Virginia Tech, Aug 2011 – present
Senior Transportation Analyst and Division Projects Lead, IEM, Inc, Dec 2005 – Aug 2011
Part-time Curriculum Development Assistant, Virginia Tech Math Department, Jan 2011 – Aug 2011
Transportation Modeler, North Carolina Department of Transportation, Sept 2004 – Dec 2005
Engineer-in-Training, Virginia, 2009
Small Projects Task Lead of the Year, IEM, 2007
Outstanding Senior, Virginia Tech Physics Department, 2004
Outstanding Senior, Virginia Tech Mathematics Traditional Option, 2002