Publications

Student Publications 

  1. Aksenova, M., Sybrandt, J., Cui, B., Lucius, M., Ji, H., Wyatt, M., Safro, I., Zhu, J., & Shtutman, M. (2019). Inhibition of the DEAD Box RNA Helicase 3 prevents HIV-1 Tat- and cocaine-induced neurotoxicity by targeting microglai activation. In 2019 Meeting of the NIDA Genetic Consortium. Extended Abstract & Poster
  1. Chodora, E. (2018). “Developing a Nonparametric Functional Calibration Framework for Physics-based Models”, Technical Report LA-UR-18-29597, Los Alamos National Laboratory, Los Alamos, New Mexico. https://www.osti.gov/servlets/purl/1477612.
  2. Flynn, G.S., Chodora, E., Atamturktur, S., & Brown, D.A. (2019 - in review). A Bayesian Inference-Based Approach to Empirical Training of Strongly-Coupled Constituent Models. ASME Journal of Verification, Validation and Uncertainty Quantification.
  3. Green, P., Chodora, E., Zhu, Z., & Atamturktur, S. (2018). Towards the Validation of Dynamical Models in Regions where there is no Data. Preprints. https://doi.org/10.20944/preprints201809.0389.v1.
  4. Hu, X., Chodora, E., Prabhu, S., & Atamturktur, S. (2019). Extended constitutive relation error‐based approach: The role of mass in damage detection. Structural Control and Health Monitoring. https://doi.org/10.1002/stc.2318
  5. Hu, X., Chodora, E., Prabhu, S., Gupte, A., & Atamturktur, S. (2019). Model calibration of locally nonlinear dynamical systems: Extended constitutive relation error with multi-harmonic coefficients. Engineering Computations. https://doi.org/10.1108/EC-10-2017-0419.
  6. Locke, W., Sybrandt, J., Safro, I., & Atamturktur, S. (2018, November 12). Using Drive-by Health Monitoring to Detect Bridge Damage Considering Environmental and Operational Effects. https://doi.org/10.31224/osf.io/ntfdp
  7. Locke, W., Sybrandt, J., Safro, I., & Atamturktur, S. (Pending Approval). Using Drive-by Health Monitoring to Detect Bridge Damage Considering Environmental and Operational Effects.
  8. Posey, Brandon; Gropp, Christopher; Herzog, Alexander; and Apon, Amy, "Automated Cluster Provisioning And Workflow Management for Parallel Scientific Applications in the Cloud" (2017). Publications . 38. 
  9. Posey, B., Gropp, C., Wilson, B., McGeachie, B., Padhi, S., Herzog, A., & Apon, A. (2018). Addressing the Challenges of Executing a Massive Computational Cluster in the Cloud. doi:10.1109/CCGRID.2018.00040
  10. Sybrandt, J., Carrabba, A., Herzog, A., & Safro, I. (2018, December). Are abstracts enough for hypothesis generation? In 2018 IEEE International Conference on Big Data (Big Data) (pp. 1504-1513). IEEE
  11. Sybrandt, J., & Hick, J. (2015). Rapid replication of multi-petabyte file systems. Work in progress in the 2015 Parallel Data Storage Workshop. Poster in 2015 Super Computing.
  12. Sybrandt, J., Shtutman, M., & Safro, I. (2017, August). Moliere: Automatic biomedical hypothesis generation system. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1633-1642). ACM
  13. Sybrandt, J., Shtutman, M., & Safro, I. (2018, December). Large-scale validation of hypothesis generation systems via candidate ranking. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 1494-1503). IEEE.
  14. West B.M., Locke W.R., Andrews T.C., Scheinker A., Farrar C.R. (2019) Applying Concepts of Complexity to Structural Health Monitoring. In: Niezrecki C., Baqersad J. (eds) Structural Health Monitoring, Photogrammetry & DIC, Volume 6. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham
  15.  Underwood, R., Anderson, J., & Apon, A. (2018). Measuring Network Latency Variation Impacts to High Performance Computing Application Performance. Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering.