Los Alamos National Laboratory

Los Alamos National Laboratory

Delivering science and technology to protect our nation and promote world stability
Find Expertise logo

Profile Pages

View homepages for scientists and researchers. Explore potential collaborations and project opportunities. Search the extensive range of capabilities by keyword to quickly find who and what you are looking for.

David Allen Osthus

David Osthus

Email
Phone (505) 665-7865

Expertise

  • Statistics
  • State-space models
  • Multiscale models
  • Bayesian methods
  • Forecasting

Education

Ph.D. Statistics, Iowa State University, 2015

M.S. Statistics, Iowa State University, 2011

B.A. Mathematics/Statistics and Religion, Luther College, 2008

 

LANL Positions

Scientist (2016 - present)

Postdoctoral Researcher (2015 - 2016)

Graduate Student Summer Intern (2013, 2014, 2015)

 

Publications

Select Publications (last updated: July 31, 2019)

  1. D. Osthus, S. A. Vander Wiel, N. M. Hoffman, & F. J. Wysocki (2019). Prediction Uncertainties beyond the Range of Experience: A Case Study in Inertial Confinement Fusion Implosion Experiments. SIAM/ASA Journal on Uncertainty Quantification7(2), 604-633.
  2. N. Reich, L. Brooks, S. Fox, S. Kandula, C. McGowan, E. Moore, D. Osthus, E. Ray, A. Tushar, T. Yamana, M. Biggerstaff, M. A. Johansson, R. Rosenfeld, & J. Shaman (2019). A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States. Proceedings of the National Academy of Sciences116(8), 3146-3154.
  3. D. Osthus, A. R. Daughton, R. Priedhorsky (2019). Even a good flu forecasting model can benefit from internet-based nowcasts, but those benefits are limited. PLOS Computational  Biology, 15 (2): e1006599. 

  4. D. Osthus, J. Gattiker, R. Priedhorsky, S. Del Valle. (2019) Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion). Bayesian Analysis. 14 (1): 261-312.

  5. J. Hyman, A. Hagberg, D. Osthus, S. Srinivasan, H. Viswanathan, G. Srinivasan (2018). Identifying Backbones in Three-Dimensional Discrete Fracture Networks: A Bipartite Graph-Based Approach. SIAM Multiscale Modeling and Simulation 16 (4): 1948–1968.

  6. G. Srinivasan, J. Hyman, D. Osthus, B. Moore, D. O'Malley, S. Karra, E. Rougier, A. Hagberg, A. Hunter, H. Viswanathan (2018). Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning. Nature Scientific Reports 8 (1): 11665.

  7. D. Osthus, H. Godinez, E. Rougier, G. Srinivasan. (2018) Calibrating the Stress-Time Curve of a Combined Finite-Discrete Element Method to a Split Hopkinson Pressure Bar Experiment. International Journal of Rock Mechanics and Mining Sciences 106: 278-288.

  8. D. Osthus, K. Hickmann, P. Caragea, D. Higdon, S. Del Valle. (2017) Forecasting seasonal influenza with a state-space SIR model. Annals of Applied Statistics 11 (1): 202-224.
  9. R. Priedhorsky, D. Osthus, A. Daughton, K. Moran, N. Generous, G. Fairchild, A. Deshpande, S. Del Valle. (2017) Measuring global disease with Wikipedia: Success, failure, and a research agenda. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing.

  10. K. Moran, G. Fairchild, N. Generous, K. Hickmann, D. Osthus, R. Priedhorsky, J. Hyman, S. Del Valle. (2016) Epidemic forecasting is messier than weather forecasting: the role of human behavior and Internet data streams in epidemic forecasting. The Journal of Infectious Diseases 214 (suppl 4): S404-S408.

  11. D. Osthus, P. C. Caragea, D. Higdon, S. K. Morley, G. D. Reeves, B. P. Weaver. (2014) Towards better empirical forecasting of radiation belt electrons and limitations on physical interpretation of predictive models. Space Weather 12 (6): 426-446.

  12. G. J. Welk, Y. Kim, B. Stanfill, D. Osthus, A. M. Calabro, S. Nusser, A. Carriquiry, (2014) Validity of 24 hour physical activity recall: Physical activity measurement survey. Medicine and Science in Sports and Exercise 46 (10): 2014-2024.

 

 

 

Documents and Files

-->