Research Results


Arora, B., D. Dwivedi, B. Faybishenko, R. Jana, and H. M. Wainwright. Understanding and Predicting Vadose Zone Processes, SubmittedReviews in Mineralogy85.  

Paine D., Ramakrishnan L. (2019) Surfacing Data Change in Scientific Work. In: Taylor N., Christian-Lamb C., Martin M., Nardi B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science, vol 11420. DOI: 10.1007/978-3-030-15742-5_2 [pdf]

D. Ghoshal, L. Ramakrishnan and D. Agarwal. “Dac-Man: Data Change Management for Scientific Datasets on HPC Systems." In the International Conference for High Performance Computing, Networking, Storage and Analysis (SC’18). 2018. [pdf] [SC18-Talk]

B.Faybishenko, F. Molz, D.Agarwal, Nonlinear Dynamics Simulations of Ecological Processes: Model, Diagnostic Parameters of Deterministic Chaos, and Sensitivity Analysis. Chapter (invited) in book "Stochastic Processes and Algebraic Structures–From Theory Towards Applications" (Springer, in Press), 2018.  

Molz F, Faybishenko B, Agarwal D (2018) A broad exploration of nonlinear dynamics in microbial systems motivated by chemostat experiments producing deterministic chaos. LBNL-2001172, Berkeley, CA.

William Fox, Devarshi Ghoshal, Abel Souza, Gonzalo P. Rodrigo, Lavanya Ramakrishnan, E-HPC: A Library for Elastic Resource Management in HPC Environments, Workflows in Support of Large-Scale Science (WORKS), workshop held at Supercomputing. 2017. [pdf]

Faybishenko, B., Detecting dynamic causal inference in nonlinear two-phase fracture flow, Advances in Water Resources 106 (2017), 111–120.


Arora, B., B. Faybishenko, and D. Agarwal (2018), Using Sensitivity Analysis as a Tool to Determine the Need for Regeneration of Hydrological and Biogeochemical Predictions, Submitted Abstract, AGU Fall Meeting, Washington D. C., 10-14 December.

Powell,T., B. Faybishenko, D.Agarwal and L.M. Kueppers, Amendments in local meteorological data alter tropical forest biomass predictions of a terrestrial ecosystem model. Submitted abstract to Fall 2018 AGU Annual Meeting.


D. Ghoshal and others, Deduce: Managing Data Change Pipelines, Poster. Sep 2017. [pdf]

J. Mueller and others, Deduce: Understanding the Types and Impact of Data Change, Poster. Sep 2017. [pdf]

Faybishenko, B., T.Tokunaga, Y.Kim, D.Agarwal, Uncertainty Propagation in Predictions of Hydraulic Parameters Based on Experimental data and Pedotransfer Functions. Fall 2016 AGU, Poster presentation B13E0668.