Research Results


Hunt AG, Faybishenko B, Powell TL (2020) A new phenomenological model to describe root-soil Interactions based on percolation theory. Ecological Modelling
DOI: 10.1016/j.ecolmodel.2020.109205 [pdf[highlight[highlight] 

Paine, D, Ghoshal, D and Ramakrishnan, L 2020 Experiences with a Flexible User Research Process to Build Data Change Tools. Journal of Open Research Software, 8: 18. DOI

Arora, B., D. Dwivedi, B. Faybishenko, R. Jana, and H. M. Wainwright. Understanding and Predicting Vadose Zone Processes, in Reviews in Mineralogy and Geochemistry: Reactive transport in Natural and Engineered Systems, 85 (1), DOI: 10.2138/rmg.2019.85.10

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, in: Silvestrov, S., et al. (eds), "Stochastic Processes and Applications,Springer, Springer Proceedings in Mathematics & Statistics, pp. 437-466, 2018. (Chapter 19, invited).

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. (2017). Detecting dynamic causal inference in nonlinear two-phase fracture flow, Advances in Water Resources 106, 111–120, DOI: 10.1016/j.advwatres.2017.02.011


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, 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. abstract to Fall 2018 AGU Annual Meeting.

Faybishenko, B.; Tokunaga, T. K.; Kim, Y.; Agarwal, D., Uncertainty Propagation in Predictions of Hydraulic Parameters Based on the Pedotransfer Functions, American Geophysical Union, Fall Meeting 2016, abstract #B13E-0668

Technical Report

Paine, D., Ghoshal, D., & Ramakrishnan, L. (2020). Investigating Scientific Data Change with User Research Methods. Lawrence Berkeley National Laboratory. Report #: LBNL-2001347. Retrieved from


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.