Lauren Padilla, Ph.D. is the VoLo Data Science in the Office of the Chief Scientist and Health Program. She analyzes large air pollution monitoring datasets to understand localized patterns of exposure and sources of emissions. She also develops scalable, automated tools that lower barriers to large data analysis for researchers and decision-makers. Lauren is committed to open and transparent data, serving as a technical adviser to EDF’s Air Quality Data Commons.
Lauren earned her B.E. at Dartmouth College and Ph.D. at Princeton University in the Mechanical and Aerospace Engineering Department, co-advised in the Atmospheric and Oceanic Sciences Program. She has over a decade of experience in modeling and evaluating environmental quality and climate change in collaboration with academic, industry, government, and non-profit organizations.
Budreski, K., M. Winchell, L. Padilla, J. Bang, R.A. Brain. 2016. A probabilistic approach for estimating the spatial extent of pesticide agricultural use sites and potential co-occurrence with listed species for use in ecological risk assessments. Integr. Environ. Assess. Manag. 12(2):315-27. doi: 10.1002/ieam.1677 Padilla, L.E., M.F. Winchell, S.H. Jackson. 2015. Evaluation of AGRO-2014 for predicting hydrophobic organic chemical concentrations in ponds. J. Environ. Qual. 44(5):1568-1578. doi:10.2134/jeq2015.03.0149 Winchell, M., L. Padilla, S. Jackson, G. Mitchell. 2014. A Modeling Approach for Predicting Pyrethroid Residues in Urban Water Bodies for Use in Environmental Risk Assessments. Describing the Behavior and Effects of Pesticides in Urban and Agricultural Settings, Chapter 5. ACS Symposium Series Vol. 1168:83-133. DOI:10.1021/bk-2014-1168.ch005 Padilla, L., M. Winchell, N. Peranginangin. 2013. Modeling flow and pesticide transport through surface water diversions in the California Central Valley. International SWAT Conference Proceedings. Texas Water Resources Institute Technical Report TR-471. pp 391-402. Padilla, L., G.K. Vallis, and C.W. Rowley. 2011. “Probabilistic estimates of transient climate sensitivity subject to uncertainty in forcing and natural variability.” Journal of Climate, 24, 5521-5537. Padilla, L. and C.W. Rowley. 2010. “An adaptive-covariance-rank algorithm for the unscented Kalman filter.” Proc. 49th IEEE Conference on Decision and Control, 1324-1329.