Climate Data Analyst Phone: +1 828.350.2014 Email: firstname.lastname@example.org
Dr. Garrett Graham joined NCICS in October 2019 as a Climate Data Analyst (Research Associate). He is focused on updating and improving one of NCEI’s oceanographic temperature records, the Optimum Interpolation Sea Surface Temperature (OISST) data set, and is developing a machine learning-based anomaly detection system for the U.S. Climate Reference Network’s suite of soil moisture sensors. He maintains his interest in quantitative biology and is integrating that background with his work in climate science. Additionally, he is co-creating and co-teaching a machine learning course for researchers at NCICS and NOAA and volunteers as a scientific advisor for the Buncombe County Department of Health and Human Services pandemic response team.
Dr. Graham earned his BS in mathematics and physics from the University of Richmond, which he attended on an Ethyl and Albemarle Science Scholarship. He went on to complete his PhD in bioengineering in the lab of Dr. Jeff Hasty at the University of California San Diego. While at UCSD, he focused on engineering biosensors to study genome-wide prokaryotic transcriptional dynamics. In collaboration with a team of other researchers and funded by the Defense Advanced Research Projects Agency (DARPA), Dr. Graham helped create a field-deployable, bacteria-based heavy metal sensor whose core technology has been licensed by Quantitative Biosciences to detect up to five distinct metals in water supplies in real time.
Dr. Graham grew up in the Asheville area and is glad to be back and working on meaningful scientific projects. He enjoys mountain biking, whitewater kayaking, and the Blue Ridge Mountains.
Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1. Journal of Climate, 34, 2923-2939. http://dx.doi.org/10.1175/jcli-d-20-0166.1
Runkle, J. D., M. M. Sugg, G. Graham, B. Hodge, T. March, J. Mullendore, F. Tove, M. Salyers, S. Valeika, and E. Vaughan, 2021: Participatory COVID-19 surveillance tool in rural Appalachia:real-time disease monitoring and regional response. Public Health Reports, In press. http://dx.doi.org/10.1177/0033354921990372
Graham, G., N. Csicsery, E. Stasiowski, G. Thouvenin, W. H. Mather, M. Ferry, S. Cookson, and J. Hasty, 2020: Genome-scale transcriptional dynamics and environmental biosensing. Proceedings of the National Academy of Sciences of the United States of America, 117, 3301–3306, https://doi.org/10.1073/pnas.1913003117.
Bleak, C., H. Bowman, A. Gordon Lynch, Graham, G., J. Hughes, F. Matucci, and E. Sapir, 2013: Centralizers in the R. Thompson group V-sub-n. Groups, Geometry, and Dynamics, 7(4), 821-865.
Graham, G. C. and O. Lipan, 2011: The effect of coupled stochastic processes in a two-state biochemical switch. Journal of biological physics, 37(4), 441-462. https://doi.org/10.1007/s10867-011-9226-8
Cates, J., G. C. Graham, N. Omattage, E. Pavesich, I. Setliff, J. Shaw, and O. Lipan, 2011: Sensing the heat stress by Mammalian cells. BMC biophysics, 4(1), 16. https://doi.org/10.1186/2046-1682-4-16