Computational Sciences: CONNected objECT (CONNECT) Algorithm
       
     
Big Data in the Earth Sciences
       
     
Water Resource Engineering
       
     
Big Data in the Earth Sciences
       
     
Big Data in the Earth Sciences

Technological advances in hardware and software have allowed data driven approaches to emerge as powerful tools that can be used in the era of Big Data and “deep analysis.” I work to develop collaborations that assist in the implementation of these technologies for massive data transfers, storage, and specialized analysis approaches in the Earth Sciences.

S. Sellars et al., "The Evolution of Bits and Bottlenecks in a Scientific Workflow Trying to Keep Up with Technology: Accelerating 4D Image Segmentation Applied to NASA Data," 2019 15th International Conference on eScience (eScience), San Diego, CA, USA, 2019, pp. 77-85, doi: 10.1109/eScience.2019.00016.

Sellars, S., 2018: “Grand Challenges” in Big Data and the Earth Sciences. Bull. Amer. Meteor. Soc. doi:10.1175/BAMS-D-17-0304.1, in press.

* The image above is displaying hyper-dimensional earth science data from model reanalysis and satellite based sources for June 1, 2012 over the continental United States. Each image represents a specific atmospheric variable.

Water Resource Engineering
       
     
Water Resource Engineering

Optimizing water resources is incredibility challenging with multi-institutional objectives and goals. Many innovative optimization ideas are being explored. One such idea that I have had the privilege to work on is the Forecast Informed Reservoir Operations (FIRO) project focused on Lake Mendocino located in the Russian River watershed of Northern California. In addtion, my research led to the contrbiution of new tools and techniques to assist water resource managers in making operational decisions.

Forecast Informed Reservoir Operations Steering Committee. (2017). Preliminary viability assessment of Lake Mendocino. Available from: http://escholarship.org/uc/item/66m803p2