I have been conducting experimental research, both applied and fundamental, for 10 years in wide range of scientific problems. This has given me a mastery level knowledge of problem formulation, problem solving, and detailed analysis skills.
My day-to-day coding/analysis is done in Python with packages such as: numpy, scipy, matplotlib, pandas, parallel python, scikit-image, scikit-learn, and h5py.
I also have extensive experience with scientific/engineering software packages including: MATLAB, ImageJ, Photoshop, the and the R Language.
My skill set also includes data analysis techniques for both sparse and large data sets. I have experience using spectral, correlation, statistical, and numerical analysis for problem solving with stochastic and multi-dimensional data. I regularly work with large time series data sets. Other interests involve data presentation and visualization, and machine learning for problems in engineering.
My current work involves the investigation of automotive fuel sprays at Argonne National Laboratory.Typical data sets on the order of hundreds of Gb to 1 Tb. Our worked has garnished interest not only from the US Department of Energy, but also the automotive industry, whom we work with regularly.