• Applied methods from latent state space modeling for unsupervised analysis of natural behavior & large scale neural population recordings• Utilized open source neuropixel data from the Allen Institute to test predictions from a spiking neural network model• Collected preliminary data on a 2-photon Random Access Mesoscope across multiple brain areas during injection of a serotonin-2A agonist, DOI• Developed network & causality metrics for the analysis of neural data to assess directed functional connectivity & network properties

• Algorithm development, modeling&simulation, software development, and hardware integration• Performed trade studies for sensor & aperture systems • Radiometry, environmental physics, sensor characteristics, star density analyses• Strengthened knowledge base in numerical analysis, orbit determination & propagation, modeling & simulation, image processing techniques, & target detection algorithms• Assumed test planning & operation responsibilities for major IR&D project• Significant Matlab development of passive tracking algorithms • Conversion of algorithms into C-programming language • Acquired C, Makefile, command-line, VxWorks, & other software development skills• Supported several projects simultaneously, outside of Boeing-LEOS

Under the guidance of Dr. Andre-Nicholas Chene, I systematically studied star clusters within our galaxy which contain at least one evolved, massive star (Wolf-Rayet stars). Characterized by it's unusual emission spectrum, WR stars remained an enigma for several decades after their discovery in the 1920s. Scientists have now figured out that these massive, short-lived stars rapidly lose mass from stellar winds, which results in its unusual emission spectrum. Now the research of focus is on determining how these massive stars form. By studying the star clusters which host these WR stars (i.e. the birth place), we hope to glean information on how these stars evolve. To do this, I analyzed photometric infrared data from the Vista Variables in the Via Lactea (VVV) survey conducted in Parnal, Chile. Using IDL programming, I wrote various scripts to transform the downloaded data, combine with other photometric surveys, decontaminate the catalogue to separate star cluster members from field stars, and analyze the resulting star cluster catalogue. All of this was done to determine the fundamental parameters of the star cluster (age, distance, extinction, mass, size), which are related to the WR star contained within the cluster. This internship provided me with an opportunity to develop novel data analysis solutions using IDL, Perl, Python, and IRAF programming. With every unique data set, there is a unique way to handle it. I learned how to navigate computers and run scripts using command line, to work with large data sets in an organized and efficient manner, and to use new programming languages fast and skillfully.

As a professional ski patroller, my responsibilities ranged from first aid, hazard marking, avalanche control, communicating current conditions and hazards to the public such as avalanche and deep snow dangers. Further responsibilities included daily medical and avalanche training.

Under Dr. Kuzyk of Washington State University, I helped search for the mechanism for reversible photodegradation in DO11 dye-doped copolymer systems. Lab responsibilities included preparing thin-film samples using thermal pressing and spin coating techniques, setting up optics, conducting experiments, and analyzing data using Origin.

At NOAA’s Mauna Loa Observatory, I gained valuable experience running a LIDAR (and CLidar) and analyzing the data from backscattered light from particles in the atmosphere. I was exposed to the handling and analysis of large data sets, laser and photomultiplier tube operation and background science, an innovative new Camera Lidar system, and scientific instrument construction (polar nephelometer).