Reverse engineered a Fortran-90 proton disorder algorithm for Ih ice into a general purpose Python package for generating proton disordered structures for any ice type. Created a force field for tetrahydrofuran molecule by independently reviewing relevant literature, and validated the force field to within 1% absolute error of experimental density and solvation free energy. Analyzed natural gas hydrate inhibition simulations using Numpy and Pandas python packages, and proposed a mechanism of inhibition by surface binding on ice structuresManaged five Ubuntu workstations, including updates, user management, and NFS shares to a central server. Configured an Ubuntu server with SLURM job manager and Spack package manager to run scientific computing workflows for other team members, ensuring no overlapping jobs over the past year.

Mentored 3-4 learning assistants each semester through two observations, including timely feedback and goal setting to ensure improved performance. Facilitated professional development for learning assistants, including weekly training on topics including teaching fundamentals and resume building. Collaborated with supervisor during weekly meetings to devise new topics for staff training. Mentored a total of 30 learning assistants over 8 semesters, with 7 former mentees going on to become mentors. Co-facilitated a presentation at the National Symposium on Student Retention to university staff from across the country in Oct 2023 on building community among peer leader

Facilitated 2-3 group study sessions outside of class to provide additional instruction in organic chemistry. Engaged students in active learning through white board exercises, group discussions, and games. Collaborated with course instructors to help facilitate classroom activities and target student need. Successfully boosted student performance by half a letter grade (GPA difference of 0.60 on a 4.0 scale), with an average of 31% of students voluntarily participating in sessions over all 10 semesters of employment

Received a $7200 fellowship grant from the Center of Biomedical Research Excellence at Boise State University to conduct research with MATERIALab under Dr. Oliviero Andreussi, Department of Chemistry and Biochemistry, Boise State University. Research was centered around creating computer models for tetrahydrofuran hydrates, a common model system used in wet labs instead of natural gas hydrates. Natural gas hydrates are a hot area of research because of large (3.1 · 10^3 to 7.6 · 10^6 trillion cubic meters) deposits of methane hydrates that could serve as a fuel source. In face, methane hydrates contain 164 times as much methane per volume as gaseous methane typically found in natural gas. Our goal was to create a computational efficient pipeline for screening potential ligands that could disrupt tetrahydrofuran hydrate formation. Over the 10 week timeframe, I was able to:- Create a forcefield (using OPLS/AA parameters) for tetrhydrofuran- Write an algorithm in Python to create a proton disordered hydrate structure- Simulate a bulk (no water interface) tetrahydrofuran hydrate in GROMACS- Simulate the tetrahydrofuran hydrate with water interface- Simulate tetrahydrofuran hydrate with an additive on the surfaceRefer to the attached poster for more details about the project.

Collaborates on drug development research related to breast cancer. Designed molecular dynamics simulations for ligand-protein interactions using GROMACS to evaluate strength of electrostatic interactions and drug viability. Implemented script for mass screening of ligands for protein binding strength in C, using AutoDock Vina. Performs bacterial transformations, along with protein expression and purification (via ACTA Start). Runs gels to identify protein purity from extraction.

Recieved a Participation Award from Idaho INBRE to partake in the Nevada INBRE Microbiome Research Bootcamp. Performed DNA extraction using a Zymo-Research Fecal DNA MiniPrep Kit on human stool samples. Used PCR to amplify 16s rRNA gene before sequencing. Performed sequence alignment and trimming with MOTHUR. Statistical analysis was performed in R, using Tidy format.

Accomplished long-term (120+ hours), self-directed dissection of the lower leg. Collaborated one-on-one with professional lab staff to devise and plan for dissection. Created professional write-up of dissection, including structures displayed and procedures followed.

Advanced research on security of portable or implanted medical devices. Collaborated with a team to develop a new project using EEG data for bio-authentication. Outlined project goals and methods, created EEG tests, trouble-shot EEG operation, and interpreted EEG data. Contributed to the creation and implemention machine learning algorithms (Concurrent Neural Network (CNN) and Random Forest Model) in Python using SciPy and Keras packages. Achieved ~60% accuracy with four electrode EEG readings and relatively small training data set.