• Researching optimizing machine learning models to improve the accuracy and computation efficiency of molecular dynamics simulations. • Investigated and ran tests with existing path sampling methods like stochastic optimal control and enhanced sampling.• Reported weekly on updates and findings to the primary Deep Learning researcher. • Used VMD and computational analysis to find a way to integrate path sampling computation as predictions rather than calling the expensive simulations numerous times in order to efficiently take a protein from one metastable state to another and improve drug delivery systems.

• Collaborated with four other interns to integrate machine learning models into a larger e-clinical application.• Analyzed the accuracy of the Random Tree Regression machine learning algorithm for time-based forecasting to improve glucose level predictions without the aid of a CGM (Continuous Glucose Monitor).