• Ph.D., Physics | The University of Texas at Dallas (May 2022)
• M.S., Physics | The University of Texas at Dallas (December 2019)
• B.S., Physics | The University of Texas at Dallas (May 2017)

Data Scientist @ Toyota Financial Services (June 2022 - Present)

• Uncovered and corrected missing step in production data pipeline which impacted over 70% of active accounts
• Redeveloped loan originations model which resulted in 50% improvement in model performance and saving 1 million dollars in potential losses

Data Science Consultant @ Shawhin Talebi Ventures LLC (December 2020 - Present)

• Conducted data collection, processing, and analysis for novel study evaluating the impact of over 300 biometrics variables on human performance in hyper-realistic, live-fire training scenarios
• Applied unsupervised deep learning approaches to longitudinal ICU data to discover novel sepsis sub-phenotypes

Publication

Developed objective strategy for discovering optimal EEG bands based on signal power spectra using Python. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.

Publication

Used Matlab to train over 100 machine learning models which estimated particulate matter concentrations based on a suite of over 300 biometric variables. We found biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body.

Be ordinary and do extraordinary