Flight Path Optimization Specialist | Python | PostgreSQL | AWS | Jupyter NotebooksI work on optimizing flight paths to reduce costs and carbon emissions through the use of shortest path algorithms. My responsibilities include collaborating with leadership to align technical development with business goals, leveraging technologies such as Python, PostgreSQL, and AWS. I also focus on improving and maintaining the existing code base, performing troubleshooting, and developing new features within Jupyter notebooks. The work experience spans cloud computing, database management, and algorithmic optimization.

- Directed data acquisition, collection and validation strategy at NEquest informed by the latest research.- Extracted biomarkers from voice data that can indicate neurological disorders and produceddescriptive statistics and visualizations to aid in diagnosis-Currently working on mapping acoustic features from voice data to pinpoint the source of damage regions in the brain.

Dark Energy Spectroscopic Instruments(DESI) focal plane has 5000 optical fibers designed to observe various galaxies in the Universe. Some of them fail. Fibers which are outliers in failure rates must be removed to increase accuracy of galaxy clustering measurements in the Universe- Conducted exploratory and statistical data analysis on large datasets containing millions of objects.- Utilized Monte Carlo simulations to identify and mask outlier fibers in DESI's 5000-fiber focal plane, in a statistically rigorous method- Developed machine-learning classifiers, including Random Forest and Gradient Boost, to predict the success/failure probability of galaxy observations, trained on astrophysical features.- Created Jupyter notebooks to conduct various tests and generate plots for identifying and removing systematic errors from DESI catalog data.- Regularly presented data visualizations at weekly team meetings and wider collaboration conferences. My work has contributed to 5 publications in the DESI collaboration.