I develop analytical tools to understand critical industry problems and create strategies based on quantitative insights.
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Evaluating deep learning models to predict failures in high risk production environments.
Random forest classifier model predicts crash severities in NYC and full report of insights from analytics deep-dive.
Large-scale data analysis and applied machine learning to improve testing and implementation of autonomous driving features.
Co-author. Contributed as student data scientist in completion of academics at NYU. Accepted for publication in May 2022 to Human-Computer Interaction International 2022, and printed to Springer Digital Library.
Co-author. Contributed statistical data analysis on published biomedical research study surrounding the effects of a novel low-toxicity drug. Accepted for publication March 2022 in Oxford Academic.
In-depth research study examining AVs within the automotive/mobility industry.
Trend Analysis in Python using Plotly visualizations. Analyzing historical waste/recycling trends.
Linear Mixed Models in SAS Studio. Modeling outcomes of wide-format data and repeated measurements.
Supervised machine learning in Python to find the cleanest eateries in NYC using health inspection data.