I’m a machine learning enthusiast skilled in Python, R and SQL. I’ve worked with libraries including TensorFlow, Keras, Scikit-Learn, XGBoost, NLTK and Gensim.
I spent the past 8 months working at Audible as a Data Science Research Assistant on projects including predicting content quality metrics and studying the interaction between audiobook content and genre. Additionally, I worked with the Quantitative Investment Strategies team at Goldman Sachs as part of my data science capstone project, where I assessed pharmaceutical R&D similarity through FDA clinical trial metadata.
As a student at Columbia, I’ve taken courses such as Applied Machine Learning (with Andreas Müller, a Scikit-Learn core contributor), Applied Deep Learning, Algorithms and Exploratory Data Analysis (using R & D3.js).
I enjoy the experimental process of model development, evaluation, optimization and deployment, and have experience writing production-level code. My latest resume can be found here.
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