I’m an MS Data Science student at Tufts University with a BS in Applied Mathematics and Computer Science. Through coursework and internships, I’ve worked on projects spanning machine learning, probabilistic modeling, reinforcement learning, and full-stack web development.
I use this GitHub to share a small number of polished, representative projects.
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Probabilistic Chess Outcome Prediction
Multiclass logistic regression modeling implemented in Python, comparing MAP estimation via gradient descent and posterior predictive estimation via MCMC (NumPy, SciPy, PyMC). -
Human Feedback in Reinforcement Learning (TAMER)
Reinforcement learning experiments implemented in Python, evaluating event-based and navigation-guided feedback strategies in Cliff Walking and Taxi environments (Gymnasium, NumPy, matplotlib).
- Sentiment Analysis of Product Reviews — Machine learning classification model implemented in Python to predict review sentiment using BERT embeddings and multilayer neural networks (scikit-learn, pandas, NumPy).
- Movie Recommendation System — Collaborative filtering model implemented in Python to predict user ratings, trained with stochastic gradient descent and evaluated using MAE and RMSE (Surprise, scikit-learn).
- Design&Dine Web App — Full-stack web application for user-tailored recipe planning using third-party APIs, server-side logic, and a relational database, with features for saving favorite recipes, managing a shopping list, and ensuring user-friendly design (PHP, MySQL, JavaScript, HTML, CSS).
Some projects are not public due to academic integrity policies or because they are still being curated for release; I’m happy to discuss additional work upon request.