Machine Learning | Statistics | Software Engineering | Prev Microsoft | Python • PyTorch • scikit-learn • NLP • Computer Vision
Achievements:
- Nationally Selected Awards: Gates Millennium Scholar, Ron Brown Scholar, Quest Bridge Scholar
- National Fellowships: Management Leadership for Tomorrow, Rewriting the Code, ColorStack, America Needs You
- Director's Award, Weill Cornell Medical College
- Selected Presenter, National Undergraduate Research Conference at Harvard University
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Energy Usage Forecasting and Demand Shaping 🔋
Developed an AI/ML solution for forecasting household energy use, leveraging a Long Short-Term Memory (LSTM) model and a Reinforcement Learning agent. -
Interactive Syndicate Bank Network Graph 🏦
Engineered a Python backend to process ~9.7k deal records and calculate network statistics for a dynamic D3.js frontend visualization. -
Time Series Forecasting of Median Order Volume 📈
Architected a reusable and scalable object-oriented forecasting framework in Python, achieving a 70% error reduction on a hold-out set. -
Sentiment Analysis for Product Strategy ☕️
Built a Natural Language Processing (NLP) model withnltkand a BERT transformer to analyze Yelp reviews and inform strategic decision-making. -
Plant Specimen Image Classification 🌿
Developed an image classification model using a convolutional neural network (CNN) in a group of 5, achieving a 94.68% success rate in filtering non-standard plant specimens from a digitized collection.
- Languages: Python, Java, C, React, JavaScript, SQL
- Libraries: PyTorch, scikit-learn, statsmodels, NumPy, seaborn, pandas, Matplotlib, BERT, nltk
- Tools: Git, GitHub, Docker, Azure, Azure Key Vault, Jira
- Concepts: Neural Networks (RNNs, CNNs), NLP, Reinforcement Learning, Time Series Forecasting
- Start Date: June 2026
- U.S. Citizen
- Based in New York, New York USA
- Willing to relocate for the right team