Experience
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Projects
Things I've built and learned from
Enhancing Football Match Predictions through Machine Learning
Trained supervised models (Random Forest, MLP, Decision Tree) in Python using scikit-learn to predict Premier League match outcomes across 380 games in the 2021–22 season, reaching 61.5% accuracy versus a 33% baseline.
Engineered match-level features including team form, recent goal differential, and live table position, then cleaned, encoded, and merged them into a training set.
Stock Market Sentiment Analysis with NLP & Deep Learning
Fine-tuned a BERT-based sentiment classifier on 10,000 finance-related tweets to label them as positive, neutral, or negative toward market direction, reaching ~85% accuracy with an F1 score of 0.83 and recall of 0.86.
Built an NLP preprocessing pipeline (tokenization, lemmatization, stopword removal, padding, batching) and evaluated multiple checkpoints.
Technical Skills
Technologies I work with
Languages
Frameworks
ML/DS
Systems/Tools
Education
B.A. in Computer Science & B.A. in Applied Mathematics (Double Major)
University of California, Berkeley
Relevant Coursework: Data Structures, Discrete Mathematics and Probability Theory, Structure and Interpretation of Computer Programs, Linear Algebra, Multivariable Calculus
Activities: Berkeley Codeology, Mobile Developers of Berkeley, MPS Scholars, Berkeley SkyDeck
Interests/Hobbies
Outside of coding, I enjoy hanging out with friends, playing soccer, golf, poker, traveling, snowboarding, sidequests, food, and everything in between. I also enjoy video editing YouTube Channel.
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