Resume

Education

• Concentration: Data Science
• GPA: 4.0/4.0

• Minor: Statistics, Mathematics; Data Intensive Computing Certificate
• GPA: 3.9/4.0 (Summa Cum Laude)

Work Experience

Software and Data

• Led on-cloud, persistent, web-scraping project to generate 1TB data source via Terraform, Docker, Selenium, & AWS.
• Scaled 100x via robust telemetry, CICD, containerization, distributed parallel computing, and data schema & backup.
• Led stakeholder meetings to co-create data integrity needs and access patterns & endpoints, and explicate tradeoffs.
• Project page

• Implemented scalable data query pipeline for sporting-event data using Python and SQL, on AWS.
• Developed a custom dataset generation process to aid statisticians' prediction tasks.

• Developed convolutional neural network for deep learning system, resulting in 2x time-series prediction capability.
• Experimented and tuned prior bounds for Bayesian hyperparameter tuning.

Research

• Developed data preprocess, ETL, analysis, and prediction pipeline via Pandas, SciKit Learn, PyTorch, & MatPlotLib.
• 20% model improvement with representation learning, dimension reduction, and feature engineering and selection.
• Performed imbalanced-multiclass regression via stratification and focal loss, as well as hyperparameter search.
• Project page

• Structured 133M unformatted YouTube comments into trees; designed algorithms to analyze conversation patterns.
• Created cloud-based, simulated users to interact with and scrape recommendations via Terraform, Selenium & AWS.
• Implemented statistical tests, post-stratification, mixed effects regression, and NLP to answer research questions.
• Lead author in publication (ICWSM, 2024).
• Paper | Code 1 | Code 2 | Project page

• Designed matrix decomposition algorithm for model training & tuning; proved correctness, runtime, and error bounds.
• Achieved 2x speed-up in training and re-tuning of global benchmark models (BERT, GPT-2).
• Author in publication (ICML, 2022); Received "Outstanding Paper runner-up" Award.
• Paper

Teaching

• Courses: Intermediate Programming (Masters level, data-intensive).
• Developed and taught coding exercises for labs, developed code scripts for grading, hosted office hours.
• Code

• Courses: Introduction to Machine Learning; Algorithms and Complexity; Data Structures.
• Created content review videos, hosted office hours and review sessions, wrote grading scripts.

Personal Projects

• Created 25+ YouTube tutorials for coding concepts, emphasizing intuition and visualization.
• Authored explanatory Medium articles on algorithmic fairness, dynamic programming, and Strassen's algorithm.

• Scraped and sampled game clips from NBA.com via BeautifulSoup and Selenium.
• Automated video reel generation process with multiprocessing and ffmpeg.
• Code

• Filtered Yelp reviews to surface Asian restaurants with higher ratings from Asian reviewers than average.
• Containerized data scraping, extraction, and storage with Docker; Filtered reviewers using DeepFace.
• Code

Honors and Awards

Leadership Activities