Analyzed NBA player interactions (passes) to model team chemistry and playstyle across seasons.
Created dynamic networks representing players as nodes and passes as edges, measuring team performance, playmaking roles, and chemistry using centrality and assortativity metrics.
Explored network evolution across different NBA eras and its impact on team performance.
Nano Type Inference
Implemented Algorithm W for type inference in `Nano’, a miniature Haskell-based language
Designed type environment, unification, and substitution logic according to Hindley-Milner type systems
Collaborated in a 4-person team to integrate independent Nano modules (pattern matching, parser, REPL)
Predicting Game Length of 2022 League of Legends Esports Matches using Early-Game Data
Developed a regression model to predict the total length of 2022 League of Legends matches using early-game data (gold difference, comeback status).
Improved model performance from linear regression (R²: 0.185) to Random Forest Regressor (R²: 0.741) by incorporating additional features and hyperparameter tuning.
Conducted a fairness analysis comparing World Championship matches to other leagues, confirming model fairness with a permutation test (p-value: 0.4).
Competitiveness of 2022 League of Legends World Championship Matches
Analyzed 2022 League of Legends match data to assess if World Championship (WCS) games are more competitive than regular season games.
Investigated the correlation between gold difference at 15 minutes and match outcomes, finding that WCS games had similar gold differences to other competitive leagues.
Cleaned and processed data from multiple leagues and visualized key patterns in match dynamics using EDA techniques.
Performed hypothesis testing to compare gold differences in WCS matches vs. other leagues, concluding that WCS games are not statistically more competitive in terms of early-game gold difference.