Projects

• Wrote a sparse attention kernel in CUDA with Longformer style masking (local window + global stride tokens); tiles with no active pairs are skipped entirely, bringing complexity from $O(n^2)$ down to $O(n \cdot w)$.
• Initial version stored a full score array per thread scaled with sequence length which spilled to global memory at longer sequences; rewrote with Flash Attention style online softmax, dropping per-thread memory to $O(1)$ and achieving $1.4$--$1.8\times$ speedup.
• Wrapped kernels as a PyTorch extension via pybind11 and benchmarked against PyTorch dense attention on a T4 GPU across sequence lengths $64$ to $512$.

Estimated cryptocurrency rolling volatility using a mean-reverting stochastic differential equation with online Bayesian filtering. Results were benchmarked against GARCH(1,1) and GARCH(2,2) models under a strict out-of-sample evaluation setup.

Developed strategy bots for Yut, a traditional Korean board game competition. Tested minimax tree search and heuristic-based strategies. Final approach used heuristic evaluation with Bayesian optimization (Gaussian Process + UCB) for weight tuning. Consistently outperforms baseline strategy with 54-56% win rate.

• Developed an automated file organization system using AI-driven content-based classification, improving file management efficiency by 40%.
• Calculated document similarity using two approaches: TF-IDF vectorization with cosine similarity, and semantic embeddings from spaCy’s en_core_web_md model.
• Integrated real-time file monitoring with Watchdog to automatically organize files into user-defined folders, reducing manual sorting time by 60%.

• Implemented Bayesian inference methods (MLE, MAP, posterior mean) for robust parameter estimation in probabilistic models.
• Optimized inventory with the Newsvendor Problem, reducing losses by 49.08% vs heuristic methods.
• Developed and deployed multiple classification models, including KNN, Logistic Regression, and Single feature models, to predict healthcare readmissions, achieving AUCs of 0.68, 0.80, and 0.78, respectively.
• Achieved up to 7.8% cost savings through predictive model optimization, improving decision-making in healthcare resource allocation.

Built a BERT-based sarcasm classifier with Hugging Face and TweetEval for real-time inference, demonstrating advanced natural language processing capabilities.