I turn vague problems into testable questions.

My work spans full-stack web development, machine learning, and open source. When working with AI, I make the scope and constraints explicit, distinguish facts from assumptions, and verify proposed solutions through tests, logs, and measurements. I share what I learn and refine my decisions through experiments and code review, weighing performance, complexity, and reliability.

💫 Open source · Fedify

An ActivityPub server framework in TypeScript. I contributed lint rules and regression tests, focusing on code-path analysis and behavior that mocks alone do not verify.

Selected merged contributions:

For actor validation, I checked that regression tests failed when the fix was reverted and removed tests that passed either way.

🚀 Machine learning projects

⚽ K League Pass End-Point Prediction
K League × University of Seoul Open AI Competition · Gold Award (2nd place)

Predicted the final pass destination from match-event sequences using a BiLSTM, spatial heatmaps, and dense supervision. The repository documents match-grouped validation, consistent decoding during training and inference, and reproducible fold artifacts.

🍴 Restaurant Menu Demand Forecasting
LG Aimers 7th · Team project · 46th on the private leaderboard

Forecasted seven days of menu sales from a 28-day history using an ensemble of tree models, an LSTM, and a seasonal baseline. Refactored the notebook into a CLI package, compared outputs on synthetic data, and documented the limits of the offline validation.

🎧 Web development · VIBR

A social music service built with a four-person team. I worked across the frontend and backend on authentication, search, playback, feed consistency, and event logging, then revisited performance and reliability through reproducible measurements.

🌱 Tools I work with

Web

ML