projects.txt

Projects

Personal

–Engineered an inference pipeline using MediaPipe BlazePose to track 33 3D landmarks per frame and classify climbing positions by joint angle in metric world space
–Implemented a biomechanics-based center-of-mass estimator using anthropometric segment weights, EMA temporal smoothing, and torso-scale pixel projection
–Architected an async backend with FastAPI and Redis, dispatching video processing jobs to a ThreadPoolExecutor
–Optimized video throughput with a multithreaded reader–inference–writer pipeline overlapping disk I/O with model inference, re-encoding output to H.264 via ffmpeg
–Built an interactive 3D skeleton viewer with React Three Fiber synced to video playback; extended to a side-by-side compare mode with shared play/pause controls, independent per-clip trim and efficiency scores, and parallel job polling
–Designed a transport-agnostic core package keeping inference logic decoupled from API and worker contexts

PythonComputer VisionMediaPipeFastAPIRedisReact Three Fiber

–Implemented a PyTorch-based FSRCNN super-resolution model and trained it end-to-end on custom image datasets
–Built reusable data loading and preprocessing pipelines to support efficient training and evaluation
–Evaluated model performance using PSNR and achieved results consistent with published benchmarks
–Deployed the model via a Flask service and containerized the system with Docker for reproducibility

Computer VisionPyTorchFlaskDocker

–Investigated three unsupervised anomaly detection methods (Z-score, IQR, Rolling Z-score) on high-frequency SpaceX CRS-10 rocket telemetry, using 5 known flight events as ground truth
–Built a cluster-based evaluation framework to group detected anomalies and score each method on precision, recall, and F1, enabling direct comparison across techniques
–Discovered through grid search that the optimal Z-score threshold (0.7) was far below the conventional 2.0–2.5 range, revealing the signal's compressed variance as a key driver
–Concluded that locally-adaptive methods consistently outperform global statistics on non-stationary signals, a finding with direct implications for real-world sensor data pipelines

PythonTime-SeriesAnomaly Detection

AI Report Summarization System

–LLM-powered pipeline that automated summarization of transfer pricing reports, reducing processing time by ~98% (4 hours → 5 minutes). Built with Python, Azure, and GPT-4.

PythonLLMNLPAzure

Personnel Operations Dashboard

–Power BI dashboard built for the San Mateo County Bar Association to track personnel metrics, headcount, and operational KPIs for leadership decision-making.

Power BISQLPython