Hi, I'm Javier Huang 👋

Designed and developed StackDAG, a full-stack AI-integrated web application built with Next.js (React), Firebase authentication, and a Supabase cloud PostgreSQL database with Supabase Edge Functions for REST request processing and API security. Enables users to view, create, share, fork, and upvote Directed Acyclic Graphs (DAGs) representing technology stacks. Integrated the OpenAI API to provide layer-by-layer setup and integration guidance for each DAG. All DAGs and AI-generated instructions are stored in the Supabase cloud PostgreSQL database for fast retrieval and storage.

Developed an end-to-end deep learning aerial object detection pipeline using YOLOv11 Nano for the DOTA v1.5 dataset, focusing on preprocessing and data transformation for oriented bounding boxes. Implemented image tiling (640×640 with padding for YOLO format) to preserve resolution and detail, translated and clamped OBB annotations to tile boundaries, converted polygons to YOLO-oriented format with normalized coordinates, and mapped class names to indices. Trained and evaluated the YOLOv11 model on the processed dataset, producing detection visualizations, loss curves, and confusion matrices. Developed and documented my complete workflow and experimentation process in a Google Colab-hosted Jupyter Notebook.

Developed an end-to-end deep learning pipeline for multi-class skin lesion classification using the HAM10000 dataset, using transfer learning with MobileNetV2 as the backbone. Addressed severe class imbalance through targeted data augmentation and stratified train/validation/test splits. Evaluated model performance with accuracy, loss curves, and confusion matrix analysis to identify common misclassifications. Developed and documented the end-to-end workflow, challenges, and results in a Google Colab-hosted Jupyter Notebook.

Developed an end-to-end machine learning pipeline for Wellington Zone 1 Power Consumption Predictions using a Kaggle dataset of environmental and time series factors. Performed feature engineering, model evaluation, and selected Ridge Regression, achieving an R² score of 0.9963 on the test set with 382 ms prediction time. Developed and documented my workflow and experimentation step-by-step in a Jupyter Notebook.