Di Wu (Woody)
I build data-driven systems that connect machine learning, numerical methods, automated pipelines, and interactive analytics.
Projects
👗 [Multimodal Retrieval / ML Systems] Fashion Search Demo
An end-to-end cross-modal retrieval demo that turns natural-language fashion queries into relevant product image results.
- Description: Built a production-style retrieval system that combines a fine-tuned CLIP-based model in PyTorch, a Python backend, FastAPI JSON endpoints, and a Next.js frontend. The system uses an ANN index for efficient retrieval over the image corpus and includes interactive model analysis so users can inspect how representation quality affects search behavior.
- Live Demo: fashionsearch.woodygoodenough.com
- Project Page: Details
📐 [Numerical Linear Algebra / Interactive Visualization] Numerical Linear Algebra Explorer
An interactive project for building intuition around the numerical foundations behind machine learning and scientific computing.
- Description: Designed a visualization-focused learning environment for core numerical linear algebra ideas such as sensitivity, conditioning, geometric behavior in 3D, and algorithm progression over time. The project emphasizes intuition-building through interactive numerical analytics and visual demonstrations that connect low-dimensional examples to high-dimensional ML practice.
- Live Demo: nla.woodygoodenough.com
- Project Page: Details
🔍 [Deep Learning / Multimodality] OpenCLIP Fine-Tuning for FashionGen Retrieval
Fine-tuned OpenCLIP variants for large-scale image-text retrieval and comparative multimodal learning analysis.
- Description: Fine-tuned multiple OpenCLIP variants (ViT-B/32, ViT-B/16, SigLIP2) on the FashionGen dataset (image–caption pairs), comparing InfoNCE vs. BCE objectives and studying architectural trade-offs, training dynamics, and caption augmentation for improved multimodal retrieval performance.
- Project Page: Details
- Source Code: GitHub
📊 [Dashboarding / Data Visualization] Finance Data Analytics — End-to-End Automation
- Description: Automated financial data analytics system covering API data ingestion, ETL pipeline design, feature engineering, aggregation, and analytics-ready data publishing. Built a fully reproducible workflow with scheduled execution, versioned outputs, and downstream visualization support, enabling rapid experimentation and multi-dashboard consumption.
- Demo (Streamlit): Live Demo (AWS EC2 + Cloudflare; offline 2am–7am EST for cost control)
- Project Page: Details
- Source Code:
Contact
- Email: woodygoodenough [at] gmail.com
- GitHub: Woodygoodenough
- LinkedIn: Di Wu