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Chaoran-Huang/README.md
Chaoran Huang

Chaoran Huang

What I do

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I build AI-assisted, event-driven systems for regulated industries. I'm drawn to zero-to-one problems — taking an ambiguous idea all the way to a production system that real users depend on — and I care about the boring parts that make that possible: clear service boundaries, reproducibility, and systems that stay predictable under pressure.

🚀 What I'm working on

  • Founding engineer on a greenfield utility-billing SaaS (React · GraphQL · Node.js · Kafka · PostgreSQL · AWS) — took it from zero to a production platform serving real customers, then moved to an AI-assisted plan-review system that embeds models into regulatory review while keeping behavior deterministic, traceable, and safe to roll out.
  • Thinking a lot about distributed systems, event-driven architecture, applied ML/LLMs, and applied cryptography.
  • Background: M.S. Computer Science, Brown University · B.S. Computer Science, UC Irvine.
  • Away from the keyboard: film photography and Brazilian Jiu-Jitsu.

✍️ Writing

Long-form engineering notes at learn.chaoran-huang.com — the kind of explanations I wish I'd had while learning.

🧪 Selected work

  • Activation Checkpointing & Tensor Swapping — automatic, graph-level memory optimization for DNN training built on torch.fx; cut peak activation memory ~70–85% by profiling the forward/backward graph and choosing what to recompute vs. swap. (Harvard CS265)
  • Athlete Chest X-ray Abnormality Detection — YOLOv5 vs. Faster R-CNN for localizing abnormalities in medical imaging, from DICOM preprocessing to evaluation. (Tongji University research, with paper)

🌱 Open source

🧠 Tech I reach for

Languages
Python TypeScript JavaScript Java C++

Backend & Systems
Node.js GraphQL Effect Kafka PostgreSQL Spring Boot

Frontend
React Next.js Tailwind

AI / ML
PyTorch TensorFlow spaCy

Cloud & DevOps
AWS Docker Kubernetes GitHub Actions

Pinned Loading

  1. Athlete-x-ray-abnormality-dectection Athlete-x-ray-abnormality-dectection Public

    Athlete: "YOLOv5 vs. Faster R-CNN for detecting abnormalities in athletes' chest X-rays (Tongji research)."

    Jupyter Notebook

  2. cs265-mlsys-2024 cs265-mlsys-2024 Public

    CS265: "Automatic activation checkpointing + tensor swapping for DNN training, built on torch.fx graph profiling."

    Jupyter Notebook

  3. Named_Entity_Recognition_Vehicle- Named_Entity_Recognition_Vehicle- Public

    Jupyter Notebook