//hi guys!

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CS + Applied Statistics at Purdue. I'm interested in machine learning, software development, and overall making cool stuff :D

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~/chen 
const chen = {
  studying: ["CS", "Applied Stats"],
  hobbies: ["cooking", "table tennis"],
  sleep: false
};

I work on a range of problems, from high-performance machine learning pipelines and synthetic data generation to language model reliability and evaluation. In the long term, I am especially interested in pursuing research at the intersection of machine learning and statistics.

I'm a CS + Applied Statistics student at Purdue, focused on machine learning and intelligence in Computer Science, and probability and applied regression in Applied Statistics. Previously, I spent the spring at Rolls-Royce working on a synthetic data framework for turbofan telemetry that lets PHM researchers train models without ITAR-restricted engine data. I built a GPU-accelerated conditional diffusion pipeline with transformer-based masked denoisers and inpainting-style sampling, and I also experimented with LoRA fine-tuning and synthetic data evaluation using MMD and domain-classifier metrics. Lately, I have been exploring LLM reliability evaluation at AbbVie.

Before that, I led the algorithms side of Purdue Robomasters and competed in math and programming competitions, including the Putnam (Top 1000), AIME (2x qualifier), and USACO (Gold Division).

Technical Skills

  • Python
  • R
  • C
  • C++
  • Java
  • JavaScript
  • TypeScript
  • HTML/CSS
  • SQL
  • NumPy
  • Pandas
  • Matplotlib
  • Scikit-learn
  • PyTorch
  • TensorFlow
  • OpenCV
  • React
  • Node.js
  • Flask
  • MongoDB
  • Ellmer
  • Vitals
  • Git
  • Docker
  • AWS
  • Linux