Hao Kang

1st-year CS PhD student · Carnegie Mellon

Co-Advised by Chenyan Xiong & Tianqi Chen

Research on agent-native systems for LLM training and hardware-aligned architectures for foundation models.

Portrait of Hao Kang
Deep down my heart, I love writing code that makes things happen!

Selected Publications

  1. Under Review

    Delta-Matching: Closing the Final Gap of Native 8-bit Training for LLMs

    Haozhan Tang, Hao Kang, Han Cai, Song Han, Chenyan Xiong

  2. NeurIPS 2026

    PithTrain: A Compact and Agent-Native MoE Training System

    Ruihang Lai*, Hao Kang*, Haozhan Tang, Akaash R. Parthasarathy, Zichun Yu, Junru Shao, Todd C. Mowry, Chenyan Xiong, Tianqi Chen

  3. NeurIPS 2026

    Benchmark Test-Time Scaling of General LLM Agents

    Xiaochuan Li, Ryan Ming, Pranav Setlur, Abhijay Paladugu, Andy Tang, Hao Kang, Shuai Shao, Rong Jin, Chenyan Xiong

  4. EMNLP 2025
  5. arXiv 2025
  6. NAACL 2025
  7. EMNLP 2023

    Token Prediction as Implicit Classification to Identify LLM-Generated Text

    Yutian Chen*, Hao Kang*, Yiyan Zhai, Liangze Li, Rita Singh, Bhiksha Raj

* Equal contribution

About

My CS journey all started with a Python tutorial on YouTube. It was immediately clear after the first few hours of watching that once I learned how to program, I could build anything. The pursuit of that kind of freedom has carried me through my undergrad years at CMU and into the work I do today. Never did I anticipate back then how the power of AI would amplify that momentum.

I believe that technology, when guided by an innovative mind, can make a positive impact on this world. To this end, I want to write the code that makes things happen :-)— Hao