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@heavyball-research

HeavyBall Research

ML System Lab @ NYU Shanghai

HeavyBall Research

HeavyBall Research is a newly established research group in Computer Science at NYU Shanghai, launching in Fall 2025, led by Yucheng Lu.

Our mission is to advance the frontier of AI and machine learning systems. We work on a range of projects such as:

  • Efficient training and inference algorithms for foundation models.
  • Hardware/software co-design for efficient modeling.
  • High-performance GPU kernels for modern AI workloads.
  • Large-scale AI application systems (e.g., Agent frameworks, RAG systems).

We collaborate closely with industry partners and other research labs to build cutting-edge AI systems, and are well-supported with abundant compute resources and research funding. We warmly welcome passionate researchers who are excited about the intersection of systems and AI to join us!

Group Updates

  • [April 2026] Thanks Google for supporting our research through the 2026 Google TPU Research Award!
  • [April 2026] We are delighted to welcome three new PhD students (Zishuo, Yufeng, and Runyu) to our group!

Current Ph.D. Students

Popular repositories Loading

  1. computing-resources-guide computing-resources-guide Public

    A comprehensive collection of guidelines to all the computing resources the Heavyball Lab @ NYU Shanghai can access.

    Python 2

  2. multi-token-residual-prediction multi-token-residual-prediction Public

    A lightweight draft module that learns to predict several future denoising steps from a single target-model forward pass, enabling speculative-style decoding that yields the same outputs with subst…

    Python 2

  3. DistillBytes DistillBytes Public

    Two-Stage Framework Distilling Token-Trained Models into Byte-Level Models

    Python 1

  4. sglang sglang Public

    Python

  5. .github .github Public

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Showing 5 of 5 repositories

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