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milad1378yz/README.md

Milad Yazdani

I am a PhD student in Electrical and Computer Engineering at the University of British Columbia. My research combines generative modeling and reinforcement learning with medical imaging, mathematical optimization, multi agent systems, and computational design.

Selected work

  • MOTFM (paper) develops optimal transport flow matching for fast medical image synthesis in 2D and 3D.
  • EvoCut (paper) combines language models with evolutionary search to generate acceleration cuts for mixed integer programs.
  • MASPRM (paper, project page) is a process reward model that guides search in multi agent systems.
  • RL-Kirigami (paper) combines conditional flow matching and reinforcement learning for inverse kirigami design and rapid fabrication.

Google Scholar · LinkedIn

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  1. MOTFM MOTFM Public

    Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality

    Python 78 13

  2. EvoCut EvoCut Public

    EvoCut: Automatic generation of acceleration cuts for integer programs via evolutionary search and LLMs.

    Python 11 1

  3. MASPRM MASPRM Public

    Multi-Agent System Process Reward Model (MASPRM): a lightweight process reward model guiding multi-agent systems at search time.

    Python 4 2

  4. RL-Kirigami RL-Kirigami Public

    Inverse design and RL fine-tuning for reconfigurable kirigami shape generation and fabrication export.

    Python