Po-Yen Tung

Physics-informed AI for Challenges in Materials Science.

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I’m a Research Associate working in both Materials & Earth Sciences Departments at the University of Cambridge. I’m chuffed to bits to be affiliated with Peterhouse, the oldest college at the university - it’s quite a special place!

My interests are pretty wide-ranging, but I’m especially excited by anything that combines physical sciences, the hunt for new materials, and machine learning. Always up for a chat about these topics, so don’t hesitate to get in touch!


News

Jun 19, 2024 The most recent version of Few-shot-segmenter has just been launched!
Jun 17, 2024 The benchmark suite VLab-Bench has just been released! :sparkles:
Apr 24, 2024 A new preprint alert: Derivative-free tree optimization for complex systems!

Latest posts


Selected publications

  1. dots-workflow.png
    Derivative-free tree optimization for complex systems
    Ye Wei*, Bo Peng*, Ruiwen Xie*, Yangtao Chen*, Yu Qin*, Peng Wen*, Stefan Bauer*, and Po-Yen Tung*
    arXiv preprint arXiv:2404.04062, 2024
  2. sigma-demo.gif
    SIGMA: Spectral interpretation using gaussian mixtures and autoencoder
    Po-Yen Tung, Hassan Sheikh, Matthew Ball, Farhang Nabiei, and Richard Harrison
    Geochemistry, Geophysics, Geosystems, 2023
  3. Science
    alloydesign.jpg
    Machine learning–enabled high-entropy alloy discovery
    Ziyuan Rao, Po-Yen Tung, Ruiwen Xie, Ye Wei, Hongbin Zhang, Alberto Ferrari, TPC Klaver, Fritz Körmann, Prithiv Thoudden Sukumar, Alisson Silva, and  others
    Science, 2022