Yuanfang Xiang / 項 遠方

(Also spelled as Yüan-fang Hsiang)

  • .edu mail: yf.xiang@smail.nju.edu.cn

  • Undergrad student @ School of Artificial Intelligence, Nanjing University
    163 Xianlin Avenue, Nanjing 210023, P.R. China

  • Interests
    AI 4 Science, Neuro-Symbolic AI, Computational & Systems Biology
    I aim to perhaps just I’m daydreaming to 🤣 develop computational methods that push further our understanding of the complex world, by building human-comprehensible AI systems solving scientific problems. I’m currently trying to realize this goal via Machine Learning + Knowledge Reasoning and AI for Life Sciences.

  • More


Activities

  • Jan. 2026, 🎉🎉ACCEPTED BY ICLR ‘26 !
    This is my first publication as the first author.
    Best gratitude to all those supported me and my attempts. Particularly thank my Love, and all contributors to the project.

  • Dec. 2025 - , currently working at Yangtze AI Lab and Changzhou Synthetic Biology Institute, BGI Genomics for protein thermal-stability modelling facing biomanufacturing scenario.

  • Mar. 2025 - , currently collaborating with Prof. Julio Saez-Rodriguez’s group (the SaezLab, EMBL-EBI) on integrating mechanistic knowledge and data-driven learning for predictive tasks in systems biology.

  • Apr. 2024 - , currently working at CAS Academician, Prof. Zhi-Hua Zhou’s group (LAMDA Lab, Nanjing University) on applications of Abductive Learning, a neuro-symbolic method.

  • Aug. 2025, Collegiate Computational Design Competition of China
    • 2nd. Prize
       
  • Oct. 2024, the iGEM (international Genetic Engineered Machine) Competition
    • Gold Medal & Nominated Track Best
    • As team leader (dry lab section)
    • Our project: Shivacosmic-Greens on engineering electro-producing microbiome;
    • My work: Developed a knowledge ontology-augmented ML model, which assisted plasmid engineering. iGEM wiki
       
  • Jul. 2024, the AI+ Innovation Challenge, Nanjing University
    • 1st. Prize & Ranked top 1
    • As team leader
    • EGOAL: gene Expression prediction via Gene Ontology and Abductive Learning
    • Project Docs (CN)