Yifeng Guo

Department of Computational Biology

St. Jude Children's Research Hospital

Memphis, TN, USA

I am a Postdoctoral Research Associate in the Department of Computational Biology at St. Jude Children’s Research Hospital, working with Dr. Xiang Chen on statistical and machine learning methods for biomedical and single-cell data.

I received my Ph.D. in Statistics from The University of Hong Kong (HKU) in 2024, advised by Prof. Guodong Li and Dr. Aijun Zhang. Prior to HKU, I obtained my B.S. in Mathematics from Sun Yat-sen University in 2019, advised by Prof. Xueqin Wang, and spent a semester as a visiting student at UC Berkeley.

My research lies at the intersection of high-dimensional statistics and artificial intelligence, with a focus on explainable AI, functional data analysis, and high-dimensional inference. I develop theoretically grounded methods for feature importance and complex data modeling, with applications in computational biology, medicine, and finance. My work has appeared in venues including the Journal of the American Statistical Association (JASA), IEEE Journal of Biomedical and Health Informatics, and ICLR.

Outside of academia, I co-founded Demetech Co., Ltd. (Hong Kong, 2024–present), where I serve as CTO, building AI-driven clinical decision support and statistical modeling tools, supported by the HKSTP Ideation Program and HKU-TEC Seed Fund.

Feel free to reach out by email — I am happy to discuss research, collaboration, or anything else.

news

May 2026	Our paper AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach has been accepted at IEEE JBHI. 🎉
Apr 2026	Presented Flow-Disentangled Feature Importance as a poster at ICLR 2026 in Rio de Janeiro, Brazil. 🇧🇷
Jan 2024	Successfully defended my Ph.D. thesis at The University of Hong Kong under the supervision of Prof. Guodong Li and Dr. Aijun Zhang. 🎓

selected publications

ICLR

Flow-Disentangled Feature Importance

Xingshu Chen^*, Yifeng Guo^*, and Jin-Hong Du

International Conference on Learning Representations (ICLR), 2026

*Equal contribution

Bib

@article{guo2026flow,
  title = {Flow-Disentangled Feature Importance},
  author = {Chen, Xingshu and Guo, Yifeng and Du, Jin-Hong},
  journal = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  note = {*Equal contribution}
}

JASA

High Dimensional Portfolio Selection with Cardinality Constraints

Jin-Hong Du^*, Yifeng Guo^*, and Xueqin Wang

Journal of the American Statistical Association, 2023

*Equal contribution

Bib

@article{du2023high,
  title = {High Dimensional Portfolio Selection with Cardinality Constraints},
  author = {Du, Jin-Hong and Guo, Yifeng and Wang, Xueqin},
  journal = {Journal of the American Statistical Association},
  volume = {118},
  number = {542},
  pages = {779--791},
  year = {2023},
  note = {*Equal contribution}
}

IEEE JBHI

AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach

Yipeng Zhuang^*, Yifeng Guo^*, Yuewen Li^*, and 8 more authors

IEEE Journal of Biomedical and Health Informatics, 2026

Accepted. *Equal contribution

DOI Bib HTML

@article{zhuang2026cancer,
  title = {{AI}-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach},
  author = {Zhuang, Yipeng and Guo, Yifeng and Li, Yuewen and Wu, Yuheng and Song, Tingting and Wang, Zhiyong and Liang, Yiteng and Zhou, Kunzhong and Wang, Weifang and Yu, Junxuan and Zhuang, Li},
  journal = {IEEE Journal of Biomedical and Health Informatics},
  year = {2026},
  doi = {10.1109/JBHI.2026.3694585},
  note = {Accepted. *Equal contribution}
}