Yuhui Hong

Postdoctoral Scholar at Noble Lab (2025 - Present)
University of Washington
Advised by Professor William Stafford Noble

Ph.D. in Computer Science (2020-2025)
Indiana University Bloomington
Advised by Professor Haixu Tang

I am a postdoctoral fellow at the University of Washington, specializing in computational mass spectrometry and developing methods to analyze biological data from analytical instruments (e.g., LC-MS, GC-MS). Currently working in proteomics mass spectrometry, I focus on advancing peptide de novo sequencing methods to better understand biological systems and enable more comprehensive protein identification.

During my PhD studies in Computer Science at Indiana University Bloomington, I developed computational approaches to overcome limitations in small molecule identification. My work focused on moving beyond traditional database-dependent methods by predicting LC-MS/MS spectra and molecular properties from 3D conformations to establish reference libraries for unknown compounds, and by identifying chemical formulas directly from spectra. I also applied deep learning methods to optimize experimental condition settings, improving the efficiency and accuracy of mass spectrometry workflows.

I received my B.S. in Computer Science from Xidian University, China, in 2019 and worked as a research assistant in computer vision at Xi'an Jiaotong University from 2019 to 2020 under the guidance of Professor Yaochen Li. Drawn to the complexity and scientific impact of biological and chemical research, I transitioned to computational biology to apply my technical expertise to problems with direct implications for human health.

Please feel free to get in touch!

News 📰

- [09/19/2025] Awarded the UW Data Science Fellowship at eScience Institute, University of Washington.

- [07/07/2025] I will join Noble Lab at University of Washington as a Postdoctoral Scholar in August 2025.

- [05/09/2025] Recipient of the the Luddy Outstanding Research Award.

- [03/04/2025] Our work, A Task-Specific Transfer Learning Approach to Enhancing Small Molecule Retention Time Prediction with Limited Data, has been selected for an oral presentation at ASMS 2025.