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, 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, and worked as a research assistant in computer vision at Xi'an Jiaotong University. I then transitioned to computational biology to apply my technical expertise to questions with direct implications for human health.

Please feel free to get in touch!

News

• 03/09/2026 Our work, De novo sequencing of chimeric spectra using Casanovo, has been selected for an oral presentation at ASMS 2026, San Diego.
• 02/11/2026 Invited to give a seminar talk at San Diego State University.
• 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 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, Baltimore.