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 researcher at the University of Washington working in computational mass spectrometry, at the intersection of machine learning and molecular science. Molecules leave signals such as spectra, retention times, and fragmentation patterns, but reading those signals back into structure is messy, ambiguous, and short on labeled data. I build machine learning models to help uncover molecules that no reference library had ever seen before.

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.
• 11/29/2025 Koina is published in Nature Communications, with 3DMolMS available as one of its models.
• 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.

Research interests

• Forward: structure to measurement
  Predicting how molecular structure gives rise to measurable signal: 3D-aware representations that predict spectra and physicochemical properties such as fragmentation, retention time, and chiral separation, building in-silico libraries that extend database search to compounds never measured and illuminate the "dark matters" of chemical space.
  3DMolMS (Bioinformatics, 2023; Nature Communications, 2025)  ·  3DMolCSP (Analytical Chemistry, 2024)  ·  TSTL (bioRxiv, 2025)
• Inverse: measurement back to structure
  Recovering molecular and peptide structure directly from spectra without a reference library: machine learning methods for small-molecule identification and de novo peptide sequencing, extending identification beyond the reach of spectral databases.
  FIDDLE (Nature Communications, 2025)  ·  De novo peptide sequencing (in progress, Noble Lab)  ·  Machine learning in small-molecule mass spectrometry (Annual Review of Analytical Chemistry, 2025)
• Trustworthy inference: knowing when to believe the answer
  Ensuring that inferences are not just accurate but statistically and mechanistically accountable: controlling false discovery rates in molecule and peptide identification, and building interpretable models robust to confounders for reliable biomarker discovery and clinical decision-making.
  MicroKPNN-MT(Bioinformatics Advances, 2024) · MicroKPNN-CF(bioRxiv, 2025)  ·  False discovery rate control for de novo identification (in progress, Noble Lab)

Selected publications

A few representative papers. For the complete list, please see my Google Scholar profile.