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 working at the intersection of machine learning and molecular science. My research focuses on developing interpretable and trustworthy machine learning methods to model and interpret high-throughput biological data, with an emphasis on mass spectrometry-based proteomics and metabolomics. Across these efforts, I aim to build systems that reveal underlying molecular mechanisms and accelerate biological and chemical discovery.

Please feel free to get in touch!

Research interests

• 3D molecular representation and property prediction
  The forward problem, going from molecule to measurement: 3D-aware molecular representations that predict how structure gives rise to 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 regions of chemical space.
  3DMolMS (Bioinformatics, 2023; Nature Communications, 2025)  ·  3DMolCSP (Analytical Chemistry, 2024)  ·  TSTL (bioRxiv, 2025)
• Reference-free molecular identification from mass spectra
  The inverse problem of mass spectrometry, going from measurement back to molecule: machine learning methods that infer the structure of unknown peptides and small molecules directly from their spectra, discovering molecules beyond the reach of reference libraries.
  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 and interpretable machine learning for science
  Building biologically informed models whose reasoning is interpretable and robust to confounders, enabling reliable biomarker discovery and trustworthy clinical decisions, with current applications in microbiome-based disease prediction.
  MicroKPNN-MT(Bioinformatics Advances, 2024) · MicroKPNN-CF(bioRxiv, 2025)

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.