About
I am currently a Ph.D. student at Xiamen University and Shanghai Innovation Institute, jointly advised by Prof. Bin Ren and Prof. Tong Zhu. I received my M.S. degree in Physical Chemistry from Xiamen University in 2023 and my B.S. degree in Applied Chemistry from Beijing University of Chemical Technology in 2020.
My research focuses on bridging molecular spectroscopy and artificial intelligence. Spectroscopic measurements provide rich information about molecular structures and dynamics, yet extracting this information remains a fundamental challenge. I develop machine learning methods that connect spectral observations with molecular representations, enabling computers to interpret, predict, and generate molecular structures directly from experimental signals.
My recent work includes spectrum–structure modeling, molecular generation from vibrational spectra, and equivariant deep learning for molecular property prediction. More broadly, I am interested in developing foundation models and geometric learning algorithms for molecular science, with the long-term goal of building AI systems capable of understanding molecular physics from scientific observations and accelerating scientific discovery.
My research interests span two directions: (1) modeling the relationship between spectra and molecular configuration — including molecular spectrum prediction and structural elucidation guided by spectrum; (2) practical applications of AI-assisted spectral analysis — including bacterial classification, chemical reaction intermediate identification, and protein sequence prediction.
CV / Experience
- B.S. in Applied Chemistry, Beijing University of Chemical Technology, 2020
- M.S. in Physical Chemistry, Xiamen University, 2023
- Ph.D. in Physical Chemistry, Xiamen University, 2027 (expected)
- Ph.D. in Artificial Intelligence, Shanghai Innovation Institute, 2027 (expected)
- AI Algorithm Researcher Intern @ Shanghai Artificial Intelligence Laboratory, Shanghai, Summer 2026
- AI Algorithm Researcher Intern @ Shanghai Academy of AI for Science, Shanghai, Summer 2025
- AI Algorithm Engineer Intern @ Fuxi AI Laboratory, NetEase, Hangzhou, Summer 2022
Recent News
Apr 2026 — Our paper “Vib2Conf: AI-driven discrimination of molecular conformations from vibrational spectra” is available on arXiv. link
Mar 2026 — Our work “Suiren-1.0 Technical Report: A Family of Molecular Foundation Models” is available on arXiv. link
Mar 2025 — Our paper “Vib2Mol: from vibrational spectra to molecular structures — a unified deep learning framework” is available on arXiv. link
Mar 2025 — Our paper “Equivariant Spherical Transformer for Efficient Molecular Modeling” is available on arXiv. link
Jan 2025 — Our work “Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments” was published in Science Advances. link
Apr 2024 — Our review “Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives” was accepted by Analytical Chemistry. link
Nov 2023 — Our paper “Rapidly determining the 3D structure of proteins by surface-enhanced Raman spectroscopy” was accepted by Science Advances. link
Dec 2023 — RamanCloud is now publicly available. Visit RamanCloud