Measurement of stellar atmospheric parameters and chemical abundances.
Application of artificial intelligence algorithms in astronomical big data analysis.
Search for metal-poor stars.
Investigation of the Milky Way's formation and evolution.
Examination of diffuse interstellar bands (DIBs) to detect interstellar molecules and assess the chemical environment.
Proficient in Python programming
Skilled in using SPECTRUM code for synthesizing template spectra
Experienced with various machine learning algorithms
Highly proficient in Bayesian methods
Familiar with fundamental organic chemistry concepts
Article 1: Very metal-poor stars I: a catalogue derived from LAMOST DR9
Journal: Monthly Notices of the Royal Astronomical Society
Co-authors: Xiaokun Hou, Gang Zhao, Haining Li
Publication date: June 2024
DOI: 10.1093/mnras/stae1567
Article 2: Very metal-poor stars II: chasing the formation history of the Galactic metal-poor disc (submitted)
Journal: Monthly Notices of the Royal Astronomical Society
Co-authors: Xiaokun Hou, Ruizhi Zhang, Haining Li, Dashuang Ye, Gang Zhao
Article 3: A Value-Added Catalogue of Stellar Atmospheric Parameters and Chemical Abundances Derived from LAMOST DR10 Low-Resolution Spectra (in preparation)
Co-authors: Xiaokun Hou, Gang Zhao
Attended the MWGaia Doctoral Network Introductory School at the University of Coimbra.
Presented a report at the 2024 China-Spain Bilateral Workshop on the Milky Way and Exoplanets.