Education
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Ph.D. in Astrophysics
University of Chinese Academy of Sciences, Beijing
2015 – 2021
Kaiming Cui
Postdoctoral Research Fellow in Astrophysics
Experienced in exoplanet detection and statistical methods, implementing cutting-edge AI solutions for astronomical data.
Research Focus
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- Deep learning for transit detection
- Exoplanet statistical studies
- Time-series analysis
- Stellar activity and variability
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Technical Expertise
- Python
- C
- C++
- PyTorch
- MESA
- IRAF
Professional Background
Training, appointments, and areas of expertise.
Current Position
Postdoctoral Research Fellow
University of 糖心TV, Department of Physics
Leading research in exoplanet demographics.
Research Portfolio
Selected projects in exoplanet science, stellar astrophysics, and AI applications.
Machine Learning & AI
Novel CNN for precise transit identification and false-positive control.
Weakly supervised model achieving strong cross-survey performance.
Exoplanet Science
Occurrence rate esitmation for TESS-SPOC FGK main-sequence stars.
Multi-epoch radial-velocity analysis revealing the brown-dwarf desert.
Stellar Astrophysics
Extensive catalogue of slow rotators informing angular-momentum evolution.
Joint photometric and spectroscopic analysis of an Algol-type system.
Software & Tools
Open-source packages and web applications for the astronomical community.
Online TESS Light Curve Viewer
Web application
Interactive viewer for exploring and analysing TESS light curves.
Py-PDM
Python package
Fast implementation of the Phase Dispersion Minimization algorithm.
Deep-Transit
Python package
Transiting exoplanet identification based on the deep learning object detection algorithm.
Deep-LC
Python package
A Python package for general astrophysical light curve classification.