Please find my full publication list at NASA ADS, Google Scholar, or ORCID.

Research Interests

My research includes a broad range of topics in stellar astrophysics. They can be roughly grouped into three subjects:

  • Modeling the effects of radiation-matter coupling in super-Eddington systems;
  • Predicting the observational signatures using radiation transfer techniques;
  • Developing and applying deep learning models to analyze astronomical data.

Using analytical and numerical methods, I focus primarily on the study of how radiation impacts the lives and deaths of massive stars. I strive to understand the physical mechanisms governing the dynamics of radiation-dominated stellar systems and to provide insights on how to interpret their observations. My research projects also necessitate continuous improvements of the numerical methods in radiation transport and hydrodynamics, which I also devote a portion of my time on. In the following, you will find links to a few ongoing/side projects that I am working on.

Ongoing Work

Software Instruments

Here’s a list of software instruments I use in my research.

  • FLASH: multi-physics, magneto-hydrodynamics code
  • Sedona: Monte Carlo radiation transport code for modeling supernovae and other transients
  • Arepo: moving-mesh magneto-hydrodynamics code
  • STELLA: a 1D multi-group radiation hydrodynamics code
  • PyTorch: an open source, general purpose machine learning framework
  • Keras/Tensorflow: another deep learning library for fast experimentation