Research
You can find my full publication list at NASA ADS, Google Scholar, or ORCID.
Research Interests
My research involves a broad range of topics in computational astrophysics. They can roughly be grouped into:
- Enhance the efficiency and accuracy of radiation/light transport and fluid dynamics simulations on heterogeneous computing platforms;
- Develop and deploy deep learning techniques to streamline our radiation/light transport and data analysis workflows;
- Study the effects of strong radiation-matter coupling in stellar systems.
In the following, you will find links to a few ongoing/side projects that I am working on.
Ongoing Work
- Exploring the applications of neural radiance fields in science visualization
- Supernova progenitor parameter estimation with invertible neural networks
- Simulating pre-supernova outbursts from red supergiant stars
Software Instruments
Here’s a list of software instruments I use in my research.
- FLASH: multi-physics, magneto-hydrodynamics code
- FLASH-MCRHD: a Monte Carlo radiation hydrodynamics module I wrote from scratch to support my work
- Sedona: Monte Carlo radiation transport code for modeling supernovae and other transient phenomena
- 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