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