Alexander Stone-Martinez
PhD Candidate

About Me

I am a sixth-year graduate student in astronomy at New Mexico State University, specializing in machine learning, asteroseismology, stellar evolution, and galactic archaeology.

My research focuses on leveraging machine learning techniques to analyze large astronomical datasets, such as spectroscopy from the Sloan Digital Sky Survey (MWM program), asteroseismology data from the Kepler and TESS missions, and astrometry from Gaia. I am particularly interested in determining stellar ages from chemical abundances, calibrated with asteroseismology, and applying this to SDSS and Gaia data to explore the history of our galaxy.

I am passionate about sharing knowledge both inside and outside the classroom. I enjoy astronomy and general science outreach and have been exploring the use of virtual reality (VR) and other tools to create new outreach possibilities. Feel free to reach out if you have questions or are interested in collaborating on VR outreach projects.

In my free time, I love learning new skills, such as operating amateur radio, building projects in my garage, playing music, 3D modeling, and picking up new languages. I have a particular passion for flying and hope to complete my private pilot's license this year.

Interests

  • Stellar Ages
  • Galactic Archeology
  • Asteroseismology
  • Machine Learning
  • Virtual Outreach

Education

PhD in Astronomy, 2025
New Mexico State University (expected)
BSc in Astronomy & Astrophysics, 2019
Embry Riddle Aeronautical University

Currently Working On

STArFLOW

Age Posterior
I am currently developing a machine learning-based method called STArFLOW, using normalizing flows to estimate stellar ages. This model takes stellar parameters and chemical abundances to produce posterior age distributions for individual stars. The approach is particularly effective for stars aged between 1 to 8 billion years (Gyr), and its key strength lies in providing a deeper understanding of the uncertainties associated with its predictions.

SDSS Stellar Ages Working Group

I lead the newly established MWM Stellar Ages Working Group as part of SDSS-V. Our primary objective is to compare and characterize various stellar age estimation methods across a wide parameter space. Ultimately, we aim to produce a comprehensive catalog, compiling stellar ages for all stars in the survey, from every method available

SDSS-V Pipeline:

As part of the SDSS-V team, I contribute to the development and maintenance of data pipelines for the latest iteration of the Sloan Digital Sky Survey. I monitor incoming data from observatories, identify issues such as low throughput or poor guiding, and ensure the integrity of the data. Additionally, I evaluate the reduced data products, addressing any problems with the reduction pipelines for the BOSS and APOGEE instruments