Spring 2020:

summary:

Astrostatistical and computational techniques for processing time-series data from large astronomical surveys.

Introduction in the usage of astronomical catalos such as SDSS and Gaia to retrieve time-series data.

Probability theory, comparison of frequentist and Bayesian inference. Strategies for data exploration and visualization.

Approaches for parameter estimation, model selection (e.g. Markov chain Monte Carlo) and machine learning using Python packages such as Scikit-Learn.

In addition, I'm regularly supervising undergraduate and graduate students (astronomy, physics, computer science, data science, mathematics) for REU, Vanderbilt Immersion, as well as other internships and thesis projects.

If you are interested, please let me know.