"Two things fill the mind with ever new and increasing admiration and awe, the more often and steadily we reflect upon them: the starry heavens above me and the moral law within me." - Immanuel Kant
My research group tackles the most challenging aspects of astrophysics in light of large data sets. My work draws heavily on a combination of theoretical modeling, statistical inferences, and machine learning. I use these tools to provide new innovative angles and shed light on the most fundamental questions of star formation, galactic evolution, the formation of black holes, and cosmology.
I primarily work on the Milky Way, capitalizing on a wide range of on-going large-scale surveys and most key future surveys in the next decade, including spectroscopy (SDSS-V, DESI, GALAH, APOGEE, LAMOST, JWST), astrometry (Gaia), photometry (DES, LSST, Euclid, WFIRST) and asteroseismology (TESS, PLATO). I am an "end-to-end" large survey-oriented scientist -- I develop novel machine learning methods to maximally harness information in the data, build theoretical models, and confront them with observation via statistical inference.