Jose Miguel Bautista

As an astrophysicist, I have gained valuable experience processing and calibrating high-resolution optical images as evidenced by designing, coding, and testing a 5 step algorithm to remove interlopers and background noise from astrophysical images. During my dissertation work, I focused on quantifying and minimizing sources of systematic error in images, in order to generate a complete catalog of low signal galaxies, leading to 2 discoveries captured in 1 publication in the Astrophysical Journal Supplement Series.

My skills in data coding and analysis are top-notch, and I excel at developing and implementing automated routines. I am well-versed in statistical analysis, spatial correlations, and comparing data with theoretical models using data analysis tools, including matplotlib, numpy, and Excel.

✷ Major contribution in 4 peer reviewed publications, including the prestigious Astrophysical Journal Supplement Series 267, Astronomy & Astrophysics 667, and Applied Physics B 125 journals.
✷ Developed a method of contaminant removal in images via unsharp masking, background calibration using pseudo-modes and contributing to 2 major discoveries, including 1,503 large galaxies (835 previously undetected) and the global behavior of galaxies in a cluster
✷ Conferences: Meeting of the American Astrophysical Society (AAS) 237 & 240

I am graduating with a PhD in physics from Stony Brook University in August 2023. 

Before that, I got a double major in physics and math from Brandeis University in 2016.