Summary
Isidro Vargas is a postdoctoral researcher with eight years of experience applying deep learning and Bayesian inference to astrophysics, currently working on generative models for stellar observations and exoplanet detection under an MSCA COFUND at IAA-CSIC. He has led ML-driven projects across institutions including the University of Geneva and UNAM, blending statistical rigor with practical algorithm development for observational cosmology and PLATO mission science. Beyond research, he teaches applied AI topics and has authored and reviewed STEM textbooks, showing a talent for translating complex concepts for diverse audiences. A practitioner in Python who also writes short stories, he brings a creative perspective to model design and scientific communication.
8 years of coding experience
4 years of employment as a software developer
The National Polytechnic Institute of Mexico