Software Engineer at NASA's Jet Propulsion Laboratory.

Internship position at the Machine Learning and Instrument Autonomy Group where I worked on studying the capabilites of different ML models to detect exoplanets orbiting nearby stars. The goal was to understand if ML could be used instead of traditional methods as that would allow for faster and more accurate detection of exoplanets without the need of more expensive and specialized equipment.

To the best of my knowledge, I was the first Chilean student to do an internship at NASA's Jet Propulsion Laboratory.

Some of the things I worked on include:

• Created an image simulator to generate datasets of millions of realistic images of stars. Worked with astronomers and physicists to assure the images created were realistic and equivalent to the ones obtained using laboratory instruments.
• Developed a pipeline to process the images and extract features using different techniques like Fourier Transform, HOG and Convolutional Neural Networks (CNN), among many others.
• Designed, implemented, trained and evaluated ML models to detect exoplanets in the images. The training of this models was executed in high performance computing (HPC) clusters, courtesy of TACC.
• Wrote a poster and paper for a conference in San Diego, California. It was called Optical distortion calibration using machine-learning for exoplanet detection.