My interest in machine learning was sparked by a second year cognitive science course during which I learned about the problem of general intelligence. That summer I worked at NASA where I acquired a newfound interest in space. The next semester I found a post by Prof. Miriam Diamond on a machine learning course forum. This post advertised a position as a cross disciplinary intern in machine learning applied to astroparticle physics. I thought this was the perfect cross-roads of my interests in machine learning and space, and so I applied.
Project: Applying Machine Learning to Event Reconstruction for the SuperCDMS SNOLAB Experiment
We use statistics to determine whether a particle hit a detector once or multiple times. The collaboration with SNOLAB provides the experimental data needed to power our machine learning models. These models will allow us to classify data into useful categories for physicists to analyze.
“It was a great opportunity to learn about particle and astroparticle physics. I was able to gain in-depth knowledge from experts in a different field of study. “
Georges’ CDI Experience
"I'm interested in space and astronomy in general. Physics was a good gateway."
I am interested in applied machine learning. Specifically, I’m interested in networks that seek to reproduce functions of the brain. For example, networks that integrate plasticity and unsupervised learning. In addition to machine learning, I’m interested in embedded systems such as spacecraft control systems or operating systems.
The Cross Disciplinary Internship gave me a better understanding and appreciation for the intricate field of astroparticle physics. I really enjoyed meeting experts in a different field from my own and learning directly from the horse’s mouth.