Hello! I am a 1st-year masters student in the Department of Atmospheric Sciences at the University of Illinois Urbana-Champaign, under the advisement of Dr. Larry Di Girolamo. I am researching the use of machine learning models to improve cloud masking techniques for satellite remote sensing.

Before coming to University of Illinois, I attended Rochester Institute of Technology (RIT) for my bachelors in computer science where I graduated magna cum laude in May of 2022. While at RIT, I specialized my classes for A.I. and machine learning to advance my education within this subsection of computer science. By tayloring my classwork to this subject area, I gained education and skills in computer vision, data science, search engines, and neural networks. Additionally while attending RIT, I acquired a minor is Geographical Information Systems, where I gained education is mapping softwares, geographical data visualization, and remote sensing.

Simultaneously while attending RIT, I was a research intern for NASA Langley Research Center (LaRC) from 2021-2022, where I worked on the Cloud Detection Neural Network project under the advisement of Dr. Snorre Stamnes. Under this internship, I researched the capabilities of using 2-D convolutional neural networks to detect clouds within NASA field campaign imagery. We used NASA imagery from mounted cameras on the NASA ACTIVATE and Camp²Ex flights and trained models to detect frames with clouds with above 95% accuracy. This prior research exposed me to the overlap of machine learning and atmospheric sciences, where I now am researching how segmentation convolutional neural networks can be used to improve cloud masking techniques for the NASA MODIS and MAIA instruments.