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Undergraduate Researcher

NASA and Aerospace Systems Design Laboratory

My work as an undergraduate researcher for the Aerospace Systems Design Laboratory and NASA provided me with exposure to machine learning, big data, and cloud modeling. Using MATLAB, I worked to improve the reliability of our machine learning algorithm by analyzing weather trends. I quickly learned how to understand and read three-dimensional big data and simultaneously use my knowledge of chemistry and physics to draw conclusions about weather trends. I then mapped these trends across a model of the globe in order to best present my findings. My findings were used to write a research paper published by the American Institute of Aeronautics and Astronautics (see link below). I summarized my work into a technical one-page summary (see document to the right).

  • Context & Problem: I worked with my research team to pioneer a global climate prediction model using three-dimensional big data collected from NASA satellites.

  • Audience: Climate scientists and analysts; Federal Aviation Administration (FAA); NASA

  • Success: These conclusions resulted in the reduction of the number of  input variables into the machine learning algorithm, making it more efficient.


Graphic created in collaboration with Manon Huguenin, Gabriel Achour, Domitille Commun, and Chima Okechukwu.

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