Nineteen-year-old Amber Yang has her head in the clouds. Actually, it’s beyond the clouds and well into the Earth’s atmosphere. While some teens are focused on cleaning up the land and water on our planet, Yang has made it her mission to clean up the litter clogging space. And she may just save lives and billions of dollars while she’s at it.
Yang was 15 when she first heard about the escalating issue of space debris that is polluting the Earth’s lower atmosphere. After watching the 2013 movie, “Gravity,” Yang imagined a world in which colliding space debris could set off a series of catastrophic events that threaten lives and technology. While the plot of “Gravity” broke some of the major rules of physics, the underlying premise — that a collision of space debris could lead to disaster — rang true and stuck in Yang’s mind.
That year, over her winter break in Florida, she brushed up on astrophysics, computer coding, and the ins and outs of space junk and developed a program called Seer Tracking to provide an accurate location for each piece of junk orbiting the Earth. Currently, there are millions of pieces of space debris, ranging in size from defunct satellites to tiny specks of paint. Traveling at a rate of around 17,500 miles per hour, each item has the potential to cause a catastrophic collision.
The Department of Defense’s Space Surveillance Network currently analyzes Earth’s space debris using tracking and data that could detect a potential collision as far as 10 days in advance, But according to Yang, her Seer Tracking program is able to predict issues weeks ahead. And the scientific community concurs. Her work has earned her the top award at the Intel International Science and Engineering Fair as well as a spot on Forbes 30 under 30 list. Yang has presented her research at the European Organization for Nuclear Research (CERN) in Geneva, Switzerland and was asked to speak at a TEDx Conference about the issues that women face in STEM careers.
Seer Tracking uses artificial neural networks to track space debris. In other words, Yang has developed an algorithim that allows the program to learn from its mistakes, so that its predictions become more accurate over time. As the data accumulates, Seer Tracking increases in its ability to pinpoint debris locations and predict collisions well in advance.
Yang is now finishing up her sophomore year at Stanford University, pursuing a degree in physics while operating Seer Tracking on the side. She’s still working on improving her software and exploring other avenues of “deep learning” in which computers learn from their mistakes.