A Sea of Change
When I was in grade 9, my teacher showed us a video about coral that would end up changing my life, though I didn’t know it at the time.
Coral can look like a plant, or maybe a strange kind of twisty stone. It’s still hard to wrap my head around the fact that these cool underwater structures are sessile animals—living creatures rooted to the ocean floor.
Ocean acidification caused by our carbon emissions is destroying coral and the reefs they produce. If coral reefs disappear, marine life will lose their homes, the fishing industry will collapse, and coastlines will rapidly erode. Learning about the effects of climate change in school made me wonder what I could do to help.
I decided I wanted to get more involved in STEM and the science community. I stumbled upon Future Science Leaders (FSL), Science World’s after school program for teens, and I still feel so lucky I did. The mentorship with experts, professors and PhD students; the network of like-minded peers; the training in advanced STEM techniques and skills—FSL really seemed like the place for me.
In Year 1 of FSL, two guest speakers gave presentations on machine learning and deep learning. I was blown away by the technology and started researching it right away. Something told me if I worked hard enough, I could use this technology to solve a big problem related to climate change. Though I didn’t know yet which one.
And then, in the summer of 2019, as I prepared for Grade 10 and Year 2 of FSL, the effects of climate change hit close to home.
Where There’s Smoke, There’s Fire
Wildfire smoke from fires in Alberta settled around my home in Surrey and lingered for a long time. Every day I could smell the smoke and see the ash, and the province extended multiple air-quality advisories. I truly realized the impact and scale of wildfires and climate change now that something hundreds of kilometres away was directly affecting my community.
I read an article about how firefighters use drones to monitor and map wildfires and became super interested in the subject. I contacted a bunch of local BC wildfire firefighters to learn more. One of them told me that the use of autonomous drones in firefighting is a really new technology—just a few years old—and that there’s a lot of potential for it to advance. That got me really excited because I felt like I could enter the field early and help make a difference.
I decided to take the skills I was learning in FSL and build software that uses deep learning to identify and locate fire. The hard skills—like robotics, microcontrollers, and soldering—enabled me to do this. But the soft skills helped too. I learned collaboration, teamwork and problem-solving, which guided me through the challenges I faced throughout my projects. And believe me, I faced a lot of challenges.
For example, when you want a machine learning algorithm to detect fire, you have to provide it with data of images labeled as wildfire. Collecting and labeling these images was a huge and difficult task. At first, my algorithm only detected fire with 40% accuracy. That wasn’t good enough. My mentor provided me with options to explore, showing me how different factors produced different results. In the end, I got to 80% accuracy. FSL showed me that, no matter what, you’re going to encounter problems and the way to solve them is through experimentation.
My project Deep Learning Based Fire Recognition for Wildfire Drones was a part of my Schulich Leader Scholarship application. I’m still so bowled over by the fact that I won. Now that my time at FSL has come to a close, the $100,000 will help me keep going. I’m proud of what I’ve done. I'm entering UBC this fall and I plan to join their autonomous drones team and continue pursuing computer science and machine learning to solve important issues.
Even though I’m not on the Schulich prize committee, I know FSL helped me win that prize. If you look at the Schulich website, they call for “innovative and entrepreneurial scientific leaders.” In a nutshell, that describes FSL. Future science leaders—the name says it all.
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