On Learning Outside the Classroom
An account of self-directed learning, from patching together early access to technology to research in computer vision and deep learning.
I grew up in Lagos and moved to Boston when I was seven. At age ten, I relied on a neighbor's sporadic wifi connection and put together components of broken computers, learning as much as I could whenever I had access to mentors and resources. My early exposure to technology moved me to conclude it would be the most effective way to surmount scarcity. Learning was never confined to the four walls of a classroom.
At fourteen, the value I saw in the openness of technology led to a conviction: technology has an unmatched ability to be transformative. This curiosity bloomed into a hypothesis. In today's hyper-informed world, brimming with various forms of media, could I do something I'd never witnessed firsthand? I questioned whether I could teach myself to code. With a refurbished laptop and a new outlook on computer science gained through a scholarship-funded summer program at Phillips Exeter, I set out to test it. Initially, coding seemed impossible. After countless hours, it clicked. I could translate my ideas into buildable solutions. By cultivating an understanding of something as alien as coding, I was no longer confined to the four walls of a classroom but liberated to the vast expanse of the world.
One inspiring office hour with a professor spurred a gap year, then an internship at UPS. Undeterred by a forty-mile commute from New York into New Jersey without a car, I was driven to take advantage of the learning opportunity and use it to influence what I would study. Upon arriving, I was eager to apply a more structured understanding of computer science to find practical uses for computer vision. I recruited graduate students and built a project that utilized emotion recognition and computer vision to acquire feedback from users in a non-intrusive way. We applied the correlation between particular emotions and injuries, which led us to win the UPS National High Tech Award. Despite receiving a full-time offer, I saw more value in grounding myself in developing computer science fields.
The next year, I returned to school and worked through physics, calculus, and computer science, then reached out to Stanford's Center for Advanced Functional Neuroimaging to learn firsthand about interdisciplinary computer vision research. Working at the CAFNLab and seeing deep learning algorithms lead to tangible reductions in cancer risk deepened the passion further.
The memory of using a neighbor's distant wifi connection to learn to code is still what drives all of it. The goal has always been the same: use technology to push boundaries for people in nonideal circumstances.