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Hi! My name is Nolan McMahon. First and foremost, I am a devout protestant Christian. My life-long goal is to continually strengthen my personal relationship with Jesus Christ and live each and everyday in a way that brings glory to Him.
I’m the Machine Learning Specialist at a marketing and technology company called LoKnow. Currently I’m working on a product called Rover which uses machine learning to devise relevant, “google-able” search phrases for our client’s websites.
Since I was a young boy, I’ve always had a keen interest in mathematics and the way that the world works. As a six year old, one of my favourite activities was to complete an endless book of sums. At age nine, I would create, and then compute enormous quotients. By the time I was thirteen, I was exasperated by endlessly tedious and slow-moving math classes in grade school, so I picked up ninth grade math and taught it to myself out of a textbook in three months.
From that point forward, I was hooked on self-directed learning. I didn’t hop back into a formal math class with my peers until introductory calculus. I consistently scored the highest among my elder peers and would compete in extracurricular contests like the University of Waterloo’s Fermat math contest, during which I scored the highest in my school.
When it came time to enter university, I chose to major in physics. I graduated with an honours degree from the University of Calgary. While I was there, I fed my passion for self-directed learning by taking on many advanced topics which were beyond the scope of my degree. Throughout my studies, I took physics classes in: classical mechanics, electrodynamics, thermodynamics, statistical mechanics, electronics, applied physics, modern physics, optics, mathematical physics, special and general relativity, quantum mechanics, computational physics, and quantum statistical physics.
In my final year of formal studies, I wrote a thesis and gave an oral presentation in contribution to the greater aim of verifying the efficacy of quantum computers in the context of np-complete problems. In so doing, I had the privilege of studying under the director of the Institute for Quantum Science and Technology, Distinguished Chair Professor at the University of Science and Technology of China, and member of the Royal Society of Canada, Dr. Barry Sanders. That research is still ongoing.
While I did pick up a wide assortment of skills during my time studying physics, the one which was the most important and has carried me the furthest is the skill of solving hard problems. I compressed my studies into a very short timespan, and in so doing, I forced myself to learn concepts quickly. Most of the time, there were no answers in the back of the book, and the problems were vaguely defined. How does one solve such a problem? You have to make use of every resource at your disposal. Teamwork, mathematical simplification, inexact numerical approximation and others were just some of the techniques I mastered.
Post-graduation, I have continued to pursue my three main academic passions: physics, computer science, and mathematics. As such, being a machine learning specialist has been a perfectly engaging role for me to take on. When I’m not improving search engine results, you can find me flipping through academic texts. I also enjoy fulfilling my role as a son and brother in my tight-knit family, and getting some fresh air by walking and cycling around my neighbourhood.
If you are still interested, take a look at my resume or my university transcript.