Noah Rowe

Hi! I am a recent Master's graduate in Engineering Physics from Queen's University, with a specialization in deep learning and data science. I am passionate about using machine learning to develop novel solutions to interesting problems and am particularly interested in the healthcare and sports industries. 

As a recent graduate from my Master's program, much of my professional experience surrounds my work with the university. As a graduate student, I supervised the 4th-year thesis project of an Engineering Physics student for the following project (of my creation): an artificial intelligence model to create a novel NHL player evaluation metric, trained on NHL play-by-play data. Was able to generate novel results on a restricted timeline. 

I also worked as a Graduate Teaching Assistant for two years, where I participated in the execution of an advanced physics course, tailored particularly for first-year undergraduates. This challenged me to delve deep into and comprehensively understand high-level scientific concepts, from electromagnetism to general relativity, and advanced calculus to dynamic and static energy systems. Leveraging this depth of knowledge, I was able to deliver targeted lectures, focusing particularly on areas identified as challenging for students. Not only did I aim to elucidate these complex concepts, but also to foster an environment of enthusiasm around learning such integral aspects of physics.

Beyond the classic teaching measures, I also facilitated interactive tutorials, enabling students to engage actively with physics problems and encouraging them to apply their learning in practical situations. I was proud to help them navigate through their academic challenges, providing support and guidance. In addition, I managed laboratory sessions, ensuring a secure environment while promoting efficient learning. Realizing that many first-year students grapple with the transition from high school to the advanced study setting of a university, I also undertook the role of mentor, offering advice and strategies to overcome any academic growing pains. In all my interactions, I aimed to foster a supportive space conducive to exploration and deep comprehension of physics.

Pursuing my Master's program, I delved deeply into quantum physics, statistical methods, reinforcement learning, and advanced concepts of deep learning. My research was centred around an international dark matter search experiment, where I was responsible for the design of deep learning techniques to analyze the experimental data more effectively.

Throughout my research, I had the opportunity to mentor colleagues in the application of machine learning within a physics context, enhancing the mutual learning environment. This culminated in successfully onboarding another Master's student into our collaborative efforts.

My expertise granted me the opportunity to present our research findings at several physics-focused, non-machine learning conferences, both domestically in Canada and on international platforms. As a testament to my active involvement in collaborative research, I have contributed to various widely recognized publications. Furthermore, I co-authored a crucial paper detailing the use of innovative machine learning techniques, purposefully developed to eliminate noise interference from particle detector signals.