After much investigation of my abilities, interests, and varied pursuits, I’ve found a sense purpose in contending with computer vision problems and from this feeling an unwavering focus to make solving these problems my career. Computer vision in a way also embodies my second, third and fourth choice as a career. Mathematicians map the universe(s) to quantities; engineers contemplate tangible solutions to real-world problems; artists spend years mastering the art of creatively visualizing ideas and how others interpret these visual percepts. As a student researching computer vision, I find myself integrating all of these skills which I sincerely enjoy.
My delight for math began in my middle school algebra classes. The process of determining the value of an unknown X was fascinating, and this allure remained with me throughout high school, causing my teachers to push me to enter math competitions in both geometry and calculus. Diligent practice in both team-based and individual events landed me an invitation to a regional math meet competing in geometry. Enjoying math as much as I did, I decided to earn my undergraduate degree in mathematics at the University of North Carolina – Charlotte. Although I enjoyed learning more advanced topics in math, I quickly felt the need to search for something additional, some creative application for it, so I added a computer science major and volunteered to work in the Future Computing Lab at UNCC. By volunteering in the Future Computing lab I was introduced to relevant and thought-provoking problems that renovated my perception of academic study, it allowed me to continue improving my math skills while inviting me to develop creative and applicable solutions. For example, my first project involved networking and calibrating multiple depth sensors into an affordable gait analysis system. The system also required the development of a graphical interface for displaying various metrics of an individual’s gait. Reading related works, documenting progress, and working independently under the guidance of an advisor are all skills I began to develop during that project. After encouragement from my advisor, I applied and was accepted to participate in the research experience program for undergraduates under Dr. Min Shin, where I studied automated tracking of biological cells in video microscopy. From that point on I’ve been hooked.
Since then I have published and presented at WACV 2015 a paper for a multi-object data association algorithm that requires significantly less training effort by the user. Recently, I also presented our latest method for automatically detecting cracks in nuclear power plant structures at WACV 2016. Now I am exploring further improvements to object tracking and crack detection algorithms through deep learning and unsupervised machine learning techniques. Additionally, I am developing an application for abstracting nearly all the image processing and computer vision knowledge required by individuals attempting to incorporate multi-object tracking results into their research (e.g. complex adaptive behavior and the division of labor within social insects, pedestrian tracking for analyzing use of public spaces, etc.).
From both a career oriented and educational perspective, my goals are homogeneous; learn and apply new information to solve problems that matter. I plan to continue progress in developing algorithms and improving learning systems for computer vision applications. My more personal strengths are curiosity and empathy, and my creativity is exercised through art and problem solving. If you can penetrate my shy tendencies, you will find humor is my hidden trait and that my weakness is patience. My personal aspirations are to maintain many quality relationships and live a well-rounded life. My professional ambition is to be a modest and respected computer scientist.