Bio

Check out my portfolio website for an up-to-date summary of my work: https://cameronrwolfe.me/ As a researcher in both academia and industry, I have a passion for leveraging basic, academic research to solve large-scale industrial problems. My primary goal is to be a bridge between these communities by making scientific information interpretable to all and helping to draw connections between scientific advancements and pressing industrial problems. I am a PhD student in Computer Science at Rice University in Houston, TX advised by Dr. Anastasios Kyrillidis. My interests are loosely related to math and machine/deep learning, including non-convex optimization, theoretically-grounded algorithms for deep neural networks, continual learning, and deep learning on video data. Prior to Rice, I was an undergraduate student in Computer Science at UT Austin, where I worked with the Neural Networks Research Group on research related to genetic algorithms and evolutionary computation. Outside of academia, I am a Research Scientist at Alegion, a software startup based in Austin, TX. At Alegion, I focus on the development of long term, practical research projects in several areas, including streaming training of deep neural networks and evaluating the quality of video-based annotations for computer vision applications. Additionally, I produce a lot of technical content—both internally and externally—with the goal of familiarizing those outside of academia with important topics and considerations relevant to artificial intelligence.