Contributors
Adam Green || Graduate Student (Team Lead)
Adam is a PhD candidate at the University of Colorado Boulder in the field of experimental soft-matter (liquid crystal physics). As part of his thesis, he discovered two new phases of matter and designed a new type of flowmeter. His latest project involved tracking the statistics of topological defects in high-speed video microscopy, which, using traditional methods, would involve hand tagging thousands of defects. The realization that modern machine learning methods are uniquely suited to this challenge was the inspiration behind this project— which is looking at the efficacy of machine learning methods to analyze experimental data.
He has a background in both experimental and computational physics, and is currently leading this project and writing his thesis, which focusses on the behaviour of quasi-2D liquid films. You can track his progress writing said thesis on github.
He is expecting to graduate August 2019, and is actively looking for opportunities in industry with focus on data science. You can follow him on his personal blog.
Eric Minor || Research Assistant
Eric is a recent graduate of the University of Colorado Boulder, graduating Summa Cum Laude with distinction with degrees in Physics, Computer Science and Mathematics. He has worked for the past two years in the Liquid Crystal Films Lab at the CU Boulder Soft Materials Research Center, designing and implementing software to efficiently analyze video data from experiments conducted aboard the International Space Station and terrestrially.
He has been the primary engineer responsible for designing the SETT system, which massively simplifies the process of enhancing simulated images, training YOLO models on those enhanced simulation images, and then validating those models against real-world experimental images. He hopes the system will make the benefits of machine learning systems for computer vision more accessible to groups without advanced machine learning expertise.
Jeffrey Moore || Graduate Student
Jeff is a physics PhD candidate at CU Boulder, where he studies nonequilibrium dynamics in the context of biological active matter. As a computational physicist, he writes his own Brownian dynamics simulations to model the physical systems in his research, and analyzes his results using statistical methods in Python.
Jeff has been interested in machine learning since 2017, and has taken classes in statistical modeling and Bayesian inference at the university to supplement his self-study of machine learning from resources online. He is expecting to graduate in May 2020 and pursue a career in data science.
Stian Howard || Post Undergradate Student
Stian has completed a Bachelors of Science degree of Engineering Physics, and two minors in Computer science and Applied Mathematics at University of Colorado Boulder. He is currently on an unintential gap year before looking into Masters and/or PhD programs.
He is interested in simulation and computational mathematics, and is interested in working in industry after completing his schooling over the next couple years. In the mean time he is looking for work to do, and potentially taking a couple months to find a sailboat to crew on before starting on graduate school.