I am a data scientist with a strong background in statistics, modeling, machine learning, experimental design, and A/B testing. I use machine learning to detect patterns, build models, and extract insight from large datasets. Moreover, I can use those insights to design experiments, and implement A/B testing to confirm cause-effect relationships. This enables a full-cycle data driven approach to iterate through product development, or grow a business.
In 2012, I earned a Ph.D. in Materials Engineering from Northwestern University. I developed and automated a 3-Dimensional image reconstruction algorithm leading to new understanding of performance in solid oxide fuel cells. I authored 17 peer-review articles in my 5 years of graduate school.
I worked 2.5 years at Intel's R+D group where I developed a manufacturing process for next-generation computer chips. I ran hundreds of A/B and multivariate tests to invent new patterning techniques, allowing future technologies to become reality. I analyzed data from many different servers to control deviations in the line and automate problem detection.