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Rachel Millin is a machine learning engineer with over 15 years of experience in various research and industry roles. Rachel started their career as a research assistant at the University of California, San Diego, where they focused on physics research. Rachel then worked as a staff research associate at the Center for Imaging of Neurodegenerative Diseases, before joining HRL Laboratories as a research intern. During their time at HRL Laboratories, they developed a software pipeline for automated brain imaging data analysis to understand how meaning is derived from sentences. Rachel then pursued their PhD in neuroscience at the University of Southern California, where they designed and implemented a simulation model of fMRI data to reveal flawed assumptions in common analyses.

After completing their PhD, Rachel worked as a postdoctoral researcher at the University of Washington, where they conducted brain imaging studies on autism. Rachel later joined Insight Data Science as a Fellow, where they developed an educational web app using Python and Flask.

Rachel then joined Intellectual Ventures/Global Health Labs as an associate scientist, specializing in convolutional neural networks and their applications in ultrasound video analysis. Rachel worked on projects related to pneumonia detection, lung collapse diagnosis, and gestational age estimation. Rachel'swork involved optimizing network architecture, developing novel layers, and advising external partners on algorithm development.

Most recently, Rachel joined Caption Health as a machine learning engineer. Rachel'sspecific role and projects at this company are not provided in the given information.

Rachel Millin earned their Bachelor of Science (B.S.) degree in Physics from UCLA, attending from 1999 to 2002. Rachel then pursued their Doctor of Philosophy (Ph.D.) in Neuroscience at the University of Southern California, studying from 2008 to 2014.