Connor Lane, MSE
Connor Lane is a scientific software developer with the Computational Neuroimaging Lab, where he builds tools to better understand large-scale brain imaging data. He earned bachelor’s degrees in mathematics and philosophy from the University of California Los Angeles, where he graduated summa cum laude. He then became a lab manager at the Johns Hopkins University Neuroplasticity and Development Lab, where he built MRI data analysis tools and contributed to research on how experience shapes brain function. Building on his math and cognitive science backgrounds, he then earned a master’s in computer science at Johns Hopkins, focusing on machine learning. At the same time, he worked in the Johns Hopkins University Vision Lab developing new algorithms for identifying hidden structure in complex image data. At the Child Mind Institute, Connor is excited to explore new ways to analyze and visualize neuroimaging data using modern data science and machine learning.
Education
- MSE, Computer Science, Johns Hopkins University
- BS & BA, Mathematics & Philosophy, University of California Los Angeles
