Jason Carpenter is a Machine Learning Engineer at Manifold. In this capacity, and with a project range spanning from developing computer vision applications to traditional modeling approaches to building data pipelines, he contributes to many client projects as a jack-of-all-trades engineer.
Prior to Manifold, Jason was a Neuroscience Research Associate at UCL (University College London), where he investigated specific brain regions by integrating computational modeling and neuroimaging techniques. This work included leading a global team to develop and execute a functional magnetic resonance imaging (fMRI) study. He also analyzed high-dimensional longitudinal neuroimaging data using Statistical Parametric Mapping (SPM) in MATLAB.
We are delighted to welcome Jason as a full-time team member after getting to know his work during the course of his Master's in Data Science program at the University of San Francisco (USF). He worked with our team and other interns to develop a multi-camera-based person-tracking system using state-of-the-art computer vision techniques, and also to develop predictive maintenance tools for the connected oilfield (IoT). We know he will continue to contribute a lot to the team at Manifold.
“I love that I get to wake up every morning and go work on topics that fascinate me,” he says. “In this role, my time doing enjoyable, meaningful work lends itself directly to ROI for our clients.”
Jason is constantly expanding his skill-set and growing as a multi-faceted engineer. He was recently lead author of an article in the Journal of Experimental Psychology: Domain-general enhancements of metacognitive ability through adaptive training. During his time as a Master's student at USF, Jason developed two packages that have received over 325 stars combined on Github. They are:
Jason earned his Master of Science degree in Data Science from the University of San Francisco (USF), and his Bachelor of Science degree in Cognitive Science from the University of California, Los Angeles (UCLA).