At Very, Jeff brings his applied mathematics and machine learning knowledge to a vast array of problems and projects involving images, natural language, social graphs, temporal data, and geospatial data.
In his role as Very's IoT Practice Lead, Jeff is a regular contributor to the OTA (over the air) firmware update server NervesHub (currently in a pre-release stage), applying his learnings from our IoT projects. He also served as a machine learning and hardware solutions leader for Hop, a client we worked with to build the world’s first facial recognition-powered beer tap. During the project, Jeff leveraged his academic background in control systems and robotics to ensure a successful launch.
Before joining Very, Jeff was a research and design engineer at Variable, Inc., where he developed proprietary mathematical models for accurate color measurement; set up a scientific analysis Python environment with custom modules for internal company use; and built and deployed internal tools that allow non-technical workers to apply machine learning models.
In addition to holding a patent for his work on computer-implemented intelligent alignment method for color sensing devices, Jeff has published research on optimal torque control of an integrated starter–generator using genetic algorithms.
Jeff regularly speaks at national events about IoT development best practices and presented his academic research at the Society of Automotive Engineers World Congress. Jeff also founded Data Science Chattanooga, a meet-up for data science professionals in the Chattanooga area.
Jeff holds a BS in Mechanical Engineering from Tennessee Tech University, an MS in Mechanical Engineering from Tennessee Tech University, and an MS in Computer Science with a focus in Machine Learning from Georgia Institute of Technology.