Director of Engineering
Jeff focuses on optimizing engineering delivery processes to deliver client projects on time and on budget.
As Very’s Director of Engineering, Jeff leverages his broad technical expertise to architect systems that involve intelligent machines within client projects.
Before graduating to his Director of Engineering role, Jeff was a regular contributor to the OTA (over the air) firmware update server NervesHub, applying his learnings from Very's IoT projects. He also served as a machine learning and hardware solutions leader.
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.
- MS in Mechanical Engineering from Tennessee Tech University
- MS in Computer Science with a focus in Machine Learning from Georgia Institute of Technology
- Holds a patent for his work on computer-implemented intelligent alignment method for color sensing devices
- Published research on optimal torque control of an integrated starter–generator using genetic algorithms
Jeff is naturally drawn to problems that most people consider "unsolvable,” and he enjoys solving those kinds of problems at Very. He 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.
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.