Jeff Vickers

Jeff Vickers has extensive experience in the engineering field. Jeff started their career in 2008 as an Engineering Co-op at Tampa Electric. Jeff then worked as a Mechanical Engineer at Honeywell, where they developed and tested extremely high tolerance components for GPS navigation satellites and conducted thermal and vibration analysis on system components. In 2011, they worked as a Biomechanics Robotics Research Assistant at the University of South Florida. From 2012 to 2014, they worked at Leader Tech Inc. as a Mechanical Automation Engineer and at Sophic Systems as a Mechanical Design Engineer, where they designed, analyzed, and manufactured medical devices and conducted product design and usability studies. In 2014, they joined Ekso Bionics as a Senior Robotics Engineer, where they designed, prototyped, and tested robotic exoskeletons for healthcare, military, and industrial markets. Jeff developed real-time robotic software packages, including dynamic control algorithms, state estimation, forward and inverse kinematics, sensor filtering and conditioning, sensor fusion, motion planning, low-level motor control, user interfaces, data logging, and error handling. Jeff also led research efforts to advance state-of-the-art exoskeleton control methods. In 2019, they joined AutoModality as a Lead Robotics Platform Engineer, where they were responsible for systems integration and platform architecture to develop drone technology capabilities. Jeff oversaw full system development, including requirements development, hardware design, sensor fusion, vendor management, assembly, autonomy execution and testing, and root cause cause analysis. In 2020, they joined Unspun as an Automation Lead.

Jeff Vickers obtained a Bachelor of Science in Mechanical Engineering from the University of South Florida in 2010, followed by a Master's degree in Biomedical Engineering from the same university in 2012. Additionally, they have obtained three certifications from Coursera: Machine Learning in May 2016, Neural Networks and Deep Learning in August 2017, and Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization in August 2017.