Control, Robotics, Machine Learning
I received my B.S. in Physics and Engineering with electrical emphasis from Hope College. While there, I designed automated part inspection systems for Lakeshore Vision and Robotics. I also worked at NASA’s Goddard Space Flight Center in the modeling, control, and construction of a reconfigurable tetrahedral rover. I received my Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign, emphasizing in control theory, robotics, and machine learning. While at Illinois, I was a member of the Language Acquisition and Robotics Group. At Beckman, I researched embodied cognition on the iCub, a humanoid robotics platform from the Italian Institute of Technology. During this time I also contracted for Valve Software in the design of sensor fusion systems for head mounted VR tracking, I instructed many courses and labs including the robotics and control labs and the senior design lab, I mentored undergraduates, and I had leadership roles with the NASA academies and FIRST Robotics. After graduation, I worked on autonomous driving and navigation at Petronics, a robotics startup at the tech incubator in Research Park. While at Petronics, I developed an AR control interface with integrated sensor fusion and map building, multi-session SLAM with a monocular camera and globally consistent occupancy, autonomous driving with surface-aware planning and dynamic obstacle avoidance, and motion stabilization for a mobile 360 camera. I also co-wrote and won an SBIR grant for over $1M. After working at Petronics, I began doing remote machine learning work. I joined Sententia and worked on a cloud document classification pipeline with image to text services and image classifiers. After that, I joined Clostra and worked on a deep learning based time series anomaly detection system. I also worked on system-level integration of a TSDB with MLOps that interfaced with a multi-user front-end.
Model Predictive Control
Generative Adversarial Networks
Recurrent Neural Networks
Neural Radiance Fields