Summary
Alex Reynell is a robotics researcher with eight years of experience applying sensor fusion, state estimation, motion planning and control to autonomous spacecraft, aircraft, and ground vehicles. Based in Palo Alto, he combines rigorous academic training from Rhodes, Stellenbosch and Stanford with hands-on work at Omron Research Center and NIO, where he focused on multi-sensor multi-object tracking and adaptive IMM filtering. His technical toolkit spans C++, Python and MATLAB, ROS middleware, advanced Kalman/particle filters, visual SLAM/odometry, trajectory optimization and MPC. Alex excels at turning complex estimation and optimal-control theory into practical perception and guidance modules that run on real platforms. He brings a rare mix of aerospace-focused optimal guidance techniques (pseudospectral and direct multiple shooting) and industry-grade perception engineering. Colleagues describe him as analytically driven yet pragmatic, able to bridge research prototypes and deployable robotic systems.
8 years of coding experience
1 year of employment as a software developer
Master of Science, Aerospace, Aeronautical and Astronautical/Space Engineering, Master of Science, Aerospace, Aeronautical and Astronautical/Space Engineering at Stanford University
Mechanical Engineering, Mechanical Engineering at Stellenbosch University
Bachelor of Science, Mathematics, Bachelor of Science, Mathematics at Rhodes University