Mark McClelland

Mark McClelland is a seasoned professional with a strong background in robotics, specializing in autonomous vehicles and ground platforms.

He has expertise in a wide range of robotics areas, including localization, mapping, object tracking, and high-level planning systems, utilizing nonlinear optimization and Bayesian reasoning.

Mark has a diverse educational background, holding a Ph.D. in Mechanical Engineering, a Master of Science in Mechanical Engineering, a B.S. in Physics, and a B.A. in Classical Studies from reputable institutions.

His professional journey includes roles such as Staff Software Engineer at Zoox, Sr. Staff Engineer at Tesla, and Researcher at Nissan Motor Corporation.

Mark's experience spans theoretical algorithm design, system design, and developing production C++ code for real-time, compute-constrained systems.

He focused his doctoral research on applying machine learning, state estimation, and qualitative reasoning techniques to enhance autonomous and semi-autonomous robotic operations.

Mark's research contributions include developing qualitative reasoning algorithms for long-term robotic mapping and navigation, as well as creating statistical models to predict human responses to time delays in remote vehicle operations.

His background also features positions like Post-Doctoral Research Associate at Autonomous Systems Lab at Cornell University and Visiting Research Assistant at Jet Propulsion Laboratory.

Mark's early career includes roles such as Graduate Research Assistant at Autonomous Systems Lab at Cornell University, Lead Designer at Diehl Equipment, and Network Administrator at Seattle Public Schools.