Jonathan P. How

Also known as Jon How, Jonathan Patrick How

Jonathan P. How is an MIT aerospace engineer who studies robust planning and learning under uncertainty, with an emphasis on multiagent systems. Born in England in 1965, he studied engineering science at the University of Toronto, earned his master's degree and PhD at MIT, and taught at Stanford from 1994 to 2000 before returning to MIT. In 2007, he led planning and control for MIT's fourth-place DARPA Urban Challenge team; later work under his guidance applied deep reinforcement learning to socially aware robot navigation, robustness to adversarial inputs and multiagent learning. The National Academy of Engineering elected him in 2021 for contributions to decision-making and control of intelligent autonomous aerospace vehicles; since July 2025, he is the Ford Professor of Engineering in MIT's Department of Aeronautics and Astronautics and directs the Ford-MIT Alliance.

Now at: Massachusetts Institute of Technology
Works: 1,032
Citations: 36,140
h-index: 91
Rankings: #77 by last-author papers
Badges: Bridge
Loyal

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

1965–1993

Degrees at Toronto and MIT

Born in England in 1965, he earns a B.A.Sc. in engineering science (aerospace) at the University of Toronto in 1987. At MIT's Department of Aeronautics and Astronautics, he completes a master's degree in 1990 and a PhD in 1993 with a thesis on robust control design with real parameter uncertainty.
1993–2000

Shuttle experiment and Stanford

As a postdoctoral associate at MIT's Space Engineering Research Center from 1993 to 1994, he works on the Middeck Active Control Experiment (MACE), which flies on Space Shuttle Endeavour in March 1995. Stanford's Department of Aeronautics and Astronautics hires him as an assistant professor in its Aerospace Robotics Laboratory, where he serves from 1994 to 2000.
2000–2009

Maclaurin Professor at MIT

He returns to MIT as an associate professor of aeronautics and astronautics in April 2000, earns tenure in 2003 and becomes Richard Cockburn Maclaurin Professor in 2009. In 2007, he serves as planning and control lead for MIT's DARPA Urban Challenge team, which places fourth in the race at Victorville, California.
2014–2019

Editor of Control Systems Magazine

From 2014 to 2017, he sits on the US Air Force Scientific Advisory Board, and his service there earns him the Air Force Commander's Public Service Award in 2017. He is editor-in-chief of IEEE Control Systems Magazine from 2015 to 2019. AIAA names him a Fellow in 2016, and IEEE elevates him to Fellow in 2018.
2017

Socially aware robot navigation

In 2017, he coauthors "Socially aware motion planning with deep reinforcement learning" with Yu Fan Chen, Michael Everett and Miao Liu. Trained in simulation with reinforcement learning, their knee-high wheeled robot avoids collisions while keeping pace with pedestrians in tests inside MIT's Stata Center.
2020–2022

National Academy of Engineering

In 2020, he receives the AIAA Intelligent Systems Award and the IEEE Control Systems Society Distinguished Member Award. The National Academy of Engineering elects him in 2021 for contributions to decision-making and control of intelligent autonomous aerospace vehicles, and the University of Toronto inducts him into its Engineering Hall of Distinction in 2022.
2021–2022

CARRL and the FURTHER framework

In 2021, he advises CARRL (Certified Adversarial Robustness for Deep Reinforcement Learning), a method that helps machines act safely on uncertain or adversarial inputs; it outperforms standard techniques in a simulated collision-avoidance test and in Pong. As a member of the MIT-IBM Watson AI Lab, he is senior author of FURTHER in 2022, a multiagent reinforcement learning framework presented at NeurIPS that lets cooperating or competing AI agents consider what other agents will do as time approaches infinity.
Since July 2025

Ford Professor of Engineering

He becomes Ford Professor of Engineering in MIT's Department of Aeronautics and Astronautics in July 2025. His research focuses on robust planning and learning under uncertainty, with an emphasis on multiagent systems. Director of the Ford-MIT Alliance, he is also president-elect of the IEEE Control Systems Society for 2026–2028.