When J. Halcombe Laning Jr. arrived at MIT in 1938, he was ready to take the world by storm. Good thing. “I arrived in Boston on the afternoon of the Great New England Hurricane of 1938,” says Laning. “My trunks arrived a week later.”
A native of Kansas City, MO, Laning earned a bachelor’s in chemical engineering and then a PhD in applied mathematics. In 1945 he began working at the MIT Instrumentation Lab, which became Draper Laboratory; he retired from the lab in 1989. “In its early years, Draper was a very informal organization,” he says. “I was given a degree of freedom to follow my own nose, follow my own interests.”
His nose led him to the development of the first algebraic compiler, completed in 1953, which was used on MIT’s Whirlwind computer and became the forerunner to programming languages such as Fortran. Also in the 1950s, Laning’s work was instrumental in the development of the Q-guidance system used in the Thor and Polaris ballistic missiles. “I’ve always referred to myself as a professional dilettante,” he says. “Much of the work was done by others, but I supplied the basic concept, the basic equations.”
Early work on an unmanned Mars project helped the lab secure the contract to develop the guidance system for the Apollo project in the early 1960s. Laning helped design the Apollo architecture for airborne computers and programmed the executive software used in the first manned lunar landing (see “Apollo’s Rocket Scientists,” p. M12). Later in his career, he developed automation technology for car manufacturing and software for a scanning electron microscope. He is a member of the National Academy of Engineering, the American Mathematical Society, and the IEEE, and he coauthored the 1956 book Random Processes in Automatic Control.
Laning and his wife, Betty, who died in 2005, raised four children in Newton, MA. He remembers the MIT of his student days as a place where real-world challenges intersected with theory. He recalls an exam that included a flue gas analysis where the students were given inconsistent data. “We were supposed to be able to identify that fact,” he says. “We had to look at the problem as a problem. That did me good later in life.”
Become an MIT Technology Review Insider for in-depth analysis and unparalleled perspective.Subscribe today