School of Engineering

Writing for Nature, Deputy Editorial Director Maia Weinstock honors the life and legacy of the late Margaret Hamilton, a pioneer of the software engineering field, who, at MIT’s Instrumentation Lab, led the team that wrote and tested software for the Apollo Guidance Computer onboard Apollo 11. Hamilton’s story “was also my inspiration for one of the vignettes in a Lego set that I created in 2016, featuring Hamilton and several other women in NASA history,” writes Weinstock. “She continued work at [her Universal Systems Language company] Hamilton Technologies well into her 80s, never failing to inspire me and countless others with her tenacity, humour, warmth and childlike wonder.”

MIT researchers have developed the Probabilistic Bias Correction (PBC) tool, which “uses machine learning to identify systematic bias in a model and make corrections” to improve weather forecasts, reports The Verge’s Justine Calma. “The researchers found that PBC improved the accuracy of a leading physics-based weather model by the European Centre for Medium-Range Weather Forecasts (ECMWF) for 98 percent of precipitation and 92 percent of temperature targets,” notes Calma. “The machine learning framework also doubled the accuracy of ECMWF’s new AI forecasting system in the study’s analysis.” 

Gizmodo’s Ellyn Lapointe spotlights the Probabilistic Bias Correction (PBC) tool developed by MIT researchers to “correct systemic errors in forecast models and improve the accuracy of their subseasonal outlooks.” “Extreme weather has the largest impact on our society and improved forecasts of these events can yield large societal benefits,” says Research Scientist Judah Cohen. “Interestingly, previous bias correction schemes actually made forecasts of extreme weather worse, and our model showed clear improvement of extreme weather forecasts.”  

MIT researchers have developed an AI system “that not only minimizes forecasting errors, but learns from its inevitable mistakes through a method called postprocessing,” writes reporter Andrew Paul for Popular Science. “Their AI postprocessing integration blew the existing forecasting models out of the water,” notes Paul. “Such massive improvements could soon help the farmers and energy providers who already rely on subseasonal forecasts, but the benefits may even save lives.” 

MIT startup LNK Energies has created a system to efficiently convert heavy-duty trucks from diesel to hydrogen, “slashing pollution and costs while shielding operators from the price spikes that result from geopolitical instability,” writes Tim De Chant for TechCrunch. “A lot of the reactors that LOHC (Liquid organic hydrogen carriers) companies have today are really large. They’re about the size of a shipping container,” says Alexis Huynh ‘24, cofounder of LNK. “We have shrunk that basically to the size of a mini fridge.” 

Using a technique based on Raman microscopy, MIT researchers have found a way to detect senescent cells, “zombie cells” that accumulate as tissues age, without destroying them, writes reporter Eric Ralls for Earth.com. “If the approach eventually works in living people, researchers could gain a way to track senescent cells over time without repeatedly removing tissue,” notes Ralls. “That could help scientists understand how these cells contribute to aging and whether treatments designed to eliminate them are working.” 

Boston Globe reporter Jon Chesto spotlights the fall fair event held as part of the MIT x GE Vernova Energy & Climate Alliance, during which, GE Vernova CEO Scott Strazik shared his enthusiasm for hiring MIT graduates. “Something we talk a lot about inside Vernova is the world is changing very quickly but we need to change quicker and even faster than that so we can lead in the world,” says Strazik. “Over the last year-plus on this campus, we see a lot of opportunities for us to get better through bringing you into the organization.”  

In an interview with Bloomberg, Prof. Justin Solomon discusses how mathematicians are responding to advances in AI in the wake of an AI platform solving the Navier-Stokes problem. “The really interesting part [of proof writing] is the modeling and figuring out which questions to ask and what insights they bring us to the universe, and the AI tools are not doing that,” says Solomon. “Writing the prompts is very much what we’ve been teaching our students to do for centuries now, and that skill, I think, persists.” 

“Groundbreaking computer scientist” Margaret Hamilton, who at MIT’s Instrumentation Lab “led the team that created the onboard flight software for NASA’s moon-bound Apollo program and later in life was celebrated as a powerful symbol of women in technology,” has died at 90, writes Associated Press reporter Jamie Stengle. Hamilton “was very much a mathematician and into the abstraction of how to turn thought into software,” notes her daughter, Lauren. 

In an interview with Smithsonian Magazine’s Diane Tedeschi, alumnus Mike Massimino SM ’88, PhD ’92 discusses working on NASA’s space shuttle as a mission specialist and repairing the Hubble Space Telescope. “Nothing can really prepare you, though, for the overwhelming beauty that you see—the emotion of it [space],” says Massimino. “When you see the beauty of Earth and the stars and the Moon and everything around you, it’s so inspiring. But as far as your job goes, you’re 100-percent ready.”  

MassLive’s Liesel Nygard spotlights the late Margaret Hamilton, “a pioneering computer scientist who helped establish the field of software engineering and led the MIT team that developed the onboard flight software for NASA’s Apollo moon missions.” Nygard writes, “Thanks to priority-driven ‘defensive’ software [in the lunar module’s computers] engineered by Hamilton and her team, the system automatically shut down unnecessary background tasks to focus on the landing, avoiding an emergency abort [of the Apollo 11 mission].”  

The Washington Post’s Juno Carmel details how the late Margaret Hamilton “developed early computer software that helped pave the way for the digital world as we know it today” and led the team at MIT’s Instrumentation Lab that programmed the alarm code that played a crucial role in the Apollo 11 mission. Hamilton “was the lead developer for the software that powered the Apollo guidance computer, which weighed 70 pounds but had only 72 kilobytes of memory, a fraction of today’s smartphones,” notes Carmel. 

Margaret Hamilton, “best known for her major contributions to the Apollo missions” at MIT’s Instrumentation Lab, “died on Sept. 30 at age 90, leaving behind a trailblazing legacy of problem-solving and entrepreneurship that paved the way for a new branch of computer science and engineering,” writes reporter Margherita Bassi for Smithsonian Magazine. Hamilton’s “coding foresight and prowess really made that mission a success,” says Smithsonian National Air and Space Museum’s Andrew Meade McGee. “She created the logical software basis for many of the critical systems we use to maintain our infrastructure today, and that silently power today’s modern aerospace world.” 

Margaret Hamilton, who developed the software that helped the Apollo 11 mission land safely on the moon and “whose historic accomplishments focused attention on the work of software engineers,” died on Sept. 30, reports Bryan Marquard for The Boston Globe. “She gave me, from the get-go, this notion that I could do anything, that there were absolutely no gender barriers,” says Hamilton’s daughter, Lauren. Hamilton’s life and work conveyed to her great-granddaughters “that very clear idea that they could have whatever future they wanted to create for themselves.” 

Prof. Virginia Vassilevska Williams speaks to the Scientific American’s Peter Hall and Joseph Howlett about AI assisting in faster matrix multiplications. “Matrix multiplication is needed all over the place,” says Williams. “But also, its complexity is one of the great mysteries in computer science. In a maybe diabolical sense, the advance on matrix multiplication can be seen as an attempt by the LLMs to speed themselves up.”