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Imagery generated from data collected by ATMS on NOAA-20
Awards

Lincoln Laboratory scientists among recipients of major NASA awards

November 6, 2018
A textile is woven with fibers that have semiconductor light-emitting diodes embedded into their core. Photo: Nicole Fandel

Fibers embedded with electronics are putting fabrics to work

October 19, 2018
a 3D printed circular structure made of triblock copolymer composite inks.

IEEE symposium brings together the best in radio science

October 1, 2018
Erik Limpaecher connects cables to solar panels on a roof
In the Media

Infrastructure for resilient power systems

September 21, 2018
Typhoon HIL
As the additive manufacturing lead at Lincoln Laboratory, Derek Straub consults with technical staff to design 3D-printed parts for various systems and technologies and oversees production on nine industrial machines. Photo: Nicole Fandel
Awards

Derek Straub is shaping the future of additive manufacturing

September 20, 2018
Captured image of the Large Magellanic Cloud and bright star R.
In the Media

NASA's TESS Shares First Science Image in Hunt to Find New Worlds

September 17, 2018
NASA
Shawn Reese, an engineering student, had a summer internship at M.I.T.’s Lincoln Laboratory. He is beginning work on a four-year degree in engineering at the University of Massachusetts at Lowell. Photo: Kayana Szymczak, The New York Times
In the Media

Teaming Up to Get Workers Ready for Technology of the Future

September 11, 2018
The New York Times
A U.S. Coast Guard responder offshore is able to access real-time information from NICS' onshore computers on a mobile device. Photo: Amna Greaves
Awards

Lincoln Laboratory honored for providing access to technology for disaster response

September 10, 2018
TbD-net solves the visual reasoning problem by breaking it down to a chain of subtasks. The answer to each subtask is shown in heat maps highlighting the objects of interest. This process allows analysts to see the network's “thought process.”

A new artificial intelligence system solves problems through human-like reasoning

September 4, 2018
From left to right are Melva James, Stephanie Carnell, Jonathan Su, and Neela Kaushik, the team developing Adaptable Interpretable Machine Learning (AIM) at Lincoln Laboratory. Photo: Glen Cooper

Replacing black box machine learning with interpretable models

September 4, 2018
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