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Lincoln Laboratory researcher Noah Yared holds a DePLife prototype. His back is showing in the image with the prototype screen in front and visible.
In the Media

Testing new technologies that help law enforcement ‘see’ through walls

July 1, 2025
DHS Science and Technology
Three cadets are photographed; one is operating a steering wheel, controlling a robot off screen. The other two look on.

Military students innovate technology solutions for U.S. Special Operations Command

October 18, 2023
A person wearing a VR headset picks up a pour-over coffee filter.

Lincoln Laboratory partners with DARPA to test augmented-reality assistance systems

June 26, 2023
The logo for the Keylime cloud security software features the inside of a Lime (left); two desktop computers run the Forensic Video Exploitation and Analysis (FOVEA) software (right).
Awards

National FLC awards recognize two Lincoln Laboratory technology transfer efforts

January 17, 2023
Viet Tran

Intern Spotlight: Viet Tran gains hands-on engineering experience by helping robots work together

September 27, 2022
This image shows the login screen for the MCFLIE serious game. Three soldiers are silouetted against a sunset in the background. The foreground has the login page.

Serious games level up logistics learning for the U.S. Marine Corps

September 13, 2022
A person with a protective gown and gloves holds a large wafer of integrated circuits. the wafer is vividly multicolored.
Awards

MIT Lincoln Laboratory wins nine R&D 100 Awards

November 2, 2021
A snapshot of a software interface shows a video feed of a subway station, with green box around a man walking, and green arrows showing possible routes. At the bottom of hte interface is more, smaller screenshots of people in the video.
Awards

Lincoln Laboratory honored for transfer of security-enhancing technologies

August 31, 2021
This is a photo of a CubeSatpayload application of the TBIRD technology.
Awards

Lincoln Laboratory technologies named 2020 R&D 100 Award winners

October 13, 2020
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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