Two small businesses awarded DHS funding for machine learning technologies

Physical Sciences Inc. (PSI), based in Andover, Mass., and Alakai Defense Systems, Inc. (Alakai), based in Largo, Fla., recently announced that they have secured funding from the Department of Homeland Security (DHS) Small Business Innovation Research (SBIR) Program.

The businesses, which received approximately $1 million in SBIR Phase II funding, are slated to develop non-contact machine learning training and classification technologies. Integrated machine learning platforms possess the potential to significantly reduce time, redundancy, and cost while improving accuracy in detecting threats such as explosives, chemical agents, and narcotics.

“S&T embraces the significant advances in artificial intelligence and machine learning capabilities and their ability to enhance threat detection,” said Kathryn Coulter Mitchell, DHS senior official Performing the Duties of the Under Secretary for Science and Technology. “The SBIR Program provides the opportunity for S&T to partner with innovative small businesses and develop machine learning tools critical to addressing threat detection needs. I am looking forward to seeing the technologies that will be developed by these SBIR efforts.”

Thoi Nguyen, DHS S&T program manager for the Next Generation Explosive Trace Detection (NGETD) Program, said the impetus for developing machine-learning modules stems from the Transportation Security Administration’s (TSA) operational needs for threat signature fusion, the ability to learn, detect and classify new threats without being explicitly programmed.

“With experienced industrial partners like Alakai and PSI, and our strong collaboration with TSA, we hope these efforts will contribute to wider applications of machine learning across the Homeland Security mission space,” Nguyen said.