The image shows the underlying time-frequency characteristics of speech that are exploited by automatic recognition systems.

Artificial Intelligence Technology and Systems

For over 50 years, the group has applied its deep technical expertise in artificial intelligence, machine learning, and signal processing to discover, create, and deliver some of the nation’s most impactful technologies to national security. Examples include state-of-the-art automatic recognition of language, speech/text, and speakers/authors from speech, text, image, and video sources and enhancement of speech to improve its quality. The group serves as the nation’s go-to technical resource in human language technologies and natural language processing and is recognized at the highest levels of the government. Our rapidly growing cyber-AI efforts are leading the development of U.S. Cyber Command’s suite of analytics. These efforts include encrypted cyber-network-traffic situational-analysis technology, all-source nation-state influence tracking, and reinforcement learning–based AI partners for cyber offense. In the counter-AI area, the group is driving initiatives within the Intelligence Community to evaluate and generate core technologies. Recently, the group’s scope expanded to include influence operations, human network analysis, and AI assurance. Our group is widely recognized for its strong publications in journals and conferences and has produced 13 IEEE Fellows. We emphasize AI and machine learning applied to multimedia and cyber domains, technology transition to government in sensitive environments, and technology evaluation with operationally relevant metrics and datasets to significantly advance the nation’s intelligence, warfighting, and law-enforcement capabilities.

Advancing Our Research


29 - 30
MIT Lincoln Laboratory, Lexington, Massachusetts


Send us an email to inquire about our career opportunities and to learn more about projects we're working on.

Featured Publications

Poisoning network flow classifiers [e-print]

Jun 2
arXiv:2306.01655v1 [cs.CR]

Improving long-text authorship verification via model selection and data tuning

May 5
Proc. 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, LaTeCH-CLfL2023, 5 May 2023, pp. 28-37.

A generative approach to condition-aware score calibration for speaker verification

Feb 8
IEEE/ACM Trans. Audio, Speech, Language Process., Vol. 31, 2023, pp. 891-901.

Our Staff

View the biographies of members of the Artificial Intelligence Technology and Systems Group.