Lawrence Livermore National Laboratory

Technical Focus Areas: Machine Learning and Pattern Analysis
State-of-the-art machine learning approaches yield insight from complex, multi-modal datasets

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Our Capabilities

LLNL has expertise in both applying and extending a wide variety of state-of-the-art Machine Learning algorithms, including Neural Networks, Random Forests, and Dynamic Belief Networks. Our researchers have used these algorithms to tackle complex classification, clustering, change and anomaly detection problems on diverse data types including network sensor information, natural language, and imagery/video. With the experience and infrastructure to handle “big data” (batch and streaming), we leverage large, highly-complex and multi-modal datasets to produce actionable information.

Recent CASIS Workshop Proceedings

Contact a Subject Matter Expert

Barry Chen, (925) 423-9429,
Knowledge Systems and Informatics Group Leader

In the Spotlight —
Traffic Anomaly Detection

The Challenge

Vehicle traffic patterns observed in wide-area motion imagery can be complex; analysts seek indicators of anomalous trajectories indicative of potential threats.

The Results

Vehicle Tracks

Probabilistic topic modeling such as Latent Dirichlet Allocation yields valuable insight into anomalous traffic patterns, providing cues for analysts.

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