Robust Deep Learning for Threat Detection

Deep learning for data that moves

Scientific breakthroughs increasingly depend on our ability to understand data that moves: climate systems that evolve over decades, turbulent fluids that change second by second, and human social dynamics that shift across communities and time. But most mainstream AI is built for static tasks like labeling images or classifying text. When you apply those same tools to spatiotemporal science, they often look impressive in the short term but break down when conditions change, when forecasts must extend further, or when small errors compound into big failures.

This project aims to change that by building deep learning models designed from the ground up for “threat detection” in complex dynamical data: discovering the persistent patterns, signals, and structures that weave through space and time, and using them to make forecasts that remain stable, reliable, and useful in the real world.

Our approach bridges today’s best ideas from computer vision and language models with the rigor of dynamical systems and control theory. Instead of treating time as an afterthought, we will develop AI that understands how systems evolve, how information propagates, and how uncertainty accumulates. Just as importantly, we will make these models more trustworthy by baking in scientific and domain knowledge—so they don’t merely fit the data, but respect the underlying rules of the world.

Project Team

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