Our work lies at the intersection of control theory, machine learning, and formal methods. Here are some of the areas we're currently working on.
Certified learning for control
Before deploying deep learning-based methods for control, we need to understand the safety of these controllers. We develop tools to make learned controllers safer and generate data-driven proofs (certificates) of correctness.
Automated testing, verification & design optimization
As autonomous systems becomes more complex, they can start to fail in unexpected ways. We develop tools that help engineers discover, understand, and mitigate these failure modes.