Justin Ruths
University of Texas at Dallas, TX·Engineering·Early-career
Self-driving cars, power grids and water systems all depend on software that senses the world and controls physical machines. How can engineers guarantee that such systems stay safe?
- Fields
Engineering
Computer Science
Mathematics & Statistics
- Topics
cyber-physical systems
autonomous vehicles
autonomy stack
- Grants
- 2 known grants, through Jun 2029
What they work on
- Represent each autonomy-stack module (mapping, perception, planning, control) and the vehicle's physical dynamics as hybrid zonotopes, sets made of unions of convex polytopes, to create one common form.
- Bound all possible behaviors of the autonomy stack and closed-loop system with hybrid zonotopes for offline verification and falsification, and improve scalability versus accuracy.
- Mathematically link system uncertainty and detector sensitivity in model-based attack detectors, defining a vulnerability index from system and uncertainty characteristics.
- Develop new attack strategies and defenses for switched dynamical systems, which switch between operating modes, by having the team act as both attacker and defender.
More about the research
The question matters in ordinary operation and when attackers tamper with their computers. Both of this lab's projects use mathematical models to bound worst-case behavior of these cyber-physical systems, where software and physical dynamics interact.
Why it matters
Safer autonomous vehicles, farm robots and drones could result from tools that formally bound worst-case behavior of their software. Better attack detectors could protect manufacturing, chemical plants, and power, water, fuel and transportation systems from cyber-attacks. Such attacks could otherwise damage equipment, compromise safety or harm the environment.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Qi Chen
University of California-Irvine, CA·Computer Science·Early-career
Develop system-to-AI and AI-to-system mapping methods that generate physical-layer attacks on autonomous vehicle AI while keeping them physically realizable and semantically equivalent across diverse real system designs.
Sayan Mitra
University of Illinois Urbana-Champaign, IL·Computer Science
Develop refinement algorithms that synthesize perception contracts, machine-checkable bounds on how wrong an AI perception module can be, together with the operating design domains in which they hold, for pipelines that fuse camera and inertial sensor data.
Bruno Sinopoli
Arizona State University Campus Immersion, AZ·Engineering
Establish closed-loop stability guarantees for REAP solvers, optimization methods that return a safe, feasible answer even when stopped early, and develop ways to implement them on discrete-time digital hardware.
The school
University of Texas at Dallas: 16 labs on PI Pages, $182M a year on research, 239 research doctorates a year.
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