Casa Góes Lab

Controlling Autonomous Systems

with Assurances

Casa Góes Lab Mission

We develop first principle methods to analyze and design complex systems with strong resilience, security and safety guarantees.

To achieve these goals, we leverage control and formal methods techniques for modeling, analysis, and design of complex systems. Specifically, our group combines discrete event and hybrid systems, supervisory control, and temporal logics. Our applications domains include critical infrastructure control systems (manufacturing, water, and transportation), robotics, software systems, and human behavioral systems.

Lab News

May 2026
New paper on online fault-tolerant control in collaboration the CAIS lab is now available on IEEE TASE.
May 2026
Congrats to all 2026 Casa Góes graduates: Aafiya, Chenghui, Daniel, Gal, Matthew, Muhammad, Souvik, and Tristan. We had a celebration for the the graduates and the end of the semester.
2026 Graduation
Mar 2026
Three papers accepted at 2026 Workshop on Discrete Event Systems (WODES), two papers in collaboration with the CAIS lab. Special congratulations to Shih-Jie on the first paper acceptance!
Jan 2026
Congrats to Parastou on her paper acceptance to ACC26!
Dec 2025
Samuel Oliveira from UDESC presenting our collaborative work at CDC25: Paper
CDC25 Presentation
Oct 2025
Our CDC 2025 accepted paper on Cybersecurity Control Recovery is now available on Arxiv
Oct 2025
Rômulo will serve as an Associate Editor for the 18th Workshop on Discrete Event Systems (WODES 2026). Consider submitting your work to WODES
Sep 2025
New NSF project to Strengthen Data Center Resilience - PSU news
NSF Data Center Resilience Project
Aug 2025
Congrats Parastou on passing her comprehensive exam
Aug 2025
Congrats Parastou on being selected to the 2025 ICDS Junior Research Program
Jul 2025
One paper in collaboration accepted at CDC 2025. Work done by visiting student Samuel Oliveira from UDESC and in collaboration with PSU CAIS lab. Hope to see you in Rio

Recent Projects

Security of Critical Systems

Robustness of Discrete Systems

Learning from Demonstrations with Uncertainty