CLiC Lab:  Control and Learning in Cyber-Physical Systems LABoratory

• 07/2026 | Four papers entitled "Invariance is Compositional for Continuous-time Systems: From Sleekness to Lebesgue Density", "Algebraic Characterization of Time-Varying Set Invariance", "Certified Reachable Sets for Nonlinear Reaction-Diffusion Systems under Uncertainty" and "H2 Stabilization of the 2-D and 3-D Heat Equation via Modal Decomposition" have been accepted for presentation at IEEE CDC. Congratulations to all co-authors!

• 06/2026 | Our paper entitled "Certified Reachable Sets for Nonlinear Reaction-Diffusion Systems under Uncertainty" got accepted at IEEE Control Systems Letters (L-CSS)!

• 06/2026 | Congratulations to Youssef Ait Si for being selected as a Top 5 Finalist of the 2026 IEEE CSS Hybrid Systems TC Outstanding Student Paper Prize for his paper entitled "Maximally Resilient Controllers under Temporal Logic Specifications"!

• 06/2026 | Our paper entitled "User-friendly Implementation of Recurring Switching Rules via Temporal Logic: Industrial Power System Case Study" got accepted at Nonlinear Analysis: Hybrid Systems (NAHS)!

• 05/2026 | Our paper entitled "Safe Deep Reinforcement Learning for Energy-Efficient HVAC Control in Multi-Zone Residential Buildings" has been accepted for presentation at IEEE Conference on Control Applications, CCTA, 2026!!

• 03/2026 | Our paper entitled "A Trajectory-Based Approach to Controlled Invariance and Recursively Feasible MPC," has been accepted for presentation at the European Control Conference, ECC, 2026I!

• 01/2026 | Our paper entitled "ART: Attention-Regularized Transformers for Multi-Modal Robustness,” has been accepted for presentation at EACLI!

• 11/2025 | Our paper entitled "Just Few States are Enough: Randomized Sparse Feedback for Stability of Dynamical Systems" has been accepted for presentation at AAAI! Congratulations to the team!

• 10/2025 | Our paper entitled "Temporal Logic Resilience for Dynamical Systems" got accepted as a full paper at the IEEE Transactions on Automatic Control!

• 07/2025 | Four papers entitled "Maximally Resilient Controllers under Temporal Logic Specifications", "Computation of Feasible Assume-Guarantee Contracts: A Resilience-based Approach", "Nagumo-Type Characterization of Forward Invariance for Constrained Systems" and "An optimal-control framework for reaction diffusion systems with application to synthetic developmental biology" have been accepted for presentation at IEEE CDC. Congratulations to all co-authors!

• 07/2025 | Our paper entitled "Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach" has been accepted to ACL! Excellent achievement!

• 06/2025 | Congratulations to Youssef AitSi for being selected as a Top 3 Finalist for the Best Student Award at the European Control Conference ECC for his paper entitled "Symbolic Control: Unveiling Free Robustness Margins"!

• 04/2025 | Our paper entitled "Controller Synthesis of Collaborative Signal Temporal Logic Tasks for Multi-Agent Systems via Assume-Guarantee Contracts" got accepted as a full paper at the IEEE Transactions on Automatic Control!

• 09/2024 | Our paper entitled "Sufficient Conditions for Robust Safety in Differential Inclusions Using Barrier Functions" got accepted in Automatica!

CLiC Lab (Control and Learning in Cyber-Physical Systems Laboratory) explores how intelligent systems can learn, adapt, and remain safe in the real world. Our work brings together tools from control theory, formal methods and machine learning to design technologies that are both smart and reliable.

We study cyber-physical systems that connect software, data, and the physical world, such as autonomous vehicles, robots, and smart infrastructure. Our goal is to make these systems trustworthy and resilient, even in uncertain or changing environments.

At CLiC Lab, we combine rigorous theory with practical experimentation. Our research focuses on three main questions:

• How can learning-based systems be made safe and predictable?

• How can control theory guide the design of intelligent algorithms?

• How can we verify and certify the behavior of AI-driven systems?

By uniting insights from engineering, computer science, and applied mathematics, we aim to build the foundations for the next generation of safe and dependable autonomous technologies.