At SCALE Lab, we develop safe and trustworthy autonomous systems across multiple layers—from perception and learning to decision-making, planning, control, and multi-robot coordination.
We combine learning-based methods with control-theoretic guarantees so robots can adapt to uncertainty while maintaining safety. Our work connects rigorous theory with practical robotic systems.
Explore our research| Aug. 2026 | OU | I joined the Department of Mechanical Engineering at Oakland University as an Assistant Professor. |
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| Jul. 2026 | T-ASE | “AMBUSH: Collaborative Capture in Complex Environments with Neural Acceleration” accepted to IEEE Transactions on Automation Science and Engineering (T-ASE). |
| Jun. 2025 | IROS | “Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions” accepted to IROS 2025. |
| Feb. 2025 | L4DC | “Multi-Constraint Safe Reinforcement Learning via Closed-Form Solution for Log-Sum-Exp Approximation of Control Barrier Functions” accepted to L4DC 2025. |
| Mar. 2024 | ICRA | “Sensor-based Multi-Robot Search and Coverage with Spatial Separation in Unstructured Environments” presented at ICRA 2024. |