Welcome!
Ulices Santa Cruz Leal
As a Solutions Engineer at Applied Dynamics International, I leverage my expertise in aerospace technology, control systems, and real-time systems to deliver cutting-edge System Integration Laboratory (SIL) solutions.
I hold a Ph.D. in Electrical Engineering and Computer Science from UC Irvine, where I focused on improving the safety and reliability of AI-driven, perception-based autonomous systems. My research, under the guidance of Prof. Yasser Shoukry in the Resilient Cyber-physical systems group, specialized in applications like autonomous aircraft landing and self-driving vehicles. By integrating machine learning, formal methods, control theory, and machine vision, I created robust frameworks for these critical systems.
Prior to my doctoral studies, I gained valuable industry experience at Honeywell Aerospace. I also hold a Master’s degree in Astronautics and Space Engineering from Cranfield University and a Bachelor's degree in Mechatronics Engineering from Universidad Autónoma de Baja California
R&D Projects
• (CDC-23) Certified Vision-based State Estimation for Auto Landing Systems using Mixed-Monotone Reachability Analysis.
• (NFM-22) Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing.
• (CDC-22) Convex Optimization Approach for Repairing Unsafe Two-Level Lattice Neural Network Controllers.
• (CDC-21) Provably Safe Model-Based Meta Reinforcement Learning.
• (GNSS-19) Orbit Modeling for Simultaneous Tracking and Navigation using LEO Satellite Signals.
• (IAF-18) Networked and Distributed Cooperative Attitude Control of Fractionated Small Satellites.