Giulia De Pasquale
Papers
1
Total Citations
2
H-Index
1
About
Giulia De Pasquale is a rising researcher at the forefront of safe and adaptive decision-making in complex, time-varying systems. Her work primarily focuses on the intersection of optimization, control theory, and machine learning, with a particular emphasis on ensuring safety in sequential decision-making problems such as robotics and process control. Her most notable contribution, detailed in her highly cited 2024 paper "Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel," addresses the critical challenge of optimizing performance while maintaining safety constraints in dynamic environments. By leveraging Gaussian processes with spatio-temporal kernels, De Pasquale has developed a novel framework that enables systems to adapt to changing conditions without compromising safety—a fundamental requirement for real-world autonomous systems. This work, already garnering 2 citations shortly after publication, demonstrates her ability to tackle pressing problems in the field. Her research promises to advance the deployment of intelligent systems in safety-critical applications, marking her as a promising voice in modern control and optimization.
Research Focus
Key Achievements
Top Papers
- 1