Daniel Casini
Papers
5
Total Citations
209
H-Index
5
About
Daniel Casini is a leading researcher in real-time systems, with a focus on the intersection of robotics, safety-critical software, and deep learning. His major contributions center on providing rigorous timing guarantees for modern, complex systems. Casini pioneered the response-time analysis of ROS 2 processing chains, developing novel techniques that bound end-to-end latency under reservation-based scheduling and exploit starvation freedom to account for execution-time variance—work that has garnered over 120 citations. He has also been instrumental in making deep learning predictable for safety-critical applications, proposing a safe, secure, and predictable software architecture for deep neural networks (DNNs) and introducing timing isolation and improved scheduling for DNNs in real-time systems. Beyond these areas, Casini has advanced the theory of semi-partitioned scheduling with task splitting and load balancing for dynamic workloads. His research is highly cited (over 200 total citations) and directly addresses pressing challenges in autonomous driving, advanced robotics, and industrial control, making him a key figure in bridging the gap between high-performance AI and rigorous real-time guarantees.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5