| Riga 36: | Riga 36: | ||
{{Panel|icon=graduation-cap|title=Publications|body= | {{Panel|icon=graduation-cap|title=Publications|body= | ||
==2026== | ==2026== | ||
| − | <paper authors="Giada Lops, Francesco De Paola, Vito Andrea Racanelli, Gioacchino Manfredi, Luca De Cicco, Saverio Mascolo" conference="IFAC World Congress" place="Busan, South Korea" date="August 2026"> | + | <paper authors="Giada Lops, Francesco De Paola, Vito Andrea Racanelli, Gioacchino Manfredi, Luca De Cicco, Saverio Mascolo" conference="IFAC World Congress" place="Busan, South Korea" date="August 2026" pdf="ifacwc2026.pdf"> |
Data-Driven Control of Type 2 Diabetes Progression via Personalized Physical Activity | Data-Driven Control of Type 2 Diabetes Progression via Personalized Physical Activity | ||
</paper> | </paper> | ||
|
Vito Andrea Racanelli |
| Eng, PhD, Researcher |
Researcher working in the fields of control theory, robotics, and autonomous systems. He is currently a visiting postdoctoral researcher at Carleton University (Canada) and collaborates with the C3Lab at the Polytechnic University of Bari, where he received his master's degree cum laude in Automation Engineering, followed by his Ph.D. in Industry 4.0 in January 2025.
His main research interests include Model Predictive Control (MPC), Reinforcement Learning, and data-driven control methods. From an applied perspective, his work focuses on the implementation of automation algorithms on drones (UAVs), mobile robotics platforms, and video streaming systems.
He is a member of ACM, IEEE, and IFAC. Over the course of his academic career, he has authored and co-authored several scientific papers presented at conferences and published in academic journals. His research primarily explores the integration between traditional control engineering techniques and artificial intelligence for industrial applications.|
|
Vito Andrea Racanelli |
| Eng, PhD, Researcher |
Researcher working in the fields of control theory, robotics, and autonomous systems. He is currently a visiting postdoctoral researcher at Carleton University (Canada) and collaborates with the C3Lab at the Polytechnic University of Bari, where he received his master's degree cum laude in Automation Engineering, followed by his Ph.D. in Industry 4.0 in January 2025.
His main research interests include Model Predictive Control (MPC), Reinforcement Learning, and data-driven control methods. From an applied perspective, his work focuses on the implementation of automation algorithms on drones (UAVs), mobile robotics platforms, and video streaming systems.
He is a member of ACM, IEEE, and IFAC. Over the course of his academic career, he has authored and co-authored several scientific papers presented at conferences and published in academic journals. His research primarily explores the integration between traditional control engineering techniques and artificial intelligence for industrial applications.