| Riga 36: | Riga 36: | ||
{{Panel|icon=graduation-cap|title=Publications|body= | {{Panel|icon=graduation-cap|title=Publications|body= | ||
==2026== | ==2026== | ||
| − | <paper authors="Giada Lops, Vito Andrea Racanelli, Luca De Cicco, Saverio Mascolo" conference=" | + | <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"> |
| − | + | Data-Driven Control of Type 2 Diabetes Progression via Personalized Physical Activity | |
</paper> | </paper> | ||
| − | <paper authors="Giada Lops | + | <paper authors="Giada Lops, Vito Andrea Racanelli, Luca De Cicco, Saverio Mascolo" conference="12th International Conference on Control, Decision and Information Technologies (CODIT)" place="Bari, Italy" date="July 2026" pdf="codit2026_diabetes.pdf"> |
| − | + | Risk-Aware Multi-Horizon Glucose Prediction for Type 2 Diabetes Using Temporal Attention and Conformal Prediction | |
</paper> | </paper> | ||
| − | <paper authors="Giada Lops, Taha Ramdan, Vito Andrea Racanelli, Luca De Cicco, Saverio Mascolo" conference="European Control Conference (ECC)" place="Reykjavík, Iceland" date="July 2026"> | + | <paper authors="Giada Lops, Taha Ramdan, Vito Andrea Racanelli, Luca De Cicco, Saverio Mascolo" conference="European Control Conference (ECC)" place="Reykjavík, Iceland" date="July 2026" pdf="0270.pdf"> |
Bridging Clinical Knowledge and Reinforcement Learning in Automated Insulin Delivery: An LLM-in-the-Loop Approach | Bridging Clinical Knowledge and Reinforcement Learning in Automated Insulin Delivery: An LLM-in-the-Loop Approach | ||
</paper> | </paper> | ||
| − | <paper authors="Federico Baldisseri, Giada Lops, Mohab Mahdy Helmy Atanasious, Danilo Menegatti, Valentina Becchetti, Francesco Delli Priscoli, Saverio Mascolo, Vito Andrea Racanelli, Andrea Wrona" conference="European Control Conference (ECC)" place="Reykjavík, Iceland" date="July 2026"> | + | <paper authors="Federico Baldisseri, Giada Lops, Mohab Mahdy Helmy Atanasious, Danilo Menegatti, Valentina Becchetti, Francesco Delli Priscoli, Saverio Mascolo, Vito Andrea Racanelli, Andrea Wrona" conference="European Control Conference (ECC)" place="Reykjavík, Iceland" date="July 2026" pdf="120.pdf"> |
Safe Deep Reinforcement Learning Control of Type 1 Diabetes | Safe Deep Reinforcement Learning Control of Type 1 Diabetes | ||
</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.