معرفی
Silvia Tulli serves as an Assistant Professor at Sorbonne University within the Faculty of Science and Engineering, affiliated with the Institute of Intelligent Systems and Robotics (ISIR) and the ACID research team. Her work bridges theoretical AI development with practical human-centered applications, focusing on creating transparent and collaborative intelligent systems.
Her research program centers on developing algorithms for human-AI symbiosis, with core expertise in:
- Reverse reinforcement learning from contrastive examples
- Human modeling for AI alignment
- Explainability through social cues in embodied agents
- Learning from explanations and demonstrations
- Human-robot interaction frameworks
- AI value alignment mechanisms
Analysis of her recent publications reveals a cohesive trajectory toward making AI systems interpretable and cooperative. The 2021 review establishes foundational principles for explainable embodied agents using social signaling, while the 2020 pilot study pioneers hybrid learning approaches combining explanations with demonstrations—both advancing the field of transparent AI-human collaboration.
No scientific awards were documented in the source materials.
Current advising activities and grant funding details remain unspecified in available records, though her active research output indicates ongoing project leadership within the ACID team.
She operates within ISIR's ACID research team at the Pyramide building (4 Place Jussieu, Paris), where her group develops algorithmic frameworks for socially aware AI systems through interdisciplinary collaboration in robotics and machine learning.
