CV
Experience
Investigating how joint attention can support zero-shot coordination in cooperative multi-agent reinforcement learning.
Developed an eye contact detection approach during naturalistic caregiver–infant interactions and assessed its accuracy. Part of VIPPSTAR.
Developed ML models to optimize anesthesia control systems by predicting patient's level of consciousness. Part of ACTIVA Project.
Research Papers
Grounded Joint-Attention Other-Play for Zero-Shot Coordination
G. Benintendi, C. Ruhdorfer, F. Kögel, A. Bulling
Under review at AAAI 2027
DreamTeam: Training Cooperative Agents in Imagined Worlds
C. Ruhdorfer, A. H. Güzel, S. K. Majumdar, G. Benintendi, M. Bortoletto, I. Bogunovic, J. Parker-Holder, A. Bulling
Under review at NeurIPS 2026
Education
Double Degree Program in Robotic Systems at Sorbonne University.
Projects and Competitions
Benchmarked imitation learning algorithms (BC, DAgger, DAgger with replay buffer) on MuJoCo locomotion tasks.
Real-time platform using CV and automatic task allocation to optimize cleaning operations in stadium-scale environments.
Technical Skills
- Robotics, Control & RL: control systems, real-time control, RL foundations, imitation learning (BC, DAgger)
- ML: predictive modeling, deep learning fundamentals, feature engineering, time-series processing, model validation
- Programming & Tools: Python (NumPy, OpenCV, PyTorch), JAX, MATLAB, C, C++, Git/GitHub