Samira Ebrahimi Kahou

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About

Samira Ebrahimi Kahou is a leading researcher at the intersection of machine learning, robotics, and multi-agent systems, with a core focus on advancing trajectory prediction and reinforcement learning. Her most impactful work introduces **Latent Variable Sequential Set Transformers**, a novel framework that models the joint distribution of social, temporal, and contextual information for robust multi-agent motion prediction—a critical capability for safe autonomous navigation and human-robot interaction. This work, published in 2021, has already garnered 38 citations, reflecting its significance in enabling socially consistent, long-horizon forecasting. Dr. Kahou also co-authored the first comprehensive survey on **Transformers in Reinforcement Learning** (2023), synthesizing how transformer architectures are revolutionizing RL across domains like robotics and control. Her contributions extend to **AutoBots**, a latent variable sequential set transformer that further refines multi-agent trajectory prediction. Through her research, Dr. Kahou is shaping the future of autonomous systems, providing foundational tools for robots and vehicles to safely anticipate and coordinate with human behavior. Her work is essential reading for anyone interested in scalable, socially-aware AI for real-world deployment.