Introduces state-of-the-art methods in optimization and simulation, covering topics like statistical analysis, variance reduction, and simulation projects.
Covers spontaneous brain network activity, neural simulation, and validation, emphasizing the importance of in-vitro and in-vivo conditions for accurate network modeling.
Explores turning bumper cars into unbumping ones through collision avoidance algorithms and the challenges faced when implementing ellipsoid barrier functions.
Delves into using simulations for Human-Robot Interaction, learning from human expertise and preferences, user models, system models, simulation results, and assisting drone landings.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
