Aleksandra Faust is a Staff Research Scientist at Google Brain Research, specializing in reinforcement learning and motion planning. Previously, Aleksandra led Task and Motion Planning research in Robotics at Google, machine learning for self-driving car planning and controls in Waymo, and was a researcher in Sandia National Laboratories. She earned a Ph.D. in Computer Science at the University of New Mexico (with distinction), and a Master's in Computer Science from the University of Illinois at Urbana-Champaign. Her research interests include learning for safe and scalable reinforcement learning, learning to learn, motion planning, decision-making, and robot behavior. Aleksandra won the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in Engineering, Mathematics, and Sciences in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, and was awarded Best Paper in Service Robotics at ICRA 2018 and Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML.