Aleksandra Faust is a Senior Staff Research Scientist, Autonomous Agents research lead, and Reinforcement Learning research team co-founder at Google Brain. Her research is centered around safe and scalable autonomous systems for social good, including reinforcement learning, planning, and control for robotics, autonomous driving, and digital assistants. Previously, Aleksandra founded and led Task and Motion Planning research in Robotics at Google, machine learning for self-driving car planning and controls in Waymo, and was a senior 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. Aleksandra won the IEEE RAS Early Career Award for Industry, the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico 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, VentureBeat, and ​was awarded Best Paper in Service Robotics at ICRA 2018, Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML 2019, Best Paper of IEEE Computer Architecture Letters in 2020, and IEEE Micro Top Picks 2023 Honorable Mention.

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Generalization in RL (2019 - present)

Reinforcement Learning On-Edge (2017 - present)

Past Projects

Learning to Learn for Reinforcement Learning (AutoRL) (2017 - 2023)

Safe reinforcement learning (2018 - 2020)

Self-supervision in planning (2019 - 2021)

Learning complex skills with hierarchical planning (2017 - 2022).