Aleksandra Faust
Aleksandra Faust is a Research Director at Google DeepMind. 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 co-founded Reinforcement Learning Research in Google Brain, founded Task and Motion Planning research in Robotics at Google, and 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.
Note: I am in Google DeepMind now, and this page out of date. See www.afaust.info for the up-to-date info.
Note: I am in Google DeepMind now, and this page out of date. See www.afaust.info for the up-to-date info.
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Authored Publications
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Google
Multimodal Web Navigation with Instruction-Finetuned Foundation Models
Hiroki Furuta
Ofir Nachum
Yutaka Matsuo
Shane Gu
Izzeddin Gur
International Conference on Learning Representations (ICLR) (2024)
Levels of AGI for Operationalizing Progress on the Path to AGI
Jascha Sohl-Dickstein
Allan Dafoe
Clement Farabet
Shane Legg
(2023)
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
Gabe Barth-Maron
Maximilian Lam
Sharad Chitlangia
Srivatsan Krishnan
Vijay Janapa Reddi
Zishen Wan
Transactions on Machine Learning Research (TMLR) 2022 (2022)
Less is More: Generating Grounded Navigation Instructions from Landmarks
Su Wang
Jordi Orbay
Vighnesh Birodkar
Izzeddin Gur
Peter Anderson
CVPR (2022) (to appear)
Metrics-only Training Neural Network for Switching among an Array of Feedback Controllers for Bicycle Model Navigation
Marco A. Carmona
Dejan Milutinovic
American Controls Conference (ACC) (2022) (to appear)
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Michael Lim
Andy Zeng
Brian Andrew Ichter
Maryam Bandari
Erwin Johan Coumans
Claire Tomlin
Stefan Schaal
International Conference on Robotics and Automation 2022, IEEE (to appear)
The Role of Compute in Autonomous Micro Aerial Vehicles: Optimizing for Flight Time and Energy Efficiency
Behzad Boroujerdian
Hasan Genc
Srivatsan Krishnan
Bardienus Pieter Duisterhof
Brian Plancher
Kayvan Mansoorshahi
Marcelino Almeida
Wenzhi Cui
Vijay Janapa Reddi
ACM Transactions on Computer Systems (TOCS) (2022) (to appear)
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
Sabrina Neuman
Brian Plancher
Bardienus Pieter Duisterhof
Srivatsan Krishnan
Colby R. Banbury
Mark Mazumder
Shvetank Prakash
Jason Jabbour
Guido C. H. E. de Croon
Vijay Janapa Reddi
IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) special session on Low Power Autonomous Systems (2022) (to appear)
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization
Sungryull Sohn
Hyunjae Woo
Jongwook Choi
lyubing Qiang
Izzeddin Gur
Honglak Lee
Uncertainty in Artificial Intelligence (UAI) (2022) (to appear)
Automatic Domain-Specific SoC Design for Autonomous Unmanned Aerial Vehicles
David Brooks
Gu-Yeon Wei
Kshitij Bhardwaj
Paul Whatmough
Srivatsan Krishnan
Vijay Janapa Reddi
Zishen Wan
55th IEEE/ACM International Symposium on Microarchitecture®, IEEE (2022) (to appear)
