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Aleksandra Faust

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Aleksandra Faust
Faust speaking at the Chief AI Officer Summit in Santa Clara, California in 2025.
Born
Belgrade, Serbia
Alma materUniversity of New Mexico (PhD)
University of Illinois Urbana-Champaign (MS)
University of Belgrade (BS)
Known forScalable autonomy, Automated Reinforcement Learning (AutoRL), Levels of AGI framework, Web Agents, robotics and motion planning, Pearl foundation model
Scientific career
FieldsArtificial Intelligence, Robotics
InstitutionsGenesis Molecular AI, Google DeepMind, Google Brain, Waymo, Sandia National Laboratories
Thesis Reinforcement Learning and Planning for Preference Balancing Tasks  (2014)
Doctoral advisorLydia Tapia
Websitehttps://afaust.info

Aleksandra Faust is a Serbian-American computer scientist, AI researcher, and technology executive. She is the Chief AI Officer at Genesis Molecular AI, having previously served as a Research Director at Google DeepMind,[1] and a Principal Investigator at Sandia National Laboratories.[2]

Faust is recognized for establishing principles of AI-driven scalable autonomy, particularly in the field of Automated Reinforcement Learning (AutoRL).[3] Her research focuses on treating the entire system design pipeline as a learnable, sequential decision-making problem—an approach she has applied to scalable autonomy in robotics, generative AI, and drug discovery.[4] Contributions include the "Pearl" biomolecular foundation model,[5] the self-improvement training methods used in Google's Gemini models,[6] and the "Levels of AGI" framework.[7] In 2020, she received the IEEE Early Career Award in Robotics and Automation.[8]

Education

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Faust received her Bachelor of Science in Mathematics and Computer Science from the University of Belgrade.[2] She earned a Master of Science in Computer Science from the University of Illinois at Urbana-Champaign in 2004.[2] In 2014, Faust completed her Ph.D. in Computer Science at the University of New Mexico under the supervision of Lydia Tapia.[9] Her dissertation, "Reinforcement Learning and Planning for Preference Balancing Tasks," won the Tom L. Popejoy Dissertation Prize, the university's highest dissertation honor.[10]

Career

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Faust was a Senior R&D Engineer at Sandia National Laboratories (2006–2015).[2] She subsequently joined Waymo (Google's self-driving car project) in 2015, focusing on machine learning for motion planning.[11]

In 2017, Faust joined Google Brain,[12] eventually rising to Director of Research at Google DeepMind, where she led scalable autonomy and reinforcement learning research.[1]

In June 2025, Faust was appointed Chief AI Officer of Genesis Molecular AI ​​(formerly Genesis Therapeutics).[13] In October 2025, she and her team released the technical report for the "Pearl" foundation model for atomic placement in biomolecular structures, reportedly the first model that outperforms AlphaFold 3.[5]

Automated Reinforcement Learning (AutoRL)

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Faust co-authored the paper that founded Automated Reinforcement Learning (AutoRL), a term her research is credited with coining.[4] AutoRL automates the design of the learning agents themselves. She co-authored the field's first survey,[4] and served as the Program Chair for the AutoML conference in 2023.[14]

Sustainable Training Methodologies

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A central tenet of Faust's work is the reliance on accessible, imperfect data to overcome scarcity in high-stakes fields.[15] Her research in robotics, web agents, and drug discovery utilizes synthetic, simulated, and noisy data to propel progress where expert demonstrations are rare or nonexistent.[12][16][5]

Robotics and Motion Planning

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In robotics, Faust bridges the gap between sensing, motion planning, and control using machine learning.[12] She created "PRM-RL," a method that combines sampling-based planning with reinforcement learning to enable long-range autonomous navigation,[17] winning the Best Paper in Service Robotics award at ICRA 2018.[18]

Faust was also an early advocate for generalist robot models capable of navigating diverse physical spaces without retraining.[19] She established the theoretical foundations for this generalization[20] as well as self-supervised methods for a learning-based robotics stack without computationally expensive methods.[12] She later expanded this approach to hardware-software co-design, characterizing dependencies between sensors, compute, and machine learning models. This interdisciplinary work earned the Best of IEEE Computer Architecture Letters runner-up award (2020)[21] and an IEEE Micro Top Picks Honorable Mention (2023).[22] Her contributions to the field were recognized with the IEEE Early Career Award in Robotics and Automation in 2020.[8]

Generative AI and Autonomous Agents

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Faust led the development of Web Agents, recognized as the first fully autonomous, open-ended task agents on the web.[23] This technology was integrated into Google Assistant.[citation needed] To measure industry progress, Faust co-authored "Levels of AGI," a framework operationalizing the path to artificial general intelligence (AGI).[7] The framework has been discussed in media outlets including Bloomberg News,[24] The Economist,[25] and Forbes.[26]

Awards and honors

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  • IEEE Micro Top Picks Honorable Mention (2023)[22]
  • 50 Women in Robotics you need to know about, Women in Robotics (2023)[27]
  • Best Paper of IEEE Computer Architecture Letters runner-up (2020)[21]
  • IEEE Early Career Award in Robotics and Automation (2020)[8]
  • ICRA Best Paper in Service Robotics (2018)[18]
  • Distinguished Alumna, University of New Mexico School of Engineering (2018)[28]
  • Tom L. Popejoy Dissertation Prize Winner, University of New Mexico (2015)[10]

Speaking engagements

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Faust is a frequent speaker at international forums, including a 2025 keynote at the IAEA's Emerging Technologies Workshop[29] and a plenary panel at World Summit AI.[30] She has served as a panelist for the National Academy of Sciences[31] and addressed 15,000 attendees as a plenary speaker at the Society of Women Engineers WE17 conference.[32] Her academic speaking engagements include keynotes at premier robotics conferences such as IROS[33] and CoRL.

References

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  1. ^ a b "Aleksandra Faust, Ph.D". Genesis Molecular AI.
  2. ^ a b c d "Sandia robotics scientist wins prestigious UNM dissertation award". Sandia Lab News.
  3. ^ "Learning Navigation Behaviors End-to-End With AutoRL". IEEE Xplore.
  4. ^ a b c "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems". Journal of Artificial Intelligence Research. arXiv:2201.03916. doi:10.1613/jair.1.13596.
  5. ^ a b c "Genesis says its new AI model bests AlphaFold 3, seeing synthetic physics data as key". Endpoints News.
  6. ^ "DeepMind's SCoRe shows LLMs can use their internal knowledge to correct their mistakes". VentureBeat.
  7. ^ a b "Levels of AGI for operationalizing progress on the path to AGI". ACM Digital Library.
  8. ^ a b c "IEEE Early Career Award in Robotics and Automation". IEEE Robotics & Automation Society.
  9. ^ "The People of Tapia Lab". Tapia Lab.
  10. ^ a b "Faust receives Tom L. Popejoy Dissertation Prize". UNM Newsroom.
  11. ^ "Meet the People Who Train the Robots (to Do Their Own Jobs)". New York Times.
  12. ^ a b c d "This Google Research Scientist Helps Robots Make Good Decisions". PC Magazine.
  13. ^ "Genesis Therapeutics Appoints Aleksandra Faust as Chief Artificial Intelligence Officer". Business Wire.
  14. ^ "AutoML Organizers". AutoML.
  15. ^ "Google suggests all software could use a little robot AI". ZDNet.
  16. ^ "Forget Go, Google helps AI learn to book flights on the Web". ZDNet.
  17. ^ "Google lays out framework for autonomous errand-running robots". VentureBeat.
  18. ^ a b "IEEE ICRA Best Paper Award in Field and Service Robotics". IEEE Robotics & Automation Society.
  19. ^ "Robot Navigation: From Abilities to Capabilities". IROS 2018 Workshop Machine Learning in Robot Motion Planning.
  20. ^ Kew, J.; Ichter, B.; Bandari, M.; Lee, TW.E.; Faust, A. (2021). "Neural Collision Clearance Estimator for Batched Motion Planning". Algorithmic Foundations of Robotics XIV. WAFR 2020. Springer.
  21. ^ a b "Best Paper Awards Archive 2020 Runners Up". IEEE Computer Society.
  22. ^ a b "Special Issue on Top Picks From the 2022 Computer Architecture Conferences". IEEE Xplore.
  23. ^ "Google DeepMind and the University of Tokyo Researchers Introduce WebAgent: An LLM-Driven Agent that can Complete the Tasks on Real Websites Following Natural Language Instructions". MarkTechPost.
  24. ^ "AI Companies Are Obsessed with AGI. No One Can Agree What Exactly It Is". Bloomberg.
  25. ^ "How to define artificial general intelligence". The Economist.
  26. ^ "Figuring Out What Artificial General Intelligence Consists Of Is Enormously Vital And Mindfully On The Minds Of AI Researchers At Google DeepMind". Forbes.
  27. ^ "50 Women in Robotics you need to know about 2023". Women in Robotics.
  28. ^ "School of Engineering to honor 8 at Distinguished Alumni Award event on Oct. 25". UNM School of Engineering.
  29. ^ "Emerging Technologies Workshop". IAEA.
  30. ^ "At World Summit AI, cautious tone of researchers drowned out by cutthroat adoption race". Betakit.
  31. ^ "SSB/ASEB Joint Meeting Spring 2023, June 6-9, 2023, DC/online". SpacePolicyOnline.com.
  32. ^ "SWE Member Irena Jovanovska is Always Engineering … Always Connecting". All Together.
  33. ^ "IROS 2022 Keynote Speakers". IROS Kyoto 2022.
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