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)
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)
Lydia Tapia
Websitehttps://afaust.info

Aleksandra Faust is a Serbian-American computer scientist who is the Chief AI officer at Genesis Molecular AI. Previously, she served as a Research Director at Google DeepMind,[1] and a Principal Investigator at Sandia National Laboratories.

Education

Faust received her Bachelor of Science in Mathematics and Computer Science from the University of Belgrade and her 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.[3]

Career

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.[4]

In 2017, Faust joined Google Brain,[5] eventually rising to Director of Research at Google DeepMind, where she led scalable autonomy and reinforcement learning research.[1] In 2020, she received the IEEE Early Career Award in Robotics and Automation.[6]

In June 2025, Faust was appointed Chief AI Officer of Genesis Molecular AI (formerly Genesis Therapeutics).[7] 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.[8]

Automated Reinforcement Learning (AutoRL)

Faust co-authored the paper that founded Automated Reinforcement Learning (AutoRL), a term her research is credited with coining.[9] AutoRL automates the design of the learning agents themselves. She co-authored the field's first survey,[9] and served as the Program Chair for the AutoML conference in 2023.[10]

Robotics and Motion Planning

In robotics, Faust bridges the gap between sensing, motion planning, and control using machine learning.[5] She created "PRM-RL," a method that combines sampling-based planning with reinforcement learning to enable long-range autonomous navigation,[11] winning the Best Paper in Service Robotics award at ICRA 2018.[12]

Faust was also an early advocate for generalist robot models capable of navigating diverse physical spaces without retraining.[13] She established the theoretical foundations for this generalization[14] as well as self-supervised methods for a learning-based robotics stack without computationally expensive methods.[5] 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)[15] and an IEEE Micro Top Picks Honorable Mention (2023).[16] Her contributions to the field were recognized with the IEEE Early Career Award in Robotics and Automation in 2020.[6]

Generative AI and Autonomous Agents

Faust led the development of Web Agents, recognized as the first fully autonomous, open-ended task agents on the web.[17] This technology was integrated into Google Assistant. To measure industry progress, Faust co-authored "Levels of AGI," a framework operationalizing the path to artificial general intelligence (AGI).[18] The framework has been discussed in media outlets including Bloomberg News,[19] The Economist,[20] and Forbes.[21]

Awards and honors

  • Fellow of the IEEE, 2026 "for contributions to technical leadership in scalable learning-based autonomy and foundation models"[22]
  • 50 Women in Robotics you need to know about, Women in Robotics (2023)[23]
  • Best Paper of IEEE Computer Architecture Letters runner-up (2020)[15]
  • IEEE Early Career Award in Robotics and Automation (2020)[6]
  • ICRA Best Paper in Service Robotics (2018)[12]

Speaking engagements

Faust was the 2025 keynote speaker at the IAEA's Emerging Technologies Workshop[24] and a plenary panel at World Summit AI.[25] She has served as a panelist for the National Academy of Sciences[26] and addressed 15,000 attendees as a plenary speaker at the Society of Women Engineers WE17 conference.

References

  1. "Aleksandra Faust, Ph.D". Genesis Molecular AI.
  2. "Sandia robotics scientist wins prestigious UNM dissertation award". Sandia Lab News. Archived from the original on 2025-12-12. Retrieved 2025-11-30.
  3. "The People of Tapia Lab". Tapia Lab. Archived from the original on 2026-01-16. Retrieved 2025-11-30.
  4. "Meet the People Who Train the Robots (to Do Their Own Jobs)". New York Times. Archived from the original on 2025-09-10. Retrieved 2025-11-30.
  5. "This Google Research Scientist Helps Robots Make Good Decisions". PC Magazine. Archived from the original on 2025-12-12. Retrieved 2025-11-30.
  6. "IEEE Early Career Award in Robotics and Automation". IEEE Robotics & Automation Society. Archived from the original on 2024-05-21. Retrieved 2025-11-30.
  7. "Genesis Therapeutics Appoints Aleksandra Faust as Chief Artificial Intelligence Officer". Business Wire.
  8. "Genesis says its new AI model bests AlphaFold 3, seeing synthetic physics data as key". Endpoints News. Archived from the original on 2025-11-19. Retrieved 2025-11-30.
  9. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems". Journal of Artificial Intelligence Research. arXiv:2201.03916. doi:10.1613/jair.1.13596. Archived from the original on 2022-06-16. Retrieved 2025-11-30.
  10. "AutoML Organizers". AutoML.
  11. "Google lays out framework for autonomous errand-running robots". VentureBeat. Archived from the original on 2025-01-15. Retrieved 2025-11-30.
  12. "IEEE ICRA Best Paper Award in Field and Service Robotics". IEEE Robotics & Automation Society. Archived from the original on 2024-04-23. Retrieved 2025-11-30.
  13. "Robot Navigation: From Abilities to Capabilities". IROS 2018 Workshop Machine Learning in Robot Motion Planning. Archived from the original on 2025-12-04. Retrieved 2025-11-30.
  14. 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.
  15. "Best Paper Awards Archive 2020 Runners Up". IEEE Computer Society. Archived from the original on 2026-01-17. Retrieved 2025-11-30.
  16. "Special Issue on Top Picks From the 2022 Computer Architecture Conferences". IEEE Xplore. Archived from the original on 2025-12-12. Retrieved 2025-11-30.
  17. "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. Archived from the original on 2024-06-20. Retrieved 2025-11-30.
  18. "Levels of AGI for operationalizing progress on the path to AGI". ACM Digital Library.
  19. "AI Companies Are Obsessed with AGI. No One Can Agree What Exactly It Is". Bloomberg.
  20. "How to define artificial general intelligence". The Economist. Archived from the original on 2025-07-24. Retrieved 2025-11-30.
  21. "Figuring Out What Artificial General Intelligence Consists Of Is Enormously Vital And Mindfully On The Minds Of AI Researchers At Google DeepMind". Forbes. Archived from the original on 2025-11-26. Retrieved 2025-11-30.
  22. "IEEE Fellow Class of 2026". IEEE. 2025. Archived from the original on 2025-12-13. Retrieved 2025-12-13.
  23. "50 Women in Robotics you need to know about 2023". Women in Robotics.
  24. "Emerging Technologies Workshop". IAEA. Archived from the original on 2026-01-05. Retrieved 2025-11-30.
  25. "At World Summit AI, cautious tone of researchers drowned out by cutthroat adoption race". Betakit.
  26. "SSB/ASEB Joint Meeting Spring 2023, June 6-9, 2023, DC/online". SpacePolicyOnline.com. Archived from the original on 2025-12-11. Retrieved 2025-11-30.