Aleksandra Faust is a Serbian-American computer scientist who serves as Chief AI Officer at Genesis Molecular AI. She is recognized for contributions to reinforcement learning, robotics motion planning, and foundation models for biomolecular structures. Faust previously held research leadership positions at Google DeepMind and Sandia National Laboratories, and she has been honored with the IEEE Early Career Award in Robotics and Automation.
Faust's research spans artificial intelligence, machine learning, and deep learning, with a focus on scalable autonomy and automated design of learning agents. She co-founded the field of Automated Reinforcement Learning (AutoRL) and has advanced generalist robot models capable of operating across diverse physical environments. Her recent work includes leading the development of Web Agents and co-authoring the 'Levels of AGI' framework.
Education
Faust earned a Bachelor of Science in Mathematics and Computer Science from the University of Belgrade. She then completed a Master of Science in Computer Science at the University of Illinois at Urbana-Champaign in 2004. In 2014, she received her Ph.D. in Computer Science from the University of New Mexico, where her doctoral research was supervised by Lydia Tapia. Her graduate studies focused on algorithmic robotics and motion planning, laying groundwork for her later work in learning-based approaches.
Career at Sandia National Laboratories
From 2006 to 2015, Faust worked as a Senior R&D Engineer at Sandia National Laboratories, a United States Department of Energy facility. During this period, she applied computational methods to complex robotic systems and autonomous navigation challenges. Her work at Sandia involved integrating sensing, planning, and control, which informed her subsequent research in reinforcement learning and motion planning. The experience also shaped her interest in bridging theoretical algorithms with practical hardware constraints.
Waymo and Google Brain
In 2015, Faust joined Waymo, the self-driving car project under Google, where she focused on applying machine learning to motion planning for autonomous vehicles. Her work at Waymo addressed real-time decision-making in dynamic traffic environments, contributing to the development of safer navigation systems.
In 2017, Faust moved to Google Brain, a deep learning research group. She rose to become Director of Research at Google DeepMind after the group's integration with DeepMind. In this role, she led research on scalable autonomy and reinforcement learning, overseeing projects that aimed to make learning algorithms more efficient and applicable to large-scale systems. Her leadership contributed to advances in neural network training methods and agent-based learning.
Automated Reinforcement Learning (AutoRL)
Faust co-authored the paper that established Automated Reinforcement Learning (AutoRL), a term her research is credited with coining. AutoRL automates the design of learning agents themselves, including choices of architecture, reward functions, and hyperparameters, reducing the need for manual tuning. She also co-authored the field's first survey, which systematized approaches and open problems in this area.
Faust served as Program Chair for the AutoML conference in 2023, helping to organize the academic community around automated machine learning. Her work in AutoRL has influenced how researchers approach hyperparameter optimization and architecture search in reinforcement learning, making these methods more accessible to non-experts.
Robotics and Motion Planning
In robotics, Faust developed methods that bridge sensing, motion planning, and control using machine learning. She created PRM-RL, a technique combining sampling-based planning with reinforcement learning to enable long-range autonomous navigation. This work won the Best Paper in Service Robotics award at ICRA 2018, a leading robotics conference.
Faust was an early advocate for generalist robot models that can navigate diverse physical spaces without retraining for each environment. She established theoretical foundations for this generalization, showing how learned policies could transfer across different robot platforms and settings. She also developed self-supervised methods for a learning-based robotics stack, avoiding computationally expensive training procedures.
Later, Faust 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 in 2020 and an IEEE Micro Top Picks Honorable Mention in 2023. Her contributions to robotics were recognized with the IEEE Early Career Award in Robotics and Automation in 2020.
Generative AI and Autonomous Agents
Faust led the development of Web Agents, recognized as the first fully autonomous, open-ended task agents operating on the web. These agents can perform multi-step tasks such as research, booking, or data collection without human intervention. The technology was integrated into Google Assistant, enabling users to delegate complex online activities.
To measure industry progress toward advanced AI, Faust co-authored 'Levels of AGI,' a framework that operationalizes the path to artificial general intelligence. The framework defines stages of capability, from narrow AI to full AGI, and has been discussed in media outlets including Bloomberg News, The Economist, and Forbes. It provides a common vocabulary for researchers and policymakers evaluating AI systems.
Chief AI Officer at Genesis Molecular AI
In June 2025, Faust was appointed Chief AI Officer of Genesis Molecular AI, formerly Genesis Therapeutics. The company focuses on applying AI to drug discovery and molecular design. In October 2025, Faust and her team released the technical report for the 'Pearl' foundation model, which handles atomic placement in biomolecular structures. Pearl is reportedly the first model to outperform AlphaFold 3 on this task, representing a significant advance in computational biology.
This work applies large language model techniques and transformer architectures to molecular problems, building on Faust's expertise in scalable learning systems. The model aims to improve accuracy in predicting molecular conformations, which is critical for drug development and protein engineering.
Awards and Honors
Faust has received numerous awards throughout her career. She was named a Fellow of the IEEE in 2026, with the citation recognizing her contributions to technical leadership in scalable learning-based autonomy and foundation models. In 2023, she was included in the '50 Women in Robotics you need to know about' list by Women in Robotics.
Her other honors include the Best Paper of IEEE Computer Architecture Letters runner-up (2020), the IEEE Early Career Award in Robotics and Automation (2020), and the ICRA Best Paper in Service Robotics (2018). These awards reflect her impact across robotics, machine learning, and computer architecture.
Speaking Engagements
Faust is a sought-after speaker on AI and robotics topics. She delivered the 2025 keynote at the IAEA's Emerging Technologies Workshop and participated in a plenary panel at World Summit AI. She has served as a panelist for the National Academy of Sciences, contributing to policy discussions on AI safety and deployment. Faust also addressed 15,000 attendees as a plenary speaker at the Society of Women Engineers WE17 conference, advocating for diversity in engineering fields.
Her public engagements emphasize the practical applications of AI research and the importance of responsible development. Faust frequently discusses the implications of autonomous systems and foundation models for industry and society, drawing on her experience across national laboratories, tech companies, and startups.
References
Faust's work is documented in academic publications and technical reports. Her research papers on AutoRL, PRM-RL, and Web Agents have been cited widely in the machine learning and robotics communities. The 'Levels of AGI' framework has become a reference point in discussions of AI progress. Her recent work on the Pearl foundation model is detailed in the October 2025 technical report from Genesis Molecular AI.