Aleksandra Faust @AleksandraFaust
Foundation Models & AI Agents for the Real World | AI Innovation from Inception to Societal Impact | @genesismolai • @GoogleDeepMind • @Waymo • @SandiaLabs afaust.info Palo Alto, CA Joined April 2009-
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Come join us and let's push the boundary of RL and agents to save lives.
Our posting for joining Google DeepMind as a Research Scientist was down for a few days but now it is back up! Apply here: google.com/about/careers/… And fill out this form: docs.google.com/forms/d/e/1FAI…
My challenge to the Class of 2026: "Write a story that protects, democratizes, and elevates the human spirit. Make the work you do worthy of the power you wield." Full video, live transcript, and my deeper reflections on engineering stewardship in my latest Substack post: open.substack.com/pub/aleksandra…
We are entering a New Intelligence Age where technology is compressing decades of discovery into days. The ink we put on the paper won’t just write code—it will write the rules of human agency for the next century. Engineering alone is no longer enough. We need stewards. Delivering the convocation keynote for the @UNMengineering was an honor, but sharing the stage with my son as he delivered the student address made it uniquely unforgettable. 👇
Happening now.
Excited to share my internship work with Genesis on biomolecular structure prediction at ICLR 🇧🇷! We simplified the structure predictor’s trunk, leading to 2-4x speedups! Find me at the Pairmixer poster: 🗓️ Saturday, 3:15–5:45 PM 📍 Pavilion 3, P3-#1022
We are honored to announce the Test of Time awards for #ICLR2026 🏆 This award recognizes papers published 10 years ago at ICLR 2016 that have had a lasting impact on the field: blog.iclr.cc/2026/04/22/ann…
Announcing the #ICLR2026 Outstanding Paper Awards 🏆 Congratulations to:
We’re looking forward to @iclr_conf in Rio de Janeiro. ➡️ Our Staff ML Research Scientist Pranav Murugan and former Genesis intern @zhang_ouyang, now a postdoc at MIT, will present findings from our Pairmixer biomolecular structure prediction model as a poster on Sat, Apr 25. More details: iclr.cc/virtual/2026/p…. We’ve also released the accompanying open-source model weights - check it out: github.com/genesistherape…. ➡️ Our ML Research Scientist @RBoiarsky is co-organizing the Learning Meaningful Representations of Life (LMRL) Workshop on Mon, Apr 27: lmrl.org ➡️ Sign up for a coffee chat with a member of our team to learn more about our open roles in ML research: jobs.ashbyhq.com/genesis-molecu… Hope to see you there!
ICLR 2026 is almost there! We have 6 exciting keynotes covering a range of areas from machine learning to robotics, neuroscience and AI for science: Maja Matarić, Max Welling, Percy Liang, Katie Bouman, Karen Adolph, Pablo Arbeláez See you all soon! #ICLR2026
Heading to @iclr_conf this week and I'll be there for the entire conference. Drop me a line or find me if you want to connect.
Calling robotics startups across Europe! Applications are now open for the @GoogleDeepMind Accelerator: Robotics, a 3-month program designed to help early-stage robotics companies scale from prototype to production. 🤖Powered by Google AI technologies, including Google Gemini Robotics Models. Apply today: goo.gle/robotics
We’re scaling up robotics in Europe. 🤖 Our Robotics Accelerator is tailored for startups and designed to bridge the gap between technology and business, powering the next generation of physical agents.
@benji_smith This is very true. For better or worse.
@benji_smith This is very true. For better or worse.
We are moving past the era of one-off digital assistants. For researchers, it’s been clear: we’re now building a self-improving learning engine. This isn't just about more compute; it’s about a fundamental shift that is (quietly) accelerating the path to AGI. Four specific pillars are enabling this transition: 1. Intent-based interaction: Natural language has broken the task barrier. Instead of rigid parameters, human intent now maps directly to machine action. 2. Code as actions: A universal layer that lets agents synthesize the interfaces they need on the fly, expanding their own capabilities to match any problem. 3. Generative simulation: Replacing manual engineering with high-fidelity sensory world models and "Social Digital Twins" to stress-test AI before it ever touches a real human. 4. Autonomous Self-Improvement: Models are beginning to teach themselves through experience—refining their own reasoning without waiting for human intervention. Together, they form a closed-loop system that breaks the traditional bottlenecks of AI development. Because tasks can expand via language and agents can "write" their own interfaces with code, they can explore increasingly diverse problems within generative worlds, and then refine their own knowledge without us labeling a single new data point. The result is an accelerated path to AGI—and on that path, I want to hold two ideas at once. First, the same shift that is accelerating AI capability is also reshaping our baseline for trust. As generative AI makes synthetic content indistinguishable from real, our global circle of trust—built over a century of mass media—begins to collapse. We are returning to something older: trust defined not by scale, but by depth. Direct observation. Shared history. Knowing who actually shows up and speaks in their own voice. Heck, we are starting to add misspellings in our own writing to indicate authencity! Second, the most important question for AI right now is not when AGI arrives. The most important question is whether we build AI as a force for genuine human augmentation, or as one more tool for synthetic noise (or worse). I’ve explored the technical and the societal implications in two new two deep-dives on Substack: aleksandrafaust.substack.com I’d love to hear your thoughts.
@JeffDean @GoogleDeepMind Thanks so much, Jeff. I am looking forward to it.
1/4 I’m excited to share that I’m returning to @GoogleDeepMind as Research Director this week! Back to the lab to push the boundaries of agentic systems and AI for Health.
@shaneguML @GoogleDeepMind Thanks. Definitely.
4/4 So why Google DeepMind now? My core research interests—self-improving models and agentic systems—are the key to solving humanity's hardest challenges. @GoogleDeepMind is the best place to push these technologies to truly serve those needs, particularly in #AI4Health.
3/4 Huge thanks to @enfeinberg, @edunov and the team. The foundation is strong, and I’m excited to see where you take it next. In my advisory role, I'll support the leadership team to ensure continuity.
Jim Fan @DrJimFan
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monoe @monochrxmatism
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Young D. Kwon ✈️ ... @YoungDKwon1
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Google DeepMind @GoogleDeepMind
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Conference on Robot L... @corl_conf
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