
Senior Research Scientist @GoogleDeepMind Discovery Team. Previously PhD with @SchmidhuberAI. Working on Reinforcement Learning.
London, UK
Joined September 2018
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Two of our #ICML papers were selected for an oral presentation (Top 1.5%)!
1. Learning Useful Representations of Recurrent Neural Network Weight Matrices (arxiv.org/abs/2403.11998)
2. GPTSwarm: Language Agents as Optimizable Graphs (arxiv.org/abs/2402.16823)
Francesco Faccio retweeted
We’re excited to introduce Inherent, a lab designed from scratch to build AI agents that discover new knowledge.
The coming era of machine-driven scientific inquiry demands a new kind of research institution and a new kind of AI.
To achieve our mission, we live within the experiment, recursively self-improving the entire research organisation. We investigate questions including:
- What does ‘AI taste’ look like in the sciences, and how can we build an institution that embraces this new aesthetic of discovery?
- What new kinds of human-machine teaming will make the most of AI that can truly innovate?
- How can we build recursive self-improvement at the collective level that continually increases human agency over outcomes?
We have just closed a $50m seed round led by @IndexVentures and @radicalvcfund, with participation from other outstanding investors including NVentures (@nvidia's venture capital arm), @buildexante, Metaplanet, Macroscopic, @MythosVentures, Charlie Songhurst, @chalfs, @jluan, @dwarkesh_sp, @Thom_Wolf, @j_foerst and @maxjaderberg. We are advised by @matthewclifford.
Inherent is a Public Benefit Corporation headquartered in London.
📣 Hiring Alert: @GoogleDeepMind Student Researcher - 2026
@Xidong_Feng and I are hiring a PhD Student Researcher to join the GDM Discovery team in London 🇬🇧 to investigate Artificial Curiosity and Intrinsic Motivation algorithms in general discovery 🔭
Apply below! 👇🏻:
Unofficial application form: forms.gle/ikYnGTNNkNecYQ7s5
Google careers page: google.com/about/careers/app…
For a sense of the work we do (and love!), check out our previous project: arxiv.org/abs/2510.23881.
I'm in Singapore for #ICLR2025! DM me if you’d like to meet and chat about Creativity and Curiosity in AI, AGI, Agents, or exciting opportunities at @GoogleDeepMind.
You might even get a free Italian coffee ☕️ :)
If you are working on World Models, you should definitely submit your paper to our #ICLR2025 workshop!
🚨 4 Days Left! 🚨
Our ICLR 2025 Workshop: "World Models: Understanding, Modelling, and Scaling" is calling for submissions! 📢
📅 Submission Deadline: February 10, 2025 (AoE)
🔗 openreview.net/group?id=ICLR…
The workshop covers the widest range of topics on world models, including world understanding, modelling, and scaling with cutting-edge generative AI.
Join us to explore model-based RL, causality, diffusion models, robotics, video generation, embodied AI and more with amazing keynote speakers & panelists!
Don’t miss out—submit your paper now! 🚀
#ICLR2025 #WorldModels #AI #ReinforcementLearning #Causality #GenerativeAI
Are you a rising star in AI? 🌟
Join us as a speaker for the 4th edition of the KAUST Rising Stars in AI Symposium. In the past 2 years co-organizing this event, I've met incredible researchers now in top industrial and academic positions worldwide.
More info:
📅 Event date: April 7-10, 2025
⏳ Application deadline: December 18
🔗 Apply here: kaust.edu.sa/en/news/rising-…
Some of the last year's amazing speakers: @AdilSlm @fengcmai @umangsbhatt @ShangtongZhang @AlhusseinFawzi @he_sihong @hadisalmanX @jd92wang @JonathonLuiten @raazdwivedi @JunXia_Westlake @sivil_taram @hermanhwdong @Mengyue_Yang_ @JaneYuBear @atasteoff @materzynska @BohanWang1 @FHKPetersen @weisshelter
I'm thrilled to announce that I'm joining the Discovery team at @GoogleDeepMind in London as a Senior Research Scientist starting this January! It's incredible what the team has achieved in the past decade and I am so looking forward towards more scientific discoveries with AI.
If you are interested in using AI for scientific discovery, this project might be perfect for you!
I am hiring 3 postdocs at #KAUST to develop an Artificial Scientist for discovering novel chemical materials for carbon capture. Join this project with @FaccioAI at the intersection of RL and Material Science. Learn more and apply: faccio.ai/postdoctoral-posit…
I had an amazing time in Riyadh meeting with many industrial partners and presenting our recent advancements!
#GlobalAISummit #GainSummit
#KAUST’s experts are joining the @globalaisummit 2024 in Riyadh.
Discover how we're driving #AI innovation for a sustainable future and contributing to #SaudiVision2030.
#GlobalAISummit #GainSummit
Had a great time presenting at #ICML2024 alongside @idivinci & @LouisKirschAI. But the true highlight was @SchmidhuberAI himself capturing our moments on camera! #AI #DeepLearning
🚀 Overdue launch of my personal website faccio.ai/! Check out my latest AI projects.
I would like to thank my ancestors for giving me a last name that means "I make" in Italian. So, yes, I make #AI 🤖
Our paper, "Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning," was accepted at #EWRL.
Congratulations to Yuhui Wang and the team!
Paper: arxiv.org/abs/2406.08404
#AI #DeepLearning #RL
Heading to #ICML2024 for a busy week with 3 posters and 2 oral presentations.
If you’re interested in discussing collaborations, visiting, or hiring opportunities at @AI_KAUST with @SchmidhuberAI, feel free to connect!
🚀Want to cut inference times by up to 50% and save money when using Transformer/CNN/Consistency-based diffusion models?
Check out our latest work on Faster Diffusion Through Temporal Attention Decomposition led by @HaoZhe65347, featuring @SchmidhuberAI.
Paper: arxiv.org/abs/2404.02747v2
Code: github.com/HaozheLiu-ST/T-GA…
#AI #DeepLearning #DiffusionModel
Can neural networks with 5000 layers improve long-term planning? 🤖
Check out our latest research with @SchmidhuberAI, @oneDylanAshley, and team: arxiv.org/abs/2406.08404
#AI #DeepLearning #RL
Recently, I had the pleasure of giving a talk at Microsoft Research Asia in Beijing on 'Learning to Extract Information from Neural Networks.' Met lots of brilliant minds there!
#MSFTResearch
Check out our most recent work!
Our #GPTSwarm models Large Language Model Agents and swarms thereof as computational graphs reflecting the hierarchical nature of intelligence. Graph optimization automatically improves nodes and edges. arxiv.org/abs/2402.16823
github.com/metauto-ai/GPTSwa…
gptswarm.org/




















