Encode Fellow @imperialcollege. NeuroAI PhD @EdinburghUni. Raised by @Cohere_Labs and @UofT.
London, England
Joined January 2014
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Bryan M. Li retweeted
Start a company in AI for Science.
The Encode: AI for Science fellowship offers a year of freedom to build what matters -- salary, 100k GBP of compute, and partnership with the top scientists in the UK.
No equity or fees, it's a fully funded fellowship!
Apply by March 28
Bryan M. Li retweeted
Freedom to Build What Matters
The first glimmers of AGI are here. Whatever you can dream of, you can build.
The efforts of a single individual have never mattered more. A single person, an idea, and a late night in the lab might be what changes a field.
And yet the outputs of such extraordinary power have mostly been disappointing. We are using the most important technology in history to create addictive short-form video, AI girlfriends, and better ad targeting.
Now is the time to do the work that will define this era.
We can cure disease, feed a continent, or make humanity multi-planetary. What you choose to work on now may be the most consequential decision of your career.
We built the Encode: AI for Science Fellowship for people who feel that weight.
One year of salary, plenty of compute, and direct access to frontier labs, data, and domain experts across biomaterials, atmospheric science, neuromodulation, robotics, and more.
No equity, no strings attached — just the freedom to explore.
We've partnered with ARIA and the UK's leading research universities to make it possible.
Applications close March 28th.
Bryan M. Li retweeted
As Transactions on Machine Learning Research (TMLR) grows in number of submissions, we are looking for more reviewers and action editors. Please sign up!
Only one paper to review at a time and <= 6 per year, reviewers report greater satisfaction than reviewing for conferences!
We present our preprint on ViV1T, a transformer for dynamic mouse V1 response prediction. We reveal novel response properties and confirm them in vivo.
With @wolfdewulf, Danai Katsanevaki, Arno Onken and Nathalie Rochefort.
Paper and code at the end of the thread!
🧵1/7
We sincerely thank Turishcheva & Fahey et al. (2023) for organising the Sensorium challenge(s!) @SensoriumComp and for making their high-quality, large-scale mouse V1 recordings publicly available, which made this work possible!
6/7
The model also works with datasets containing a few hundred neurons from different animals and laboratories. There is more good stuff in the appendix of the paper and the code repository!
Paper: biorxiv.org/content/10.1101/…
Code and model weights: github.com/bryanlimy/ViV1T-c…
7/7
Bryan M. Li retweeted
Launching: 18 Encode Fellows
We gathered our inaugural cohort in London for 3 days of intense workshops, red-teaming, discovery, socials, and more. It culminated in our first ‘Community Critique’.
Curious? Check out our projects: encode.pillar.vc/the-science
Bryan M. Li retweeted
How can AI become a partner in scientific discovery, design and dreaming?
@pillar_vc brought together 150 of the Encode: AI for Science community for 'The Experimental Imagination' - a BBQ below The Shard in London Bridge to celebrate Encode's milestones so far.
Bryan M. Li retweeted
Applications are now open for the next cohort of the Cohere Labs Scholars Program! 🌟
This is your chance to collaborate with some of the brightest minds in AI & chart new courses in ML research. Let's change the spaces breakthroughs happen.
Apply by Aug 29.
Bryan M. Li retweeted
Excited to co-organize our NeurIPS 2025 workshop on Foundation Models for the Brain and Body!
We welcome work across ML, neuroscience, and biosignals — from new approaches to large-scale models. Submit your paper or demo! 🧠💪
Excited to announce the Foundation Models for the Brain and Body workshop at #NeurIPS2025!🧠
We invite short papers or interactive demos on AI for neural, physiological or behavioral data.
Submit by Aug 22 👉 brainbodyfm-workshop.github.…
Bryan M. Li retweeted
Retrieval-augmented generation (RAG) helps AI systems provide more information to a large language model when generating responses to user queries. A new method for conducting “global” queries can optimize the performance of global search in GraphRAG. msft.it/6018WmEkm
ALT The image features three white icons on a gradient background transitioning from blue on the left to green on the right. The first icon, located on the left, depicts a hierarchical structure resembling a workflow with connected squares. The middle icon represents GraphRAG (interconnected nodes and lines). The third icon, on the right, shows a globe with a magnifying glass overlaying it, symbolizing global search.
Bryan M. Li retweeted
Introducing ✨Aya Expanse ✨ – an open-weights state-of-art family of models to help close the language gap with AI.
Aya Expanse is both global and local. Driven by a multi-year commitment to multilingual research.
cohere.com/research/aya
Bryan M. Li retweeted
Did you miss our last 🛣️ Roads to Research session with @bryanlimy, @lekeonilude and @strubell?
Catch up on paths to graduate studies with these guest speakers now on our channel or podcast! ✨
Bryan M. Li retweeted
1/3 Excited to share our tell-tale heart study on Bipolar Disorder (BD), now published in @npjMentalHealth : nature.com/articles/s44184-0… @tetraduzione @bryanlimy @DHidalgoMazzei @BioMedAI_CDT @BipolarBCN
Bryan M. Li retweeted
Congrats to Dr. @andreasgrv who just defended his #PhD thesis on unargmaxability and constraints in deep learning, examined by Vlad Niculae and @driainmurray 🎉
A pity you could not (yet) see Andreas' beautifully designed thesis!
In the meantime, some pics from the pre-viva 👇
Happy to share that I have started my internship at @MSFTResearch and will be working in Redmond WA for the next 3 months! Let me know if you’re around and want to hangout!