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working on a course project on spectral outliers appearing from low-rank connectivity structure in RNNs, documenting everything here: aniketdeshpande.com/src/blog.html
28.09.2025 23:57 — 👍 0 🔁 0 💬 0 📌 0
Simons Foundation Launches Collaboration on the Physics of Learning and Neural Computation
Simons Foundation Launches Collaboration on the Physics of Learning and Neural Computation on Simons Foundation
Our new Simons Collaboration on the Physics of Learning and Neural Computation will develop powerful tools from #physics, #math, computer science and theoretical #neuroscience to understand how large neural networks learn, compute, scale, reason and imagine: www.simonsfoundation.org/2025/08/18/s...
19.08.2025 14:43 — 👍 21 🔁 5 💬 0 📌 3
The materials for tomorrow, today.
We are the Matter Lab at the University of Toronto, led by Professor Alán Aspuru-Guzik. Our group works at the interface of theoretical chemistry with physics, computer science, and applied mathematics.
Scientist @ DeepMind and Honorary Fellow @ U of Edinburgh.
RL, agency, philosophy, foundations, AI.
https://david-abel.github.io
We advance science and technology to benefit humanity.
http://microsoft.com/research
Sr Principal Research Manager, Microsoft Research NYC. Machine Learning, AI Fairness.
Neuroscience PhD student at NYU in the Movshon and Chung Labs.
https://www.cns.nyu.edu/~saraf/
ss6786@nyu.edu
PhD student at Machine Learning Department @ CMU
Assistant Prof of CS at the University of Waterloo, Faculty and Canada CIFAR AI Chair at the Vector Institute. Joining NYU Courant in September 2026. Co-EiC of TMLR. My group is The Salon. Privacy, robustness, machine learning.
http://www.gautamkamath.com
Associate professor at U of Toronto. Computer science and math research: (differentially) private data analysis, geometry, discrepancy, optimization.
Professor, Stanford University, Statistics and Mathematics. Opinions are my own.
Research Scientist@Google DeepMind
Assoc Prof@York University, Toronto
mbrubake.github.io
Research: Computer Vision and Machine Learning, esp generative models.
Applications: CryoEM (cryoSPARC), Statistics (Stan), Forensics, and more
DeepMind Professor of AI @Oxford
Scientific Director @Aithyra
Chief Scientist @VantAI
ML Lead @ProjectCETI
geometric deep learning, graph neural networks, generative models, molecular design, proteins, bio AI, 🐎 🎶
web: http://maxim.ece.illinois.edu
substack: https://realizable.substack.com
Principal Researcher in AI/ML/RL Theory @ Microsoft Research NE/NYC. Previously @ MIT, Cornell. http://dylanfoster.net
RL Theory Lecture Notes: https://arxiv.org/abs/2312.16730
Theoretical neuroscientist; currently a Harvard Junior Fellow. jzv.io
full-time ML theory nerd, part-time AI-non enthusiast
AI & Physics PhD student at EPFL. Working on understanding AI (generalization, deep generative models, post-training). Former applied scientist intern at Amazon AI.
https://alesfav.github.io/
PhD student @UofT and @Vector, Deep Learning Theory
https://www.cs.toronto.edu/~chuning/
•PhD student @ https://www.ucl.ac.uk/gatsby 🧠💻
•Masters Theoretical Physics UoM|UCLA🪐
•Intern @zuckermanbrain.bsky.social|
@SapienzaRoma | @CERN | @EPFL
https://linktr.ee/Clementine_Domine
Chief AI Scientist at Databricks. Founding team at MosaicML. MIT/Princeton alum. Lottery ticket enthusiast. Working on data intelligence.
Research scientist at Anthropic.
PhD in machine learning from the University of Toronto and Vector Institute.
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