Hamidreza Jamalabadi
@HamidrezaJamal9
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Professor of Computational Psychiatry. Mental health, Neuroscience, Dynamical Systems, AI, Memory processes
Marburg, Germany
Joined January 2020
Nature Neuroscience Falling asleep follows a predictable bifurcation dynamic https://t.co/40xdcA33zL
nature.com
Nature Neuroscience - Li et al. propose a conceptual framework to study the phenomenon of falling asleep based on electroencephalogram data. They show that a tipping point marks the brain’s...
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I encourage you to read this article, in which we describe the current situation and the directions in which, in our view, mathematics is heading. Many thanks to Ken Ono for including me in this extraordinary project. I look forward to a wide-ranging discussion and will be
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Our #DynamicalSystems #FoundationModel was accepted to #NeurIPS2025 with outstanding reviews (6555) – first model which can *0-shot*, w/o any fine-tuning, forecast the *long-term statistics* of time series provided a context. Test it on #HuggingFace: https://t.co/FrbK5Kx9t2 ...
huggingface.co
Can time series #FoundationModels like Chronos zero-shot generalize to unseen #DynamicalSystems (DS)? No, they cannot. But *DynaMix* can, the first FM based on principles of DS reconstruction, capturing the long-term evolution of out-of-domain DS: https://t.co/fL1CLATTpB (1/6)
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Sampling is hard b/c target distribution can be high-dim with many modes. ML can help, even when state space is discrete (thus non-differentiable)! https://t.co/vNtqYOzQc5 constructs a strong sampler by fine tuning a discrete diffusion model via stochastic optimal control / RL.
arxiv.org
We study the problem of learning a neural sampler to generate samples from discrete state spaces where the target probability mass function $π\propto\mathrm{e}^{-U}$ is known up to a normalizing...
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Can AI help understand how the brain learns to see the world? Our latest study, led by @JRaugel from FAIR at @AIatMeta and @ENS_ULM, is now out! 📄 https://t.co/y2Y3GP3bI5 🧵 A thread:
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Our new preprint compares naïve baselines, network models (incl. PLRNN-based SSMs), and Transformers on 3x40‑day EMA+EMI datasets. PLRNNs gave the most accurate forecasts, yielded interpretable networks, and flagged “sad” & “down” as top leverage points. https://t.co/9trDupOR4A
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The modern university is a place where a scientist asking hard questions is seen as a threat, and an administrator burying the truth is called a leader.
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📢 Present your NeurIPS paper in Europe! Join EurIPS 2025 + ELLIS UnConference in Copenhagen for in-person talks, posters, workshops and more. Registration opens soon; save the date: 📅 Dec 2–7, 2025 📍 Copenhagen 🇩🇰 🔗 https://t.co/2wOfBxeGn7
#EurIPS @EurIPSConf
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🌀 Universality in Computable Dynamical Systems: Old and New Can dynamical systems compute? From Turing-complete fluids to Topological Kleene Field Theories, we explore how dynamics encodes computation—past, present, and future. https://t.co/LVSDaaGdL2
arxiv.org
The relationship between computational models and dynamics has captivated mathematicians and computer scientists since the earliest conceptualizations of computation. Recently, this connection has...
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Got prov. approval for 2 major grants in Neuro-AI & Dynamical Systems Recons., on learning & inference in non-stationary environments, OOD generalization, and DS foundation models. To all AI/math enthusiasts: Expect job announcements (PhD/PostDoc) soon! Feel free to get in touch.
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Compression is the heart of intelligence From Occam to Kolmogorov—shorter programs=smarter representations Meet KARL: Kolmogorov-Approximating Representation Learning. Given an image, token budget T & target quality 𝜖 —KARL finds the smallest t≤T to reconstruct it within 𝜖🧵
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prediction ≠ understanding foundation model ≠ theory
Can an AI model predict perfectly and still have a terrible world model? What would that even mean? Our new ICML paper formalizes these questions One result tells the story: A transformer trained on 10M solar systems nails planetary orbits. But it botches gravitational laws 🧵
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A statistical physics framework for optimal learning Authors: Francesca Mignacco, Francesco Mori https://t.co/hlRp0uU7Wj
arxiv.org
Learning is a complex dynamical process shaped by a range of interconnected decisions. Careful design of hyperparameter schedules for artificial neural networks or efficient allocation of...
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Clarifying the conceptual dimensions of representation in neuroscience.
arxiv.org
Despite the centrality of the notion of representation to its explanations, neuroscience lacks a unified framework for the concepts used to characterize representation, leading to disparate use of...
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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 👉 https://t.co/t77lrS2by5
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AI at least currently, is simply a prediction machine, it is hardly a bad thing to be able to predict the next words/bits. We would be less negative if we differentiate between science and scientists, very likely we will be doing better science, just need to retrain ourselves
It won't be long and physics will be overrun by AI slop, mark my words. It's a disaster waiting to happen for academia that has been optimizing for quantity over quality for decades, you can basically see it coming. https://t.co/pZ6dr0rdya
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It won't be long and physics will be overrun by AI slop, mark my words. It's a disaster waiting to happen for academia that has been optimizing for quantity over quality for decades, you can basically see it coming. https://t.co/pZ6dr0rdya
I get especially sad when I see researchers at my university, including very senior professors, excitedly saying "Look! Now I can use <random GenAI tool> to read papers, write papers, derive equations, write code, ..." And it's like... why are you even here, man? (1/2)
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Demanding that "everyone should immediately evacuate" Tehran is astonishing. Most of the city’s 10 million residents simply cannot leave. No one can justify striking them by saying: "We warned you!" Warnings mean nothing when escape isn’t an option.
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Can time series #FoundationModels like Chronos zero-shot generalize to unseen #DynamicalSystems (DS)? No, they cannot. But *DynaMix* can, the first FM based on principles of DS reconstruction, capturing the long-term evolution of out-of-domain DS: https://t.co/fL1CLATTpB (1/6)
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