Theoretical #Engineer and #Computational Innovator in #physics of #complex #systems, #network science and machine #intelligence. Chair Professor @Tsinghua_Uni

Beijing, China
Joined June 2018
#NSIA @NSIAsatellite Natasa Przulj,Professor of Computational Biology, MBZUAI; Professor, Computer Science UCL, Inventor of graphlets kick-starts the inaugural edition of the Network Science Informs AI satellite @netscisociety #netsci26
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Towards @nvidia 1:4 Semi-Structured 75% Sparsity via Cannistraci–Hebb N:M Dynamic Sparse-to-Sparse Training See the main results. @netscisociety
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It is fascinating to study complex systems such as the brain and to inspire new manners to train neural networks. We do this with a group of students @Tsinghua_Uni youtu.be/pzkctDSZn8g?si=Sfb2…
“#AI learns from the brain, and the #brain is revealed through AI,” says #TsinghuaRen Prof. Carlo Vittorio Cannistraci @NetworkAutomata (Italy), Chief Scientist at THU Laboratory of Brain and Intelligence, who envisions a new generation of AI that is energy-efficient & effective.
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This is the top 10 of disinformation spreaders in X according to Grok 😂. Who do you think will be at the position number 10?
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Replying to @morganb
@morganb @elonmusk @deepseek_ai #DeepSeek is only the beginning. The future of efficient #AI is to develop complex systems and brain-inspired algorithms that leverage dynamic sparse connectivity and morphology to automatically learn network architectures. youtu.be/pzkctDSZn8g?si=vDmI…
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Neural Networks: the future of efficient #AI is to develop complex systems and brain-inspired algorithms that leverage dynamic sparse connectivity and morphology to automatically learn network architectures. @netscisociety @CompSysSoc youtu.be/pzkctDSZn8g?si=N1J5…
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Neural Networks: the future of efficient AI is to develop complex systems and brain-inspired algorithms that leverage dynamic sparse connectivity and morphology to automatically learn their network architectures. @elonmusk @xai @OpenAI @YiMaTweets @andst7 youtu.be/pzkctDSZn8g?si=p5mj…
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Below you find some introductory figures. Group separability measures in data science: an overview.
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To pursue a dream you can't stay in the crowd. You need to stay 'alone' in the crowd, lost in the infinite imprecision of this world. Only in that moment you will feel the depth (and depth is sparsity and sparsity is risk of fracture) of the constitutive rules or the universe,
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Tomorrow I will talk to the Institute of Data Science of the @HKUniversity I will discuss our recent achievements in Brain-network inspired computing for next generation efficient and sustainable #AI openreview.net/forum?id=iayE… nature.com/articles/s41928-0… @NatureElectron @Tsinghua_Uni
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I meditate at sunset when I am in Sicily in Milazzo, to envision new directions of research and to be inspired about the future of complexity science and #AI: #ScienceWithTheFlow. @comunemilazzo – at Milazzo, Sicily
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