Carlo Vittorio Cannistraci
Zhou Yahui Chair Professor
Chief Scientist, Tsinghua Laboratory of Brain and Intelligence (THBI)
Director, Center for Complex Network Intelligence (CCNI) at THBI
Department of Psychological and Cognitive Science
Department of Computer Science and Technology
School of Biomedical Engineering
Tsinghua University, Beijing, China
If Physics studies the principles and mechanisms of the outside universe.
Brain science studies the principles and mechanisms of the inside universe.
My research is at the interface between these two disciplines.
I deal with "Physics and Engineering of Complexity and Intelligence"：
studying principles of natural and artificial intelligence.
Carlo Vittorio Cannistraci Carlo Vittorio CaCarlo Vittorio Cannistraci
Biography and scientific mission
Prof. Carlo Vittorio Cannistraci is a theoretical engineer and computational innovator working in complex network intelligence, an interdisciplinary field at the intersection of complex systems, network science, and artificial intelligence. At the core of his research is the idea that intelligence is fundamentally a property of organized connectivity: how elements connect, reorganize, and interact can be as important as the elements themselves. His work has contributed to the modelling of complex network connectivity and geometry, brain-inspired sparse artificial intelligence, and neuromorphic computing.
His scientific trajectory has progressively explored this principle across natural and artificial systems: from understanding the organization and geometry of complex networks, to uncovering how network structure can shape learning, computation, and intelligence.
His current research at Tsinghua University focuses on brain-inspired sparse network intelligence. The central objective is to develop fundamental theories and computational principles for a new generation of artificial intelligence in which structure itself becomes a source of intelligence, adaptability, and computational efficiency. Rather than treating network architecture as a fixed substrate on which learning occurs, his research investigates how sparsity, topology, and learning dynamics can jointly organize computation and allow artificial systems to learn not only their parameters, but also the structure through which information flows.
This perspective is inspired by natural intelligence, where high-level computation emerges from highly selective, structured, and adaptive connectivity. By uncovering these organizing principles, his work aims to establish theoretical foundations for artificial intelligence capable of achieving high performance with substantially lower requirements in energy, memory, data, and computing resources. Efficiency therefore emerges also as a consequence of better network organization: more intelligence from more meaningful structure.
This research is inherently transdisciplinary. Prof. Cannistraci is Zhou Yahui Chair Professor at the Tsinghua Laboratory of Brain and Intelligence (THBI) and the Department of Psychological and Cognitive Sciences, Adjunct Professor in the Department of Computer Science and Technology, and affiliated with the School of Biomedical Engineering at Tsinghua University.
He is the founder and director of the Center for Complex Network Intelligence (CCNI) at THBI. CCNI develops fundamental theories and pioneering computational methods at the interface of information science, the physics of complex systems, network science, and machine intelligence, with particular emphasis on brain- and life-inspired computing.
The resulting methods extend from fundamental theory to applications in precision biomedicine, neuroscience, and social and economic systems. Across these domains, the unifying research question remains the same: how the organizing principles of complex natural systems can reveal new principles of intelligence and inspire more powerful, adaptive, and sustainable forms of artificial computation.
Current research challenge of CCNI: Brain-network-inspired computing for next generation efficient and sustainable AI
The human brain performs learning, perception, and reasoning while consuming only about 20 watts of metabolic power. By comparison, training today’s large language models can require megawatt-scale computing infrastructure, while frontier AI increasingly relies on computing infrastructure whose power capacity is reaching the gigawatt scale. These contrasts highlight the extraordinary energy efficiency of biological intelligence. Brain-inspired network science research can play a relevant role for designing low-consumption and efficient deep learning. We need to develop new concepts and theories for an ecological and sustainable approach to AI, and some of these new computing paradigms can be borrow from the brain and its complex systems biology.
In the Center for Complex Network Intelligence (CCNI), we are interested to investigate three key brain network features adopted for efficient computing: connectivity sparsity, connectivity morphology and neuro-glia coupling.
On sparse connectivity, see our four recent articles:
“Epitopological learning and Cannistraci-Hebb network shape intelligence brain-inspired theory for ultra-sparse advantage in deep learning”, in The Twelfth International Conference on Learning Representations (ICLR) 2024.
“Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected” in The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS) 2025
“Adaptive Cannistraci-Hebb Network Automata Modelling of Complex Networks for Path-based Link Prediction” in The Thirty-Ninth Conference on Neural Information Processing Systems (NeurIPS) 2025
“Cannistraci-Hebb Training on Ultra-Sparse Spiking Neural Networks” in The fourteenth International Conference on Learning Representations (ICLR) 2026.
On sparse morphology, see our two recent articles:
“Neuromorphic dendritic network computation with silent synapses for visual motion perception” in Nature Electronics 2024.
“Artificial neurons beyond spikes: Neuromorphic systems” in Nature Electronics 2025.
On neuro-glia coupling, soon we will publish our work in progress.
Research strategy at CCNI
Organization chart of research at the Center for Complex Network Intelligence (CCNI). The chart clarifies what is the main strategy behind research at CCNI and how it influences the topics of our interest in theoretical and applied science. There is a clear interrelation between theoretical topics (which are crucial to offer new solutions in applied topics) and applied topics (which play an indispensable role to open new questions and to trigger innovation in theoretical research).
Research directions at CCNI
The CCNI adopts a transdisciplinary approach integrating information theory, machine intelligence and network science to investigate adaptive processes that characterize complex interacting systems at different scales, from molecules to ecological and socio-economic systems. This knowledge is leveraged to create novel and more efficient artificial intelligence algorithms; and to perform advanced analysis of patterns hidden in data, signals and images. Our theoretical effort is to translate advanced mathematical paradigms typically adopted in theoretical physics (like topology and manifold theory) to characterize many-body interactions in quantitative life science. We apply the theoretical frameworks we invent in the mission to develop computational tools for systems and network biology, personalized biomedicine and combinatorial drug therapy, social and economic data science.
Plasticity phenomena – like remodelling, growth and evolution – modify the topology of complex living systems, their internal state and their multidimensional representation in form of networks or high-dimensional datasets. Our theoretical mission is to elucidate the general rules and mechanisms that underlie this type of structural plasticity, which is at the basis of learning and memory processes in living organisms. In particular, we develop methods for topological analysis of self-adaptive and self-organizing learning systems such as protein interaction and bacteria-metabolite networks at the molecular level, and brain networks at the cellular level.
In neuroscience, we are interested in how the brain networks wire at synaptic and functional levels to modulate learning processes. And, on a molecular pathway scale, we seek to identify the network patterns that could suggest which broken functional modules are responsible for memory aberrations in neurodegenerative diseases. Since general paradigms of regeneration and degeneration can be significantly inspired by developmental biology models, we study regulatory patterns of tissue differentiation in normal and cancer conditions.
Our mission in translation and network medicine is to adopt advanced machine learning and network science approaches to integrate molecular networks and omic profiles for the definition of personalised therapeutic plans and individualised drug treatments. Furthermore, as the cardiovascular system is a paradigmatic example of an adaptive complex system, we apply our pattern recognition algorithms to explore normal/pathological conditions in cardiovascular patients.
Finally, since the CCNI aims to study the complexity of life systems and their mechanisms of adaptation across scales, we collaborate with experts in ecological, social and economic science in order to apply our algorithms to their data and to reveal whether generalized rules of self-organization characterize life matter from molecules and cells to microbes and animals (including humans).
Education
- Italian Inter-polytechnic School of Doctorate (Turin, Milan and Bari), PhD, 2010.
- Polytechnic School of Milan, Milan, Italy, MS, 2005.
Previous Appointments
- Biotechnologisches Zentrum (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden (TUD), Germany. Principal Investigator (Tenured)，2019 -2021
- Biotechnologisches Zentrum (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden (TUD), Germany. Principal Investigator (Tenure-track), 2014-2018
- King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia. Researcher Scientist, 2013
- King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia. Postdoctoral Researcher, 2010-2012
- University of California, San Diego, USA. Visiting Scholar, 2009
- San Raffaele Scientific Institute, Milan, Italy. Research Fellow, 2006-2010
- Biomedical Engineering Institute of the Italian National Research Council (ISIB-CNR, Milan department), Milan, Italy. Research Fellow, 2005-2006
Selected Publications
Artificial Intelligence, Network Science & Physics
A Generalized Geometric Theoretical Framework of Centroid Discriminant Analysis for Linear Classification of Multi-dimensional Data
Y Wu, J Zhao, CV Cannistraci
The fourteenth International Conference on Learning Representations (ICLR) 2026
Latent Geometry-Driven Network Automata for Complex Network Dismantling T Adler, M Grassia, Z Liao, G Mangioni, CV Cannistraci
The fourteenth International Conference on Learning Representations (ICLR) 2026.
Cannistraci-Hebb Training on Ultra-Sparse Spiking Neural Networks Y Hua, J Zhang, Y Zhang, W Gu, L You, B Xiong, CV Cannistraci*, H Chen*
The fourteenth International Conference on Learning Representations (ICLR) 2026.
Alignment-Enhanced Integration of Connectivity and Spectral Sparsity in Dynamic Sparse Training of LLM W Wu, Y Zhang, J Zhao, CV Cannistraci
The fourteenth International Conference on Learning Representations (ICLR) 2026.
A generalized logistic-logit function and its application to multi-layer perceptron and neuron segmentation
W Gu, Y Zhang, A Muscoloni, CV Cannistraci
Frontiers in Artificial Intelligence 9, 1785867, 2026
Artificial neurons beyond spikes: Neuromorphic systems
CV Cannistraci, E Baek
Nature Electronics 2025
Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected Y Zhang, D Cerretti, J Zhao, W Wu, Z Liao, U Michieli, CV Cannistraci
The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS) 2025
Adaptive Cannistraci-Hebb Network Automata Modelling of Complex Networks for Path-based Link Prediction J Zhao, A Muscoloni, U Michieli, Y Zhang, CV Cannistraci
The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS) 2025
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
J Zhao, Y Zhang, X Li, H Liu, CV Cannistraci
Forty-second International Conference on Machine Learning, 2025 (ICML25)
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models
J Zhao, Y Zhang, CV Cannistraci
Forty-second International Conference on Machine Learning, 2025 (ICML25)
Neuromorphic Dendritic Computation with Silent Synapses for Visual Motion Perception
Eunye Baek, Sen Song, Zong Rong, Luping Shi, Carlo Vittorio Cannistraci
Nature Electronics, 2024
Epitopological learning and Cannistraci-Hebb network shape intelligence brain-inspired theory for ultra-sparse advantage in deep learning
Yingtao Zhang, Jialin Zhao, Wenjing Wu, Alessandro Muscoloni, Carlo Vittorio Cannistraci
The Twelfth International Conference on Learning Representations (ICLR) 2024
Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models
Yingtao Zhang, Haoli Bai, Haokun Lin, Jialin Zhao, Lu Hou, Carlo Vittorio Cannistraci
The Twelfth International Conference on Learning Representations (ICLR) 2024
Stealing fire or stacking knowledge’ by machine intelligence to model link prediction in complex networks
Alessandro Muscoloni and Carlo Vittorio Cannistraci
iScience, 2023
Geometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes
Carlo Vittorio Cannistraci and Alessandro Muscoloni
Nature Communications, 2022
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
Claudio Duran, …, Giovanni Gasbarrini, Antonio Gasbarrini & Carlo Vittorio Cannistraci
Nature Communications, 2021
Modular gateway-ness connectivity and structural core organization in maritime network science
Mengqiao Xu, Qian Pan, Alessandro Muscoloni, Haoxiang Xia & Carlo Vittorio Cannistraci
Nature Communications, 2020
Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions
Eunhye Baek, Nikhil Ranjan Das, Carlo Vittorio Cannistraci, … & Gianaurelio Cuniberti
Nature Electronics, 2020
Navigability evaluation of complex networks by greedy routing efficiency
Alessandro Muscoloni & Carlo Vittorio Cannistraci
Proceedings of the National Academy of Sciences, 2019
A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities
Alessandro Muscoloni & Carlo Vittorio Cannistraci
New Journal of Physics, 2018
Machine learning meets complex networks via coalescent embedding in the hyperbolic space
Alessandro Muscoloni, … , Ginestra Bianconi & Carlo Vittorio Cannistraci
Nature Communications, 2017
Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks
Simone Daminelli, Josephine Maria Thomas, Claudio Durán & Carlo Vittorio Cannistraci
New Journal of Physics, 2015
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks
Carlo Vittorio Cannistraci, Gregorio Alanis-Lobato, Timothy Ravasi
Scientific reports, 2013
Computational Biomedicine & Neuroscience
Hyperpathway: visualizing organization of pathway-molecule enriched interactions in omics studies via hyperbolic bipartite network embedding
I Abdelhamid, Z Liao, Y Liu, A Lefebvre, A Acevedo, CV Cannistraci
npj Systems Biology and Applications 2026
Frequency-specific intermuscular coherence of synergistic muscles during an isometric force generation task
D Borzelli, A Cacciola, CV Cannistraci, A Alito, D Milardi, A d’Avella
Frontiers in Neural Circuits 19, 1675012, 2025
De novo identification of universal cell mechanics regulators
M Urbanska, Y Ge, M Winzi, ..., Carlo Vittorio Cannistraci, Jochen Guck.
Elife, 2025
Spatial Reconstruction of Oligo and Single Cells by De Novo Coalescent Embedding of Transcriptomic Networks
Y Zhao, S Zhang, J Xu, Y Yu, G Peng, CV Cannistraci, JDJ Han
Advanced Science, 2023
Prevalence, Characteristics, and Outcomes of COVID-19–Associated Acute Myocarditis
E Ammirati, L Lupi, M Palazzini, NS Hendren, JL Grodin, CV Cannistraci, …, Marco Metra
Circulation, 2022
Proprotein convertase subtilisin/kexin 9 (PCSK9) promotes macrophage activation via LDL receptor-independent mechanisms
Shunsuke Katsuki, ... , Carlo V. Cannistraci, ... , Masanori Aikawa
Circulation Research, 2022
Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle
Xian Xia, … , Carlo Vittorio Cannistraci, Yong Zhou & Jing-Dong J Han.
Nature Metabolism, 2020
Machine learning of human plasma lipidomes for obesity estimation in a large population cohort
Mathias J Gerl, ... , Carlo Vittorio Cannistraci and Kai Simons
Plos Biology, 2019
Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory
Claudio Durán, Simone Daminelli, ..., & Carlo Vittorio Cannistraci
Briefings in Bioinformatics, 2018
A promoter-level mammalian expression atlas
Fantom Consortium (including Carlo Vittorio Cannistraci)
Nature, 2014
Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation
Fantom Consortium (including Carlo Vittorio Cannistraci)
Proceedings of the National Academy of Sciences, 2014
Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding
Carlo Vittorio Cannistraci, Gregorio Alanis-Lobato, Timothy Ravasi
Bioinformatics, 2013
Identification and Predictive Value of Interleukin-6+ Interleukin-10+ and Interleukin-6− Interleukin-10+ Cytokine Patterns in ST-Elevation Acute Myocardial Infarction
Enrico Ammirati^, Carlo Vittorio Cannistraci^, … & Attilio Maseri
Circulation Research, 2012 (^first co-authorship)
Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes
CV Cannistraci, T Ravasi, FM Montevecchi, T Ideker, M Alessio
Bioinformatics, 2010
An atlas of combinatorial transcriptional regulation in mouse and man
Timoty Ravasi^, Harukazu Suzuki^, Carlo Vittorio Cannistraci^, et al.
Cell, 2010 (^first co-authorship)
Honors and Awards
- Symposium with the Premier Minister of China as a merit foreign expert (2026)
- National Natural Science Foundation of China International Researcher Project for Senior Scientists (NSFC RFIS-III, 2025)
- Third Place, HICOOL Global Entrepreneurship Competition (2025)
- Chair Professor Zhou Yahuī Award, Tsinghua University (2021)
- National High-Level Talent Program of China for Senior Scientists (2021)
- Shanghai 1000 talents plan award (2019)
- Technische Universität Dresden (TUD) Young Investigator Award in Physics. (2016)
Speeches and Talks
Professor Carlo delivered over 80 talks at various conferences and institutions many of them as Invited, Keynote and Plenary speaker. Notably, among the most recent:
- Plenary Speaker at the AI4X–Accelerate Conference 2026, jointly organized by the Institute for Functional Intelligent Materials (I-FIM), National University of Singapore, and the Acceleration Consortium, University of Toronto. June 16-19 2026 at the Raffles City Convention Centre, Singapore.
- Invited Speaker at the Massachusetts Institute of Technology (MIT) in Boston (USA) to deliver an open colloquium at the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). IAIFI is one of the inaugural institutes of the U.S. National Science Foundation, focusing on pioneering interdisciplinary AI+Physics research. March 13, 2026. The talk is online at: https://www.youtube.com/watch?v=yq9_VkCkhAI
- Invited Speaker seminar at the Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen, Germany. March 19, 2026. Invited by Dr. Shiwei Liu.
- Invited Speaker seminar at the Network Science institute, Northeastern University, Boston, USA. March 16, 2026. Invited by Prof. Albert-László Barabási.
- Keynote Speaker at the Nature Conference on “Exploring the frontiers of brain science and brain-inspired intelligence”, December 12-13, 2025, Beijing, China.
- Keynote Speaker at the TEDxTHU 2024, December 5, Tsinghua University, Beijing, China. The talk is online at: https://www.youtube.com/watch?v=pzkctDSZn8g
- Invited Speaker at the 2nd Nature Conference on Neuromorphic Computing, October 13-16, 2024, Beijing, China.
Events organization
Prof. Carlo and his lab are the organizers of the satellite on network science for artificial intelligence (network science informs AI, https://network-science-ai.github.io/) which was selected by the network science society to be included in Netsci2026, the 20th flagship conference of the Network Science Society
Graduates at CCNI
Yingtao Zhang, PhD (2022-2026), Thesis: Research on Sparsification Methods for Large Language Models
Diego Cerretti, Master (2024-2026), Thesis: Brain Network Science Modelling of Sparsity in Artificial Neural Networks
Contact Informations
Email: kalokagathos.agon@gmail.com or kailong@mail.tsinghua.edu.cn
Office: Center for Complex Network Intelligence, Tsinghua Laboratory of Brain and Intelligence, 512 503 Lv Dalong Building, Tsinghua University
Contact: +86-10-62786624
