Carlo Vittorio Cannistraci
周亚辉讲席教授
清华大学脑与智能实验室首席研究员
清华大学脑与智能实验室复杂网络智能中心主任
任职于清华大学心理与认知科学系
清华大学计算机科学与技术系兼职教授
清华大学生物医学工程系
邮箱：kalokagathos.agon@gmail.com；kailong@mail.tsinghua.edu.cn
地址：北京市海淀区清华大学吕大龙楼5层512,503
电话：+86-10-62786624
教育背景
2005年，意大利米兰理工大学（Politecnico di Milano）获硕士学位
2010年，意大利跨理工大学博士学院（Italian Inter-polytechnic School of Doctorate，联合都灵、米兰和巴里三地理工院校）获博士学位
工作履历
2019–2021年，德国德累斯顿工业大学（Technische Universität Dresden, TUD）分子与细胞生物工程中心（CMCB）生物技术中心（BIOTEC），终身教职独立课题组负责人（Principal Investigator, Tenured）
2014–2018年，德国德累斯顿工业大学（TUD）分子与细胞生物工程中心（CMCB）生物技术中心（BIOTEC），准聘长聘独立课题组负责人（Principal Investigator, Tenure-track）
2013年，沙特阿拉伯吉达阿卜杜拉国王科技大学（King Abdullah University of Science and Technology, KAUST），研究科学家（Research Scientist）
2010–2012年，沙特阿拉伯吉达阿卜杜拉国王科技大学（KAUST），博士后研究员（Postdoctoral Researcher）
2009年，美国加利福尼亚大学圣迭戈分校（University of California, San Diego, UCSD），访问学者（Visiting Scholar）
2006–2010年，意大利米兰圣拉斐尔科学研究所（San Raffaele Scientific Institute），研究员（Research Fellow）
2005–2006年，意大利米兰意大利国家研究委员会生物医学工程研究所（Biomedical Engineering Institute, Italian National Research Council，ISIB-CNR 米兰分部），研究员（Research Fellow）
研究概况
主要从事复杂网络智能（Complex Network Intelligence）研究。该领域是一门交叉学科，融合了复杂系统、网络科学与人工智能。其研究的核心理念是：智能从根本上而言是有组织连接所呈现的一种属性。系统中的基本单元如何相互连接、重组和交互，其重要性并不亚于这些基本单元本身。
研究工作涵盖复杂网络连接模式与几何结构建模、脑启发稀疏人工智能以及神经形态计算等方向，并持续探索自然系统与人工系统中“连接结构如何塑造智能”这一基本科学问题。从复杂网络的组织规律与几何特征，到网络结构如何影响学习、计算和智能产生，其科研发展始终围绕这一核心思想展开。
目前，Cannistraci 教授在清华大学主要开展脑启发稀疏网络智能（brain-inspired sparse network intelligence）研究。其核心目标是建立面向新一代人工智能的基础理论和计算原理，使网络结构本身成为智能、适应性与计算效率的重要来源。不同于将网络架构视为学习过程中的固定载体，他的研究重点关注：稀疏性、网络拓扑以及学习动力学如何协同组织计算，使人工智能系统不仅能够学习模型参数，还能够学习信息流动所依赖的网络结构本身。这一研究思路受到自然智能系统的启发。在生物智能中，高水平计算能力往往产生于高度选择性、结构化且具有适应性的连接模式。通过揭示这些基本组织原则，Cannistraci 教授及其团队致力于建立新的人工智能理论基础，使人工智能系统能够在显著降低能源、存储、数据和计算资源需求的同时保持较高性能。因此，高效并非单纯依赖更强大的计算资源，而可以成为更优网络组织方式所带来的自然结果——通过更有意义、更合理的结构实现更高水平的智能。
Cannistraci 教授的研究具有显著的跨学科特征。目前，他担任清华大学脑与智能实验室（THBI）及心理与认知科学系周亚辉讲席教授，同时担任清华大学计算机科学与技术系兼职教授，并与清华大学生物医学工程学院保持学术合作与研究联系。他是清华大学脑与智能实验室复杂网络智能研究中心（Center for Complex Network Intelligence，CCNI）的创始人和主任。CCNI 聚焦信息科学、复杂系统物理学、网络科学与机器智能交叉领域的基础理论和前沿计算方法研究，尤其关注脑启发计算与生命启发计算。相关研究成果从基础理论进一步延伸至精准生物医学、神经科学以及社会与经济系统等应用领域。贯穿这些不同研究方向的核心科学问题始终是：复杂自然系统的组织规律如何揭示新的智能原理，并进一步启发更强大、更具适应性和更加可持续的人工计算模式。
奖励与荣誉
2026年，作为优秀外国专家参加与中国国务院总理的座谈交流活动
2025年，获国家自然科学基金外国资深学者研究基金项目（NSFC RFIS-III）
2025年，获 HICOOL 全球创业大赛三等奖
2021年，获聘清华大学周亚辉冠名教授（Zhou Yahui Chair Professor）
2021年，获国家高层次人才计划（资深科学家）
2019年，获上海市高层次人才计划
2016年，获德国德累斯顿工业大学（Technische Universität Dresden, TUD）物理学青年研究员奖（Young Investigator Award in Physics
代表性论文
人工智能、网络科学与复杂系统物理
1. 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
2. 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.
3. 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.
4. 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.
5. 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
6. Artificial neurons beyond spikes: Neuromorphic systems
CV Cannistraci, E Baek
Nature Electronics 2025
7. 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
8. 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
9. 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)
10. 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)
11. Neuromorphic Dendritic Computation with Silent Synapses for Visual Motion Perception
Eunye Baek, Sen Song, Zong Rong, Luping Shi, Carlo Vittorio Cannistraci
Nature Electronics, 2024
12. 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
13. 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
14. Stealing fire or stacking knowledge’ by machine intelligence to model link prediction in complex networks
Alessandro Muscoloni and Carlo Vittorio Cannistraci
iScience, 2023
15. Geometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes
Carlo Vittorio Cannistraci and Alessandro Muscoloni
Nature Communications, 2022
16. 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
17. 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
18. 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
19. Navigability evaluation of complex networks by greedy routing efficiency
Alessandro Muscoloni & Carlo Vittorio Cannistraci
Proceedings of the National Academy of Sciences, 2019
20. 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
21. Machine learning meets complex networks via coalescent embedding in the hyperbolic space
Alessandro Muscoloni, … , Ginestra Bianconi & Carlo Vittorio Cannistraci
Nature Communications, 2017
22. 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
23. 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
计算生物医学与神经科学
1. 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
2. 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
3. De novo identification of universal cell mechanics regulators
M Urbanska, Y Ge, M Winzi, ..., Carlo Vittorio Cannistraci, Jochen Guck.
Elife, 2025
4. 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
5. 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
6. Proprotein convertase subtilisin/kexin 9 (PCSK9) promotes macrophage activation via LDL receptor-independent mechanisms
Shunsuke Katsuki, ... , Carlo V. Cannistraci, ... , Masanori Aikawa
Circulation Research, 2022
7. 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
8. 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
9. 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
10. A promoter-level mammalian expression atlas
Fantom Consortium (including Carlo Vittorio Cannistraci)
Nature, 2014
11. 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
12. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding
Carlo Vittorio Cannistraci, Gregorio Alanis-Lobato, Timothy Ravasi
Bioinformatics, 2013
13. 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 (共同第一作者)
14. 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
15. An atlas of combinatorial transcriptional regulation in mouse and man
Timoty Ravasi^, Harukazu Suzuki^, Carlo Vittorio Cannistraci^, et al.
Cell, 2010 (共同第一作者)
