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Evolving Together: Where Human Potential Meets Artificial Intelligence

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    Grow Your Vision

    Cambridge AI Lab [CAM AI LAB] bridges cutting-edge research and business transformation.

    We help organizations discover how humans and AI can create value together.

    The Challenge We Address

    Every business knows AI is transformative.

    But most struggle with the same questions:

    ❓ How do we integrate AI without disrupting our people?

    ❓ Will AI replace our workforce or empower them?

    ❓ How do we build trust in AI-driven decisions?

     

    We believe these are the wrong questions.

    The real question is: "How can humans and AI evolve together to create unprecedented value?"

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    Our Approach

    Hand Holding Lamp
    Hand Holding Lamp
    Hand Holding Lamp

    Research-Driven Transformation​

     

    We don't just implement AI tools,

    we study how organizations actually adopt and integrate AI. Our methods are grounded in real-world experiments across industries.

    Human-AI Collaboration Design  

     

    We design AI systems that augment human capabilities, not replace them. Our focus is on creating new workflows where humans and machines complement each other.

    Industry-Specific Deep Dive  

     

    We go deep into specific sectors (legal, architecture, logistics), understanding unique challenges and building tailored AI transformation roadmaps.

    For Organizations

    • AI Readiness Assessment

    • Human-AI Workflow Design

    • Custom AI Implementation

    • Change Management Support

    • Industry-Specific AI Solution Packages

    • Digital Transformation Roadmapping

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    For Leaders

    • Executive AI Education

    • World Leading Continue Education

    • AI Strategy Retreats & Workshops

    • Professional Certification Courses

    • AI Investment Decision Support

    • Global AI Policy & Trend Briefings

    • Peer Network Building Events​

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    For Researchers

    • Collaborative Research Projects and Opportunities

    • Joint Grant Proposal Development

    • Academic-Industry Knowledge Exchange Programmes

    • Global AI Research Consortia & International AI Research Networks

    • Co-Development of Open AI Standards & Benchmarks

    • Joint Public Engagement Programmes

    • Open Knowledge Repositories & Toolkits for Emerging Economies

    • AI Education Resource Co-Creation

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    Why Partner With Us

    Led by researchers from top universities, grounded in real-world implementations

    Academic Rigor Meets Business Reality

    We design AI that enhances human potential, not replaces human

    Human-Centered AI Philosophy

    Combining AI, organizational behavior, industry knowledge, and business strategy

    Cross-Disciplinary Expertise

    Successful transformations across multiple sectors with measurable ROI

    Proven Track Record

    News

    Jan-March
    2026

    Looking Ahead to 2026:

    Toward Global AI Equity and Co-Evolution

     

    • Form the “Global AI South Alliance”: Partner with research institutions and enterprises in emerging economies to promote equitable access to AI resources and localized technology adaptation.

    • Deepen Enterprise Co-Evolution Partnerships: Introduce the “AI-Human Co-Evolution Toolkit” to help organizations design human-machine collaborative workflows and cultures.

    • Expand Global Educational Impact: Co-develop the “AI for Everyone” open course series with partner universities and establish the “CAM AI LAB Fellowship” to support young scholars.

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    28
    Dec
    2025

    AI Co-Evolution Pilot Projects

     

    • The lab team visited leading enterprises in retail, energy, and robotics to explore AI implementation scenarios. Collaborative intentions were established with three companies for “AI Co-Evolution Pilot Projects,” focusing on human-machine collaboration solutions.

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    20
    Dec
    2025

    The Evolution of AI Innovation Business Ecosystems

     

    • At the Annual Conference of Technology Innovation Incubators, Dr. Yirui Jiang presented “The Evolution of AI Innovation Ecosystems: From Technological Breakthroughs to Societal Empowerment,” highlighting the importance of open innovation and ecosystem collaboration.

    Image by ZHENYU LUO
    18
    Dec
    2025

    Presented on Harvard Kennedy School Postdoctoral Forum

     

    • Co-Director Dr. Yirui Jiang delivered a keynote speech titled “AI for Business Digital Transformation: A Human-Centric Approach” at the Harvard Kennedy School Postdoctoral Forum, emphasizing that “AI should not replace humans but act as a cognitive collaborator for employees.”

    Image by Elissa Garcia
    11
    Nov
    2025

    CAM AI LAB launched at Cambridge

     

    • The lab was officially launched at the Cambridge, attracting partnerships with leading Cambridge research centers and researchers, joined the collaboration, forming an interdisciplinary coalition of scientists.

    Image by Connor Wang
    1
    Nov
    2025

    Global Network and Collaboration

     

    • The lab formed the “Global AI Network,” uniting scholars from world-class institutions, including Oxford, Stanford, Harvard, MIT, UCLA, Keio University, the University of Tokyo, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the University of Sydney, National University of Singapore, Nanyang Technological University, University of Waterloo, University of Toronto, Tsinghua University, Peking University. The network focuses on cross-cultural, interdisciplinary research on human-AI co-evolution.

    Image by Steve Johnson
    28
    Oct
    2025

    Meeting with Prof. Edison Tse & Researchers in HAI Lab, Stanford

     

    • Meeting with Prof. Edison Tse of Stanford University, focusing on “how AI can expand the boundaries of human thinking,”. Discussion with researchers of Stanford’s Human-Centered AI Institute (HAI). Together,  designing frameworks and courses and books for human-AI co-evolution topic.

    Image by Ian Mackey
    11
    Oct
    2025

    Ideation & Vision - Massive Thanks to Prof. Pietro Liò, Cambridge  

     

    • Inspired and discussion with Prof. Pietro Liò, Cambridge, the CAM AI LAB’s concept took shape. The founding team engaged in deep discussions on “how humans and AI can coexist and happy together in the future,” establishing the core vision: “Evolving Together: Where Human Potential Meets Artificial Intelligence.”

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    Where We Are

    January, 2026, Cambridge, UK

     

    Since its inception in the autumn of 2025, the Cambridge AI Lab (CAM AI LAB) has been committed to a core mission: “Advancing the co-evolution of humans and AI to build a future where technology and humanity thrive together.” In just a few months, the lab has evolved from an interdisciplinary thought experiment into an open collaboration platform connecting leading global academic institutions, industries, and international organizations.

    We are committed!

    Community Publications

    Zuo, K., Jiang, Y., Mo, F., & Lio, P. (2025, April). Kg4diagnosis: A hierarchical multi-agent llm framework with knowledge graph enhancement for medical diagnosis. In AAAI Bridge Program on AI for Medicine and Healthcare (pp. 195-204). Taghizad, N. and Lio, P., 2025 (Published online). A Systematic Review on the Integrating Artificial Intelligence for Enhanced Fault Detection in Power Transmission Systems: A Smart Grid Approach Computing&AI Connect, v. 2 Doi: 10.69709/caic.2024.103229 Singh, V., Khanzadeh, M., Davis, V., Rush, H., Rossi, E., Shrader, J. and Lio’, P., 2025 (Published online). Bayesian Binary Search Algorithms, v. 18 Doi: 10.3390/a18080452 Purificato, A., Cassarà, G., Siciliano, F., Liò, P. and Silvestri, F., 2025 (Published online). Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems ACM Transactions on Recommender Systems, Doi: 10.1145/3742898 Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lio, P., Lepri, B. and Passerini, A., 2025. Explaining the Explainers in Graph Neural Networks: a Comparative Study. ACM Comput. Surv., v. 57 Yan, C., Lu, X., Lio, P. and Hui, P., 2025. EARVP: Efficient Aggregation for Federated Learning With Robustness, Verifiability, and Privacy IEEE Transactions on Information Forensics and Security, v. 20 Doi: 10.1109/TIFS.2025.3576008 Mamalakis, M., Mamalakis, A., Agartz, I., Mørch-Johnsen, LE., Murray, GK., Suckling, J. and Lio, P., 2025. Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks AI Open, v. 6 Doi: http://doi.org/10.1016/j.aiopen.2025.02.001 Wang, R., Tian, Y., Liò, P. and Bianconi, G., 2025. Dirac-equation signal processing: Physics boosts topological machine learning Pnas Nexus, v. 4 Doi: 10.1093/pnasnexus/pgaf139 Prinzi, F., Barbiero, P., Greco, C., Amorese, T., Cordasco, G., Liò, P., Vitabile, S. and Esposito, A., 2025. Using AI explainable models and handwriting/drawing tasks for psychological well-being Information Systems, v. 127 Doi: 10.1016/j.is.2024.102465 Orka, NA., Awal, MA., Liò, P., Pogrebna, G., Ross, AG. and Moni, MA., 2025. Quantum deep learning in neuroinformatics: a systematic review Artificial Intelligence Review, v. 58 Doi: http://doi.org/10.1007/s10462-025-11136-7 Wang, P., Lu, X. and Lio, P., 2025. Research on Privacy Protection Technology of "2+2" Verifiable Federated Learning IEEE Internet of Things Journal, v. 12 Doi: 10.1109/JIOT.2025.3554155 Yan, C., Lu, X., Lio, P., Hui, P. and He, D., 2025. Self-Simulation and Meta-Model Aggregation-Based Heterogeneous-Graph-Coupled Federated Learning IEEE Internet of Things Journal, v. 12 Doi: 10.1109/JIOT.2024.3462724 Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lio, P., Lepri, B. and Passerini, A., 2025. Explaining the Explainers in Graph Neural Networks: a Comparative Study ACM Computing Surveys, v. 57 Doi: 10.1145/3696444 Zahoor, S., Liò, P., Dias, G. and Hasanuzzaman, M., 2025. Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data. CoRR, v. abs/2501.15973 Bi, X., Tang, S., Xiao, B., Li, W., Gao, X. and Liò, P., 2025. A Systematic Review of Heart Sound Detection Algorithms: Experimental Results and Insights IEEE Transactions on Instrumentation and Measurement, v. 74 Doi: http://doi.org/10.1109/TIM.2025.3547082 Hasan, MDM., Abdar, M., Khosravi, A., Aickelin, U., Lio, P., Hossain, I., Rahman, A. and Nahavandi, S., 2025. Survey on Leveraging Uncertainty Estimation Toward Trustworthy Deep Neural Networks: The Case of Reject Option and Post-Training Processing ACM Computing Surveys, v. 57 Doi: 10.1145/3727633 Li, K., Zheng, J., Ni, W., Huang, H., Lio, P., Dressler, F. and Akan, OB., 2025. Biasing Federated Learning with a New Adversarial Graph Attention Network IEEE Transactions on Mobile Computing, v. 24 Doi: http://doi.org/10.1109/TMC.2024.3499371 Raisa, RA., Rodela, AS., Abu Yousuf, M., Azad, A., Alyami, SA., Lio, P., Islam, MZ., Pogrebna, G. and Moni, MA., 2025. Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study (vol 12, pg 122959, 2024) IEEE ACCESS, v. 13 Doi: 10.1109/ACCESS.2025.3551391 Bali, A., Wolter, S., Pelzel, D., Weyer, U., Azevedo, T., Lio, P., Kouka, M., Geißler, K., Bitter, T., Ernst, G., Xylander, A., Ziller, N., Mühlig, A., von Eggeling, F., Guntinas-Lichius, O. and Pertzborn, D., 2025. Real-Time Intraoperative Decision-Making in Head and Neck Tumor Surgery: A Histopathologically Grounded Hyperspectral Imaging and Deep Learning Approach Cancers, v. 17 Doi: 10.3390/cancers17101617 Nobel, SMN., Swapno, SMMR., Islam, MB., Azad, AKM., Alyami, SA., Alamin, M., Liò, P. and Moni, MA., 2025. A Novel Mixed Convolution Transformer Model for the Fast and Accurate Diagnosis of Glioma Subtypes Advanced Intelligent Systems, v. 7 Doi: 10.1002/aisy.202400566 Crowley, R., Parkin, K., Rocheteau, E., Massou, E., Friedmann, Y., John, A., Sippy, R., Liò, P. and Moore, A., 2025. Machine learning for prediction of childhood mental health problems in social care. BJPsych Open, v. 11 Doi: http://doi.org/10.1192/bjo.2025.32 Telyatnikov, L., Bucarelli, MS., Bernardez, G., Zaghen, O., Scardapane, S. and Liò, P., 2025. Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design Transactions on Machine Learning Research, v. 2025-February Aucello, R., Pernice, S., Tortarolo, D., Calogero, RA., Herrera-Rincon, C., Ronchi, G., Geuna, S., Cordero, F., Lió, P. and Beccuti, M., 2025. UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale. Bioinformatics, v. 41 Doi: http://doi.org/10.1093/bioinformatics/btaf103 El, B., Choudhury, D., Liò, P. and Joshi, CK., 2025. Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs. CoRR, v. abs/2502.12352 Minelli, A., Meloni, A., Bortolomasi, M., Pisanu, C., Zampieri, E., Congiu, D., Lana, B., Manchia, M., Meattini, M., Paribello, P., Baune, BT., Serretti, A., Dierssen, M., Maron, E., Potier, MC., Gennarelli, M., van Westrhenen, R., Squassina, A., Stacey, D., Mehta, D., Janzing, JGE., Ebert, B., Fabbri, C., Lio’, P. and Rybakowski, F., 2025. Telomere length and mitochondrial DNA copy number in association with trauma-focused psychotherapy efficacy Neuroscience Applied, v. 4 Doi: http://doi.org/10.1016/j.nsa.2024.104095 Lomoio, U., Veltri, P., Guzzi, PH. and Liò, P., 2025. Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution. Artif Intell Med, v. 160 Doi: http://doi.org/10.1016/j.artmed.2024.103058 Sun, B. and Liò, P., 2025. EU-Nets: Enhanced, Explainable and Parsimonious U-Nets. CoRR, v. abs/2502.18122 Ali, R., Caso, F., Irwin, C. and Liò, P., 2025. Entropy-Lens: The Information Signature of Transformer Computations. CoRR, v. abs/2502.16570 Fiore, P., Terlizzi, A., Bardozzo, F., Liò, P. and Tagliaferri, R., 2025. Advancing label-free cell classification with connectome-inspired explainable models and a novel LIVECell-CLS dataset Computers in Biology and Medicine, v. 192 Doi: http://doi.org/10.1016/j.compbiomed.2025.110274 Sun, B. and Liò, P., 2025. Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers. CoRR, v. abs/2501.01311 Bergna, R., Calvo-Ordoñez, S., Opolka, FL., Liò, P. and Hernandez-Lobato, JM., 2025. UNCERTAINTY MODELING IN GRAPH NEURAL NETWORKS VIA STOCHASTIC DIFFERENTIAL EQUATIONS 13th International Conference on Learning Representations Iclr 2025, Carli, F., Di Chiaro, P., Morelli, M., Arora, C., Bisceglia, L., De Oliveira Rosa, N., Cortesi, A., Franceschi, S., Lessi, F., Di Stefano, AL., Santonocito, OS., Pasqualetti, F., Aretini, P., Miglionico, P., Diaferia, GR., Giannotti, F., Liò, P., Duran-Frigola, M., Mazzanti, CM., Natoli, G. and Raimondi, F., 2025. Learning and actioning general principles of cancer cell drug sensitivity. Nat Commun, v. 16 Doi: http://doi.org/10.1038/s41467-025-56827-5 Lee, CK., Jeha, P., Frellsen, J., Lio, P., Albergo, MS. and Vargas, F., 2025. Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo. CoRR, v. abs/2502.06079 Buterez, D., Janet, JP., Oglic, D. and Liò, P., 2025. An end-to-end attention-based approach for learning on graphs Nature Communications, v. 16 Doi: 10.1038/s41467-025-60252-z Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Lió, P., 2024 (Published online). Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting Nature Communications, v. 15 Doi: 10.1038/s41467-024-45566-8 Thaventhiran, J., 2024 (Accepted for publication). Intratumoral antigen signaling traps CD8+ T cells to confine exhaustion to the tumor site Science Immunology, Doi: 10.1126/sciimmunol.ade2094 Rathod, S., Lio, P. and Zhang, X., 2024. Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes. CoRR, v. abs/2410.14426 Liu, L., Cheng, Y., Deng, Z., Wang, S., Chen, D., Hu, X., Liò, P., Schönlieb, CB. and Aviles-Rivero, A., 2024. TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios Mm 2024 Proceedings of the 32nd ACM International Conference on Multimedia, Doi: 10.1145/3664647.3681153 Gantz, M., Mathis, SV., Nintzel, FEH., Lio, P. and Hollfelder, F., 2024. On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering. Faraday Discuss, v. 252 Doi: 10.1039/d4fd00065j Zuo, K., Jiang, Y., Mo, F. and Lio, P., 2024. KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis. CoRR, v. abs/2412.16833 Mamalakis, M., Mamalakis, A., Agartz, I., Mørch-Johnsen, LE., Murray, GK., Suckling, J. and Lio, P., 2024. Solving the enigma: Deriving optimal explanations of deep networks. CoRR, v. abs/2405.10008 Ali, R., Kulyte, P., Sáez de Ocáriz Borde, H. and Liò, P., 2024. Metric Learning for Clifford Group Equivariant Neural Networks Proceedings of Machine Learning Research, v. 251 Kujawa, Z., Poole, J., Georgiev, D., Numeroso, D. and Liò, P., 2024. Neural Algorithmic Reasoning with Multiple Correct Solutions. CoRR, v. abs/2409.06953 Bardozzo, F., Terlizzi, A., Simoncini, C., Lió, P. and Tagliaferri, R., 2024. Elegans-AI: How the connectome of a living organism could model artificial neural networks Neurocomputing, v. 584 Doi: 10.1016/j.neucom.2024.127598 Du, Y., Jamasb, AR., Guo, J., Fu, T., Harris, C., Wang, Y., Duan, C., Liò, P., Schwaller, P. and Blundell, TL., 2024. Machine learning-aided generative molecular design Nature Machine Intelligence, v. 6 Doi: 10.1038/s42256-024-00843-5 Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2024. GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data Transactions on Machine Learning Research, v. 2024 Yang, L., Liò, P., Shen, X., Zhang, Y. and Peng, C., 2024. Adaptive multi-scale Graph Neural Architecture Search framework Neurocomputing, v. 599 Doi: 10.1016/j.neucom.2024.128094 Caralt, FH., Gil, GB., Duta, I., Liò, P. and Cot, EA., 2024. Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks Proceedings of Machine Learning Research, v. 251 Braithwaite, L., Duta, I. and Liò, P., 2024. Heterogeneous Sheaf Neural Networks. CoRR, v. abs/2409.08036 Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Lió, P., 2024. Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting. Nat Commun, v. 15 Doi: 10.1038/s41467-024-45566-8 Li, M., Micheli, A., Wang, YG., Pan, S., Lio, P., Gnecco, GS. and Sanguineti, M., 2024. Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications IEEE Transactions on Neural Networks and Learning Systems, v. 35 Doi: 10.1109/TNNLS.2024.3371592 Wang, Z., Ma, J., Gao, Q., Bain, C., Imoto, S., Liò, P., Cai, H., Chen, H. and Song, J., 2024. Dual-stream multi-dependency graph neural network enables precise cancer survival analysis. Med Image Anal, v. 97 Doi: 10.1016/j.media.2024.103252 Kulyte, P., Vargas, F., Mathis, SV., Wang, YG., Hernández-Lobato, JM. and Liò, P., 2024. Improving Antibody Design with Force-Guided Sampling in Diffusion Models. CoRR, v. abs/2406.05832 Su, S., Duta, I., Magister, LC. and Liò, P., 2024. Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts. CoRR, v. abs/2410.07764 Mumenin, N., Yousuf, MA., Nashiry, MA., Azad, AKM., Alyami, SA., Lio', P. and Moni, MA., 2024. ASDNet: A robust involution-based architecture for diagnosis of autism spectrum disorder utilising eye-tracking technology Iet Computer Vision, v. 18 Doi: 10.1049/cvi2.12271 Somathilaka, S., Ratwatte, A., Balasubramaniam, S., Vuran, MC., Srisa-an, W. and Liò, P., 2024. Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity. CoRR, v. abs/2403.08549 Moss, J., England, J. and Lió, P., 2024. Deep Kernel Learning of Nonlinear Latent Force Models Transactions on Machine Learning Research, v. 2024 Huang, K., Wang, YG., Li, M. and Liò, P., 2024. How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing Proceedings of Machine Learning Research, v. 235 Sabari, A., Hasan, I., Alyami, SA., Liò, P., Ali, MS., Moni, MA. and Azad, AKM., 2024. LandSin: A differential ML and google API-enabled web server for real-time land insights and beyond[Formula presented] Software Impacts, v. 22 Doi: http://doi.org/10.1016/j.simpa.2024.100718 Georgiev, D., Wilson, JJ., Buffelli, D. and Liò, P., 2024. Deep Equilibrium Algorithmic Reasoning Advances in Neural Information Processing Systems, v. 37 Dong, T., Jamnik, M. and Liò, P., 2024. Sphere Neural-Networks for Rational Reasoning. CoRR, v. abs/2403.15297 Raisa, RA., Rodela, AS., Yousuf, MA., Azad, A., Alyami, SA., Lio, P., Islam, MZ., Pogrebna, G. and Moni, MA., 2024. Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study IEEE Access, v. 12 Doi: 10.1109/ACCESS.2024.3426928 Lope, EGD., Deshpande, S., Torné, RV., Liò, P., Glaab, E. and Bordas, SPA., 2024. Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease. CoRR, v. abs/2406.14442 Schneuing, A., Harris, C., Du, Y., Didi, K., Jamasb, A., Igashov, I., Du, W., Gomes, C., Blundell, TL., Lio, P., Welling, M., Bronstein, M. and Correia, B., 2024. Structure-based drug design with equivariant diffusion models. Nat Comput Sci, v. 4 Doi: http://doi.org/10.1038/s43588-024-00737-x Defilippo, A., Veltri, P., Lió, P. and Guzzi, PH., 2024. Leveraging graph neural networks for supporting automatic triage of patients. Sci Rep, v. 14 Doi: 10.1038/s41598-024-63376-2 Rowbottom, J., Maierhofer, G., Deveney, T., Schratz, K., Liò, P., Schönlieb, C-B. and Budd, CJ., 2024. G-Adaptive mesh refinement - leveraging graph neural networks and differentiable finite element solvers. CoRR, v. abs/2407.04516 Buterez, D., Janet, JP., Oglic, D. and Lio, P., 2024. Masked Attention is All You Need for Graphs. CoRR, v. abs/2402.10793 Zaki, JK., Tomasik, J., McCune, JA., Bahn, S., Liò, P. and Scherman, OA., 2024. Explainable Deep Learning Framework for SERS Bio-quantification. CoRR, v. abs/2411.08082 Zhao, X., Li, Z., Shen, M., Stan, G-B., Liò, P. and Zhao, Y., 2024. Enhancing Real-World Complex Network Representations with Hyperedge Augmentation. CoRR, v. abs/2402.13033 Jamasb, AR., Morehead, A., Joshi, CK., Zhang, Z., Didi, K., Mathis, S., Harris, C., Tang, J., Cheng, J., Liò, P. and Blundell, TL., 2024. Evaluating Representation Learning on the Protein Structure Universe. ArXiv, Zhu, M., Bazaga, A. and Liò, P., 2024. FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models. CoRR, v. abs/2406.04501 Ceccarelli, F., Liò, P. and Holden, SB., 2024. AnnoGCD: a generalized category discovery framework for automatic cell type annotation. NAR Genom Bioinform, v. 6 Doi: http://doi.org/10.1093/nargab/lqae166 Bazaga, A., Liò, P. and Micklem, G., 2024. HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs Emnlp 2024 2024 Conference on Empirical Methods in Natural Language Processing Findings of Emnlp 2024, Doi: 10.18653/v1/2024.findings-emnlp.537 Georgiev, D., Wilson, JJ., Buffelli, D. and Liò, P., 2024. Deep Equilibrium Algorithmic Reasoning. CoRR, v. abs/2410.15059 Lu, X., Zhao, J., Zhu, S. and Lio, P., 2024. SNDGCN: Robust Android malware detection based on subgraph network and denoising GCN network Expert Systems with Applications, v. 250 Doi: 10.1016/j.eswa.2024.123922 Zhou, B., Zheng, L., Wu, B., Yi, K., Zhong, B., Tan, Y., Liu, Q., Liò, P. and Hong, L., 2024. A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity. Cell Discov, v. 10 Doi: 10.1038/s41421-024-00728-2 Bi, X., Yang, Z., Liu, B., Cun, X., Pun, C-M., Lio, P. and Xiao, B., 2024. ZeroPur: Succinct Training-Free Adversarial Purification. CoRR, v. abs/2406.03143 Yi, K., Fan, Y., Hamann, J., Liò, P. and Wang, YG., 2024. ABCMB: deep delensing assisted likelihood-free inference from CMB polarization maps Machine Learning Science and Technology, v. 5 Doi: 10.1088/2632-2153/ad9af9 Barucci, A., Ciacci, G., Liò, P., Azevedo, T., Di Cencio, A., Merella, M., Bianucci, G., Bosio, G., Casati, S. and Collareta, A., 2024. An explainable Convolutional Neural Network approach to fossil shark tooth identification Bollettino Della Societa Paleontologica Italiana, v. 63 Doi: 10.4435/BSPI.2024.15 Oliva, V., Martone, A., Fanelli, G., Domschke, K., Minelli, A., Gennarelli, M., Martini, P., Bortolomasi, M., Maron, E., Squassina, A., Pisanu, C., Kasper, S., Zohar, J., Souery, D., Montgomery, S., Albani, D., Forloni, G., Ferentinos, P., Rujescu, D., Mendlewicz, J., De Ronchi, D., Baune, BT., Potier, MC., van Westrhenen, R., Rybakowski, F., Mehta, D., Dierssen, M., Janzing, JGE., Liò, P., Serretti, A. and Fabbri, C., 2024. 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