- I am Full Professor at the department of Computer Science and
Technology of the University
of Cambridge and I am a member of the Artificial
Intelligence group. I am a member of the
Cambridge Centre for AI in Medicine.
- My research interest focuses on developing Artificial Intelligence and Computational Biology models to understand diseases complexity and address personalised and precision medicine. Current focus is on Graph Neural Network modeling.
- I have a MA from Cambridge, a PhD in
Complex Systems and Non Linear Dynamics (School of
Informatics, dept of Engineering of the University
Firenze, Italy) and a PhD in (Theoretical) Genetics (University
of Pavia, Italy).
- Other Affliations: I am member of - the Integrate
Cancer Medicine Institute, the committee of MPhil
in Computational Biology (Stakeholder Group for the
CCBI) , steering committee of Cambridge
BIG data, VPH-UK
(Virtual Physiologycal Human), Fellow
of Clare Hall College , I am member of Ellis, the
European Lab for Learning & Intelligent Systems.
I am a member of the Academia Europea (https://www.ae-info.org/); I am listed in www.topitalianscientists.org/Top_italian_scientists_VIA-Academy.aspx
Admin: member of Complaint Officer/Examination Review Committee
(Cambridge University); reviewer of MPhils (Newcastle University).
- I am happy to receive enquiries for
PhD applications. I have successfully completed the equality and
Office: FC20; tel: +44 (0)1223-763604; E_mail: Pietro.Lio at cl.cam.ac.uk
Data Integration, cross-modality, evidence synthesis, Machine Learning in medicine
the figure below shows a variety of plots from different papers on deep learning (see at the bottom) focusing on data integration. Data integration is essential to extract all the information, including causality, about a certain subject.
(Various Deep Learning Approaches for data integration: arXiv:1909.06442; arXiv:1907.05628; arXiv:1905.08721; arXiv:1905.06515; arXiv:1905.00534; arXiv:1904.06316; Medical Imaging 2019: Image Processing 10949, 109491L; arXiv:1901.03906; arXiv:1901.03419; BioRxiv, 801605; arXiv:1809.10341; International Conference on Theory and Application of Diagrams, 390-398; Bioinformatics 34 (17), 2944-2950; Nature communications 8 (1), 2045)
Methods Integration: relationship between computational modeling and machine learningThe figure below shows on the right, examples of modeling techniques ( a computational/mathematical model is a representation of the essential aspects of an existing system (or a system to be constructed) which presents knowledge of that system in usable form' (Eykhoff, 1974). On the left there are methodologies, implemented in software, to analyse Big data, showing the ability to automatically learn and improve from experience without being explicitly programmed. At the bottom some relevant publications from the group.
(adapted from Bartocci and Liò, Computational Modeling, Formal Analysis, and Tools for Systems Biology, Plos Computational Biology; also MLCSB 2018: 121-141; NeuroImage 189: 276-287 (2019), CoRR abs/1906.09807 (2019); BMC Bioinformatics 19(1): 439:1-439:18 (2018);CoRR abs/1812.03715 (2018); BMC Bioinformatics 17(S-4): 83 (2016); Mol. BioSyst., 2011, 7, 2796-2803; ACRI 2008: 354-361; Front. Genet., 14 June 2018 | https://doi.org/10.3389/fgene.2018.00206; Bioinformatics 14(8): 726-733 (1998); Fundam. Inform. 171(1-4): 367-392 (2020); BioRxiv, 801605 2019 )
Modeling Social Groups, Policies and Cognitive Behavior in COVID-19 Epidemic Phases. Basic Scenarios (Substantia)
The Computational Patient has Diabetes and a COVID (arxiv)
Forecasting ultra-early intensive care strain from COVID-19 in England (medxiv)
Recent papers accepted at Neurips 2020
On Second Order Behaviour in Augmented Neural ODEs
Path Integral Based Convolution and Pooling for Graph Neural Networks
Principal Neighbourhood Aggregation for Graph Nets
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
Other recent papers:
Petar Veličković, William Fedus, William L. Hamilton,
Pietro Lio', Yoshua Bengio, R Devon Hjelm Deep
Graph Infomax. https://arxiv.org/abs/1809.10341
Haider S. at al. Pathway-Based Subnetworks Enable Cross-Disease Biomarker Discovery. In press on Nature Communications.Editing a Springer Book on Automated Reasoning in Systems Biology and Medicine with Paolo Zuliani
J. Despeyroux, A. Felty, P. Lio’, C. Olarte A Logical Framework for Modelling Breast tumorigenesis. submitted to International Symposium on Molecular Logic and Computational Synthetic Biology.
Hui Xiao, Krzysztof Bartoszek and Pietro Lio'. Multi-omic analysis of signaling factors in inflammatory comorbidities. BMC Bioinformatics (in press)
Ioana Bica, Petar Velickovi ́c, Hui Xiao and Pietro Lio' (2018) Multi-omics data integration using cross-modal neural networks ESANN 2018 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 25-27 April 2018
Di Stefano et al, Social Dynamics Modeling of
Chrono-nutrition. Plos Comput. Biology
Edgar Liberis Petar Velickovic, Pietro Sormanni, Michele
Vendruscolo and Pietro Lio' (2018)
Parapred: Antibody Paratope Prediction using Convolutional
and Recurrent Neural Networks. Bioinformatics 2018
Apr 16. doi: 10.1093/bioinformatics/bty305.
Petar Veličković, Guillem Cucurull, Arantxa Casanova,
Adriana Romero, Pietro Lio', Yoshua Bengio Graph
Attention Networks. accepted at ICLR 2018
Scatà M, Di Stefano A, La Corte A, Lio' P. (2018) Quantifying
the propagation of distress and mental disorders in social
networks. Sci Rep. 2018 Mar 22;8(1):5005. doi:
Duo Wang, Mateja Jamnik, Pietro Lio' (2018) Investigating
diagrammatic reasoning with deep
neural networks. Accepted at Diagrams 2018
Bianchi L, Lio' P. Opportunities
for community awareness platforms in personal genomics and
bioinformatics education. Brief Bioinform. 2017 Nov
1;18(6):1082-1090. doi: 10.1093/bib/bbw078
Awards2018 - BITS Bioinformatics Italian Society- Torino
2016: The paper “Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea” (M. Taffi first author) has won the 2016 BYRA first prize at ISEM (The International Society for Ecological Modelling Global Conference) 2016 and the MCED (Modelling Complex Ecological Dynamics) 2016 Award second prize.
2018 - Visiting professor at the University of Padova.
2013 - Lagrange Fellowship (ISI, Universita' Piemonte Orientale).
2012 - Best Paper (Computing with Metabolic Machines) at Turing 100 in Manchester (first author Claudio Angione)
2011 - 3rd prize awarded by the European Commission (sponsored by ERCIM) for the "Methodological bridges for complex systems" (with E. Merelli and N. Paoletti) at the FET 11, 2011 - Future and Emerging Technologies Conference ('Science Beyond Fiction') conference - Budapest