Pietro Liò

Professor at University of Cambridge
📚 Professor at University of Cambridge | Cambridge, Cambridge, United Kingdom
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S9-122024-1606868
University of Cambridge
University of Pavia (UP)

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220 Publications
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👤 About

Skills & Expertise

Machine Learning Genomics Neural Networks Dynamics Bioinformatics and biostatics Computational Intelligence

Research Interests

Artificial Intelligence Machine Learning Deep Learning Medical Imaging Technology nonlinear dynamics Computer Science Biology Computer Vision Bioinformatics Medicine deep learning Complex Systems AI methodology Geometric deep learning Graph Neural Networks Hypergraphs Neural ODE PDE Latent force models Neural execution of algorithms multimodality Neuroimaging Precision Medicine Computational Biology Personalised Medicine Disease Complexity Modeling Explainable AI Mechanistic Modeling Systems Medicine Multi-Omics Data Integration Medical Digital Twin Decision Support Systems Human Body Communication Theoretical Genetics Nonlinear Dynamics AI in Medicine Predictive Modeling Digital Health Health Informatics Stroke MRI Analysis Real-Time Cancer Imaging Feature Visualization Clinical Decision Support Systems Chronic Disease Prediction Translational Data Science Data Science Algorithms EEG and ECG Analysis Secondary Data Utilization Automated Disease Diagnosis Interpretable Machine Learning

Connect With Me

💼 Experience

Professor

University of Cambridge · December 1998 - Present

Visiting Professor

University of Padova (UP) · December 2013 - December 2018

Research Associate

University of Southampton (US) · December 1996 - December 1997
  • Research Associate in Statistical Epidemiology, Princess Anne Hospital, University of Southampton

Lecturer

University of Florence (UF) · December 1988 - December 1990

🎓 Education

University of Pavia (UP)

Ph.D. in Theoretical Genetics · 1998

University of Florence (UF)

Ph.D. in Complex Systems and Non Linear Dynamics · 1998

🚀 Projects

MICA: Mental Health Data Pathfinder
Agency Name: University of Cambridge || June 2018 - June 2021
Funded: Yes || Amount: USD 1,995,119
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details. Technical Summary With strong NHS partnerships and recent contributions to national mental health (MH) informatics, we shall add novel methods, epidemiology and phenotyping to the MH Platform. We envisage a modular pipeline that de-identifies MH data; supports flexible consent for sharing/contact; and links MH, cognitive, physical, psychosocial and biomarker data. Project (P) 1. Our open-source tools de-identify clinical records to create CPFT’s Research Database, supporting research and participation. We shall extend them to generate anonymised subsets and link data from consenting patients across MH/community services, acute care, and research organizations, including from existing deeply phenotyped longitudinal cohorts. We emphasize rigorous interface standards and NHS governance over identifiable/pseudonymised data. We shall collaborate on a national natural language processing framework, allowing NHS/research organizations to generate structured data from free text. P2. We have created novel open-source neuropsychiatric assessment software. We shall extend it for broad and integrated NHS and research use. This will take automated cognitive testing into routine clinical practice. As a bold but tractable exemplar with research and clinical applications, we shall use it to apply electronic diagnostic algorithms and neuropsychiatric phenotyping, and link these detailed data to clinical records and biomarkers that include immunophenotyping. P3. We shall apply P1 tools to a public health crisis: the premature death of those with serious mental illness. We shall link MH, national and acute Trust data and use machine learning to develop early predictors of mortality. P4. We shall democratize MH research though broad consultation on generic tiered consent models for data-sharing and participation, by giving the research database direct clinical interfaces, and by enhancing data visualization to help clinicians and service users develop research and the NHS improve local services.
🔗 https://app.dimensions.ai/details/grant/grant.7612257
Chemometric Histopathology via Coherent Raman Imaging for Precision Medicine
Agency Name: Horizon Europe || June 2022 - Present
Funded: Yes || Amount: £275k
Ultrafast Holographic FTIR Microscopy
Agency Name: Horizon Europe || June 2022 - Present
Funded: Yes || Amount: £294k
Trophy (G112412 NRAG/752), Horizon Europe UKRI Underwrite Innovate, Title: Ultrafast Holographic FTIR Microscopy (2022-2026)

🎤 Conferences & Seminars (2)

Denoising Probabilistic Diffusion Models for Synthetic Healthcare Image Generation
IEEE - Institute of Electrical and Electronics Engineers · City , Crete, Country · June 2024
Healthcare data are an essential resource in Machine Learning (ML) and Artificial Intelligence (AI) to improve clinical practice, empower patients and enhance drug development with the aim to discover new medical knowledge. In particular, the biomedical imaging analysis plays a important role in the health-care context producing a huge amount of data that can be used to study complex diseases and their evolution in a deeper way or to predict their onsets. In this work we consider an approach based on Denoising Diffusion Probabilistic Models (DDPM) which is a type of generative model that uses a parameterized Markov chain and variational inference to generate synthetic samples that match real data. In particular, we execute a study by training on Malaria images and generating high-quality synthetic samples in order (i) to test the performance of the DDPMs, (ii) to estimate the association between original and synthetic data and (iii) to understand how the natural and human-made environmental factors impact Malaria disease. Finally, we use a well-defined convolutional neural network for classification tasks to assess the DDPM's goodness in generating the synthetic images.
7th International Conference on Computational and Mathematical Biomedical Engineering
Politecnico di Milano · Cambridge, Cambridge, Country · June 2021
EVENTS: 20/10/20 talk at TEDxCambridgeUniversity 23/11/20 talk on AI and Medicine at FuturoRemoto, Naples; 28-30 June 2021: Plenary Lecture at CMBE21 (7th International Conference on Computational and Mathematical Biomedical Engineering); Politecnico di Milano.

🏅 Certificates & Licenses (1)

BITS Bioinformatics Italian Society
Event: BITS Bioinformatics Italian Society · BITS · Issued on June 2018

🏆 Awards & Achievements (2)

🏆 Best student paper
Awarded by: AIAI2020 || Year: 2020
Description
🏆 Listed in the top Italian Scientists
Awarded by: Italy || Year: 2019
Description

👨‍🎓 Thesis Guided (1)

Deep concept reasoning: beyond the accuracy-interpretability trade-off
Name: Ph.D Thesis || Type: Ph.D. Thesis || Status:
Institution: University of Cambridge
Computer Science and Technology

Professional Memberships (2)

University of Cambridge
Member: examiner of ACS MPhil || Join dt: 2023 - 2023
Country: United Kingdom
University of Cambridge
Member: Member || Join dt: 2023 - 2023
Country: United Kingdom

✉️ Invited Position (3)

The 8th Cambridge-UTokyo Joint Symposium: Parallel Session 3 AI Trends, Opportunities and Threats
Organization Name: West Hub, Cambridge || Country: United Kingdom
From 2023 - 2023
🔗 https://sp.t.u-tokyo.ac.jp/UTokyo_Cam/activities/the-8th-cambridge-utokyo-joint-symposium-session-3-opportunities-and-threats/
OxML 2023
Organization Name: University of Oxford || Country: United Kingdom
From 2023 - 2023
🔗 https://www.oxfordml.school/2023
DEGAS at GSP Workshop 2023: AI and Medicine: Graph and Hypergraph Representation Learning
Organization Name: IEEE Signal Processing Society || Country: United Kingdom
From 2023 - 2023
🔗 https://www.youtube.com/watch?v=IwnwOfcqo2I

📚 Publications (220)

Adaptive multi-scale Graph Neural Architecture Search framework
Journal: Neurocomputing • September 2024
Graph neural networks (GNNs) have gained significant attention for their ability to learn representations from graph-structured data, in which message passing and feature fusion strategies play an ess...
Self-simulation and Meta-Model Aggregation Based Heterogeneous Graph Coupled Federated Learning
Journal: IEEE INTERNET OF THINGS JOURNAL • September 2024
A heterogeneous information network (heterogeneous graph) federated learning plays a crucial role in enabling multi-party collaboration in the IoT system. However, due to differences in business and d...
SNDGCN: Robust Android malware detection based on subgraph network and denoising GCN network
Journal: Expert Systems with Applications • September 2024
Android malware seriously affects the use of Android applications, and a growing number of Android malware developers are using adversarial attacks to evade detection by deep learning models. This wor...
Heterogeneous Sheaf Neural Networks
Journal: ArXiv.org • September 2024
Heterogeneous graphs, with nodes and edges of different types, are commonly used to model relational structures in many real-world applications. Standard Graph Neural Networks (GNNs) struggle to proce...
Neural Algorithmic Reasoning with Multiple Correct Solutions
Journal: ArXiv.org • September 2024
Neural Algorithmic Reasoning (NAR) aims to optimize classical algorithms. However, canonical implementations of NAR train neural networks to return only a single solution, even when there are multiple...
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