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About

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. Background: PhD in Complex Systems and Non Linear Dynamics (School of Informatics, dept of Engineering of the University of Firenze, Italy) and PhD in (Theoretical) Genetics (University of Pavia, Italy). More information is on my personal homepage Other Affliations: Fellow and member of the Council of Clare Hall College, member of Ellis, the European Lab for Learning & Intelligent Systems, I am member of the Academia Europaea; I am listed in www.topitalianscientists.org/Top_italian_scientists_VIA-Academy.aspx I am happy to receive enquiries for PhD applications. I have successfully completed the equality and diversity essentials. 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 to build predictive models based on the integration of multi scale, multi omics and multi physics data; integrate deep learning and mechanistic approaches; explainability and interpretability in medicine; exploiting short and long range communications in the human body, between cells and tissues and predict emerging mechanistic properties at systemic medicine level. Develop an AI-based medical digital twin to increase self-awareness; Develop an AI personal decision support system to increase social awareness.

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Skills

Experience

Professor

University of Cambridge

Dec-1998 to Present
Visiting Professor

University of Padova (UP)

Dec-2013 to Dec-2018
Research Associate

University of Southampton (US)

Dec-1996 to Dec-1997
Lecturer

University of Florence (UF)

Dec-1988 to Dec-1990

Education

University of Florence (UF)

Ph.D. in Complex Systems and Non Linear Dynamics

Passout Year: 1998
University of Pavia (UP)

Ph.D. in Theoretical Genetics

Passout Year: 1998

Publication

Using AI explainable models and handwriting/drawing tasks for psychological well-being

This study addresses the increasing threat to Psychological Well-Being (PWB) posed by Depression, Anxiety, and Stress conditions. Machine learning methods have shown promising results for se...

Structure-based drug design with equivariant diffusion models

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods leverage stru...

Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learning

Topological signals are variables or features associated with both nodes and edges of a network. Recently, in the context of Topological Machine Learning, great attention has been devoted to...

ABCMB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps

The existence of a cosmic background of primordial gravitational waves (PGWB) is a robust prediction of inflationary cosmology, but it has so far evaded discovery. The most promising avenue ...

AnnoGCD: a generalized category discovery framework for automatic cell type annotation

The identification of cell types in single-cell RNA sequencing (scRNA-seq) data is a critical task in understanding complex biological systems. Traditional supervised machine learning method...

An end-to-end attention-based approach for learning on graphs

There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersede handcraf...

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean spa...

From Charts to Atlas: Merging Latent Spaces into One

Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces. We investigate in this study the aggregation of such latent sp...

Research Article
  • dott image Zohreh Hosseini Nodeh
  • dott image December, 2024

Fate-mapping lymphocyte clones and their progenies from induced antigen-signals identifies temporospatial behaviours of T cells mediating tolerance

Tissue homeostasis is maintained by the behaviours of lymphocyte clones responding to antigenic triggers in the face of pathogen, environmental, and developmental challenges. Current methodo...

A Novel Mixed Convolution Transformer Model for the Fast and Accurate Diagnosis of Glioma Subtypes

Glioblastoma is the most common adult brain tumor, significantly impacts disability and mortality. Early and accurate diagnosis of glioma subtypes is essential, but manual categorization is ...

Projects

Jun-2022 to Present

Ultrafast Holographic FTIR Microscopy

£294k

Funded by Horizon Europe

Trophy (G112412 NRAG/752), Horizon Europe UKRI Underwrite Innovate, Title: Ultrafast Holographic FTIR Microscopy (2022-2026)
Jun-2022 to Present

Chemometric Histopathology via Coherent Raman Imaging for Precision Medicine

£275k

Funded by Horizon Europe

Jun-2018 to Jun-2021

MICA: Mental Health Data Pathfinder

USD 1,995,119

Funded by University of Cambridge

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.
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Conference/Seminar/STTP/FDP/Symposium/Workshop

Conference
  • dott image Jun 2021

7th International Conference on Computational and Mathematical Biomedical Engineering

Hosted By:

Politecnico di Milano ,

Cambridge, Cambridge, United Kingdom
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.
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Workshop
  • dott image Jun 2024

Denoising Probabilistic Diffusion Models for Synthetic Healthcare Image Generation

Hosted By:

IEEE - Institute of Electrical and Electronics Engineers ,

Crete, Greece
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.
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Certificates

Issued : Jun 2018
  • dott image By : BITS
  • dott image Event : BITS Bioinforma...
BITS Bioinformatics Italian Society

Membership

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Member

University of Cambridge

From year 2023 to 2023
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examiner of ACS MPhil

University of Cambridge

From year 2023 to 2023

Invited Position

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DEGAS at GSP Workshop 2023: AI and Medicine: Graph and Hypergraph Representation Learning

IEEE Signal Processing Society

From year 2023 to 2023

https://www.youtube.com/watch?v=IwnwOfcqo2I

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OxML 2023

University of Oxford

From year 2023 to 2023

https://www.oxfordml.school/2023

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The 8th Cambridge-UTokyo Joint Symposium: Parallel Session 3 AI Trends, Opportunities and Threats

West Hub, Cambridge

From year 2023 to 2023

https://sp.t.u-tokyo.ac.jp/UTokyo_Cam/activities/the-8th-cambridge-utokyo-joint-symposium-session-3-opportunities-and-threats/

Honours & Awards

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Best student paper
Awarded by:

AIAI2020

Year: 2020
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Listed in the top Italian Scientists
Awarded by:

Italy

Year: 2019

Doctoral and Master Thesis Guided

Deep concept reasoning: beyond the accuracy-interpretability trade-off
Research Scholar:

Ph.D Thesis (Ph.D. Thesis)

Institute : University of Cambridge

Area of research: Computer Science and Technology