Lorenzo Madeddu

Lorenzo Madeddu

Senior data scientist (R&D), PhD



Lorenzo Madeddu is a senior data scientist (R&D) in the Knowledge Graph Insights team at AstraZeneca (Sweden). He received his PhD from Sapienza University of Rome. His research interests include machine learning, network theory, and network biology. He develops machine learning algorithms in interdisciplinary projects in the fields of healthcare and graph mining.

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  • Machine Learning
  • Bioinformatics
  • Network Biology
  • Network Pharmacology
  • Data Visualization
  • PhD in Innovative Biomedical Technologies in Clinical Medicine, 2022

    Sapienza University of Rome

  • MSc in Computer Science (Grade: 110/110 with honors; GPA 4.00), 2018

    Sapienza University of Rome

  • BSc in Computer Science (Grade: 108/110; GPA 4.00), 2016

    Sapienza University of Rome

Recent Experiences

Senior Data Scientist (R&D)
Sep 2022 – Present Molndal, Sweden
Developed graph mining frameworks for large biomedical knowledge graphs to investigate novel pharmaceutical insights.
UnitelmaSapienza University of Rome
Machine Learning Teacher
Jun 2022 – Aug 2022 Rome, Italy
Recorded 17 Machine Learning lessons.
LUISS Guido Carli
Data Science Teacher
Feb 2022 – May 2022 Rome, Italy
Teaching assistant for the Data Visualization course.
Harvard Medical School, Brigham and Women's Hospital, Loscalzo Lab
Machine Learning Scientist (PhD experience)
Jun 2021 – Sep 2021 Boston, Massachusetts
Tested 6000+ drugs with network pharmacology and machine learning strategies in order to uncover effective treatments for the COVID-19 infection
Network Medicine Institute and Global Alliance
Protein-Protein Interaction Prediction Challenge
Nov 2020 – Sep 2021
Worked with an international team of academics from Harvard University and Sapienza University of Rome, to develop a research challenge for predicting new protein-protein interactions. Co-researcher and I developed a protocol for evaluating, analyzing and testing the methods and results submitted by challenge participants. We worked within the organizing team to provide the requirements and datasets to the research teams (Conference Program) (Video)
Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE)
Machine Learning Scientist in the International AI task-force on COVID-19
Jun 2020 – Jun 2021
  • Released a public bioinformatic dataset on Covid-19 for drug repurposing tasks. The dataset includes protein-protein interactions, drug-target interactions, COVID-19 targets, drug structures and protein functions (GitHub) (Video)
  • Developed a drug repurposing deep learning model for Covid-19 (submitted paper)

Recent Publications

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(2021). Deep Graph Networks for Drug Repurposing with Multi-Protein Targets. Submitted.


(2021). Deep Learning Methods in Network Biology. By World Scientific.

Cite Book Chapter

(2021). Integrating Categorical and Structural Proximity in Disease Ontologies. In EMBC.


Attended & Awards

The foundational concepts of neural networks and deep learning
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womENcourage Hackathon 2019 (Winner)
Hackathon event aimed at bringing participants from diverse technical disciplines together to collaborate on developing a novel solution for the challenge of “Enabling Sustainable Cities Through Blockchain”
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womENcourage 2019 (Volunteer)
Event aimed at bringing participants from diverse technical disciplines to discuss pressing issues of women in the computing profession
DeepLearn Summer School 2019
Research training event about the most recent advances in the critical and fast developing area of deep learning