About
I am a very curious person. I am interested in science and all its shades. I have studied Computer Science and Artificial Intelligence and I want to focus on their application to various research fields.
Artificial Intelligence PhD Student
I am a Ph.D. student in Artificial Intelligence under the supervision of prof. Roberto Capobianco. I am interested in the application of AI to different scientific fields and I think that Explainable AI could play an essential role for this purpose.
My Ph.D. project consists in developing model-specific XAI methods, using a topology-based approach, in order to identify and learn data representations able to provide human-interpretable explanations of the neural network predictions.I have an M.Sc. degree in AI & Robotics and a B.Sc. degree in Computer and Control Engineering.
I have also some background in the application of AI to chemistry and drug discovery gained through abroad experience at the University of North Carolina and collaborations with the Pharmaceutical Chemistry and Technology Department at the Sapienza University of Rome.
- Birthday: 24 July 1997
- Website: alessio.ragno.info
- City: Rome, Italy
- Degree: Master's Degree in Artificial Intelligence & Robotics
- Email: ragno@diag.uniroma1.it
- Email: alessio.ragno@uniroma1.it
- Institution: Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome
Skills
Resume
I am interested with all the application of Artificial Intelligence. In particular, my research is focused on the role of Explainable AI, which I see as a promising research field that could bring huge improvements in different applications.
I am also interested in Reinforcement Learnign and in the application of AI to Pharmaceutical Chemistry and Drug Design.
Education
PhD in Artificial Intelligence
2021 - Present
Sapienza University of Rome, IT
Research Project: Topology-based Explanations for Neural Networks
Master's Degree in Artificial Intelligence & Robotics
2019 - 2021
Sapienza University of Rome, IT
Final Mark: 110/110 cum Laude.
Selected Student for the Excellence Programme.
Best Student according to Sapienza “Exam Bonus” Award.
Thesis on Explainable AI: “Explainable AI in Drug Design: Perturbation based molecular attributions using Graph Convolutional Networks”.
Bachelor's Degree in Computer and Control Engineering
2016 - 2019
Sapienza University of Rome, IT
Final Mark: 110/110 cum Laude.
Selected Student for the Excellence Programme.
Best Student of the third year according to Sapienza “Exam Bonus” Award.
Thesis on Reinforcement Learning: ”Deep Deterministic Policy Gradient for Regularity Rally in TORCS Simulator”.
Professional Experience
Machine Learning Engineer
2016 - Present
Rome Center for Molecular Design, Sapienza University of Rome, IT
Participation with the research group developing web apps and developing Machine Learning models for Pharmaceutical Chemistry.
High School Teacher
2021
“Enrico Fermi“ High School
Math high school substitute teacher.
Visiting Scholar
2019
Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC
Application of Machine Learning to the field of Pharmaceutical Chemistry for Drug Discovery.
Junior Scholarship
2019
Department of Computer, Control and Management Engineering “Antonio Ruberti“, Sapienza University of Rome, IT
Analysis of Scientific Production through Google Scholar and Scopus.
Scientific Production
Prototype-based Interpretable Graph Neural Networks
2022 - IEEE Transactions on Artificial Intelligence
Ragno, A.; La Rosa, B.; Capobianco, R.
10.1109/TAI.2022.3222618
Explainable AI in drug design: self-interpretable graph neural network for molecular property prediction using concept whitening
2022 - 3rd Molecules Medicinal Chemistry Symposium: Shaping Medicinal Chemistry for the New Decade
Proietti, M; Ragno, A.; Capobianco, R.
Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar. com portal
2022 - Journal of Computer-Aided Molecular Design
Proia, E.; Ragno, A.; Antonini, L.; Sabatino, M.; Mladenovič, M.; Capobianco, R.; Ragno, R.
10.1007/s10822-022-00460-7
Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils
2021 - Molecules
Ragno, A.; Baldisserotto, A.; Antonini, L.; Sabatino, M.; Sapienza, F.; Baldini, E.; Buzzi, R.; Vertuani, S.; Manfredini, S.
10.3390/molecules26206279
Semi-Supervised GCN for learning Molecular Structure-Activity Relationships
2021 - ELLIS Machine Learning for Molecules Workshop (ML4Molecules)
Ragno, A.; Savoia, D.; Capobianco, R.
arXiv:2202.05704
Molecule Generation from Input-Attributions over Graph Convolutional Networks
2021 - ELLIS Machine Learning for Molecules Workshop (ML4Molecules)
Savoia, D.; Ragno, A.; Capobianco, R.
arXiv:2202.05703
Services
AI Engineer and Developer Consultant
I provide counseling in different applications of Computer Science, Machine Learning and Artificial Intelligence