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About me
I am currently Assistant Professor in the Department of Computer Science at the University of Pisa.
Previously, I was a Visiting Scientist in the Data Science and Computational Vaccinology Team at GSK, where I study predictive models for Reverse Vaccinology.
I got my Ph.D. in Computer Science at the Department of Computer Science, University of Pisa, within the Computational Intelligence & Machine Learning Group. My supervisors were Davide Bacciu and Alessio Micheli.
My research interests are Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, Generative Models. At the moment, I am focusing on how to learn from graph data, whether the objective is to predict something useful with them or to generate novel graphs with desired properties.
My research in Machine Learning finds application for the most part in the biomedical field: from prenatal care to drug design/discovery, to biological pathways.
Contacts
marco.podda AT unipi.it
Room 345
Dipartimento di Informatica
Largo Bruno Pontecorvo, 3
56127, Pisa (PI) - Italy
Ph.D. Thesis
M. Podda
Deep Learning on Graphs with Applications to the Life Sciences
Publications
M. Podda, D. Bacciu, A. Micheli, R. Bellù, G. Placidi, L. Gagliardi
Sci Rep 8, 13743 (2018) doi:10.1038/s41598-018-31920-6
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F. Errica, M. Podda, D. Bacciu, A. Micheli
A Fair Comparison of Graph Neural Networks for Graph Classification
8th International Conference on Learning Representations (ICLR), 2020
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M. Podda, D. Bacciu, A. Micheli
A Deep Generative Model for Fragment-Based Molecule Generation
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 108, 2240-2250, 2020
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D. Bacciu, F. Errica, A. Micheli, M. Podda
A Gentle Introduction to Deep Learning for Graphs
Neural Networks, Volume 129, 2020, Pages 203-221.
ISSN 0893-6080, doi:10.1016/j.neunet.2020.06.006.
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P. Bove, P. Milazzo, A. Micheli, M. Podda
Prediction of dynamical properties of biochemical pathways with Graph Neural Networks
Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies -
Volume 3: BIOINFORMATICS, ISBN 978-989-758-398-8, pages 32-43. doi:10.5220/0008964700320043 (2020)
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D. Bacciu, A. Micheli, M. Podda
Edge-based sequential graph generation with recurrent neural networks
Neurocomputing, 2020. doi:10.1016/j.neucom.2019.11.112
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D. Bacciu, A. Micheli, M. Podda
Graph generation by sequential edge prediction
ESANN 2019 Proceedings, pages 95-100, ISBN 978-287-587-065-0.
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M. Podda, D. Bacciu, A. Micheli, P. Milazzo
Biochemical Pathway Robustness Prediction with Graph Neural Networks
ESANN 2020 Proceedings, pages 121-126, ISBN 978-2-87587-074-2.
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M. Podda, P. Bove, A. Micheli, P. Milazzo
Classification of Biochemical Pathway Robustness with Neural Networks for Graphs
Biomedical Engineering Systems and Technologies, 2021. doi: 10.1007/978-3-030-72379-8_11
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M. Podda, D. Bacciu
Graphgen-redux: a Fast and Lightweight Recurrent Model for Labeled Graph Generation
International Joint Conference on Neural Networks, 2021. doi: 10.1109/IJCNN52387.2021.9533743
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M. Podda, A. Micheli
Deep Learning in Cheminformatics
Deep Learning in Biology and Medicine, 2022. doi: 10.1142/9781800610941_0006