About
Positions
Full professor Computer Science Nov 2019 -
AnacletoLab - Computational Biology and Bioinformatics, University of Milan
I lead a group of Bioinformatics research (AnacletoLab) at the Department of Computer Science, University of Milano, with researchers having different research profiles, spanning from Artificial Intelligence, Data Analytics and Data Management, till to Molecular Biology. I myself have a hybrid education both in Biology and in Computer Science.
Our main research interest is in the development and application of Machine Learning methods to relevant problems in Medicine and Molecular Biology.
My research group has ongoing collaborations with several research groups in Europe (e.g. Berlin Institute of Health) and USA (e.g. Jackson Lab for Genomic Medicine, CT), and from the next year we will be responsible of the EU-funded Collaborative Doctoral Partnership for an European doctorate in Genomics and Bioinformatics.
Education
University of Genova 2000 - 2003
Field of study: Computer Science
Degree: Ph.D.
University of Genova 1993 - 1999
Field of study: Computer Science
Degree: Master
University of Genova 1977 - 1981
Field of study: Biology
Degree: Master
Skills
Computational Biology, Bioinformatics, Artificial Intelligence, Machine Learning, Programming languages, Application of Artificial Intelligence to Genomic Medicine, Systems Biology.
Professional interests
My professional interests are in the area of Bionformatics research, with a special focus on Machine Learning for Personalized and Precision Medicine.
I am interested in collaborations with bio-medical research groups for common research work and funded projects in the area of Genomic Medicine.
Very schematically my research lines can be summarized as follows:
1. Machine Learning for Personalized Genomic Medicine
2. Machine Learning for biomolecular network analysis, Systems Biology and Network Medicine
3. Big-data analysis in Computational Biology using parallel, distributed and secondary memory-based technologies
4. Machine Learning methods for the prediction of biomolecular function and property of proteins and genes using structured ontologies
5. Machine Learning for the prediction of phenotype/outcome/response to drug in pre-clinical and clinical applications
6. Unsupervised methods for the detection of patterns in biomolecular data.
7. Big data integration in biological and medical domains
CV
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