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Francesca Vitali

  • Assistant Professor, Neurology
  • Member of the Graduate Faculty
  • Assistant Professor, BIO5 Institute
Contact
  • francescavitali@arizona.edu
  • Bio
  • Interests
  • Courses
  • Scholarly Contributions

Biography

Francesca Vitali, PhD is an Assistant Professor (Tenure Track) and the Director of Bioinformatics in the Center for Innovation in Brain Sciences (CIBS).

After completing her Bachelor's Degree and Master's Degree in Biomedical Engineering at the University of Pavia, Dr. Vitali received her PhD in Bioinformatics and Biomedical Engineering from the University of Pavia, Italy. She was a postdoctoral researcher at the Bioinformatics, Mathematical Modelling and Synthetic Biology (BMS) Lab at the University of Pavia under the mentorship of prof. Riccardo Bellazzi. Most recently, she was a Research Assistant Professor for three years in the Lussier Research Group at the University of Arizona.

In her current role at CIBS, she focuses on the development of computational methods to support precision medicine, drug repurposing and poly-pharmacology. Her background includes data mining, statistics, graph theory, machine learning, artificial intelligence and data integration techniques. She has a long record of working in multi­disciplinary collaborative teams, including collaborations with international laboratories and pharmaceutical companies (Sanofi and AstraZeneca). She is the organizer and instructor of a monthly workshop on the use of Orange, an open-source tool for data visualization, machine learning and data mining.

Degrees

  • Ph.D. Bioinformatics and Biomedical Engineering
    • University of Pavia, Pavia, Italy
    • Network-based approaches for multi-target therapies: application to Triple Negative Breast Cancer
  • M.A. Biomedical Engineering
    • University of Pavia, Pavia, Italy
    • A bionformatic method to support polypharmacological approaches
  • B.A. Biomedical Engineering
    • University of Pavia, Pavia, Italy
    • Data import, data conversion and implementation ofan on-line system for the Stroke Unit Network Lombardia

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Interests

Research

Data mining, statistics, graph theory, machine learning, big data and data integration techniques.

Teaching

Artificial Intelligence, data mining, biostatistics, machine learning, biomedical informatics, programming and big data.

Courses

2022-23 Courses

  • Independent Study
    NEUR 599 (Spring 2023)

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Scholarly Contributions

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