Graduate Students

Luca Calzoni

Photo of Luca Calzoni

Biographical Info

Doctoral Candidate, Department of Biomedical Informatics

Research Advisor:
Academic Advisor:


MD (2014, Medicine) Sapienza University of Rome, Italy

MS (2018, Biomedical Informatics) University of Pittsburgh

Career objectives:

My expertise lies at the intersection of Medicine, Computing, and Business: my passion is to help shape the future of clinical research, innovation, and data analytics in healthcare.  As a physician with advanced clinical training, expertise in biomedical informatics and hands-on business experience, I understand the challenges clinicians, researchers, organizations, and entrepreneurs face in the increasingly digitalized healthcare system. I have a proven record of managing healthcare innovation projects from concept to completion, designing research strategies, building relationships, and facilitating the adoption of healthcare technologies. I have worked on a broad spectrum of Public Health and Clinical Informatics projects, including: the development and testing of an EMR system, and the facilitation of its introduction in a healthcare facility; the implementation of an optical mark-recognition system for pain medication monitoring; the development and evaluation of a Learning EMR that uses Machine Learning models to predict and highlight on screen the most relevant data for each patient. I’m open to work in Artificial Intelligence Researcher, User Experience Researcher, Data Scientist, Healthcare Information Technology Consultant and Innovation Consultant roles.


  1. La Torre G, Calzoni L, et al. Management in Sanità. Un Approccio Metodologico [Healthcare Management. A Methodological Approach]. Rome: Senses & Sciences, 2014.
  2. La Torre G, Calzoni L, et al. Smoking Prevention and Cessation. New York: Springer, 2013.
  3. Calzoni L, La Torre G, et al. The INHES Cohort Study on the Health Status of Nurses in Italy: Research Protocol. Ann Ig. 2011;23:387-397.
  4. Tajgardoon M, Samayamuthu MJ, Calzoni L, et al. Patient-Specific Explanations for Predictions of Clinical Outcomes. ACI Open. 2019;3(2):e88-e97.
  5. Calzoni L, Clermont G, Cooper GF, et al. Graphical Presentations of Clinical Data in a Learning Electronic Medical Record. Submitted to Applied Clinical Informatics Journal, accepted for publication.

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