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Information Systems and Applied Computer Sciences

Chair of Explainable Machine Learning

  1.  University of bamberg
  2. Faculties
  3. Information Systems and Applied Computer Sciences
  4. Subject groups
  5. Applied Computer Science
  6. Chair of Explainable Machine Learning
  7. Team
  8. Francesco Di Salvo, M.Sc.

Francesco Di Salvo

[Translate to English:] Francesco Di SalvoTim Kipphan/Uni Bamberg

Teaching and Research Assistant

M.Sc., Doctoral Candidate

Adresse:       An der Weberei 5, 96047 Bamberg
Room:           WE5/04.091

Phone:          +49-951-863 3107

Email:            francesco.di-salvo@uni-bamberg.de

Consultation hour:

during lecture times: Thursday 1 p.m. to 2 p.m. in WE5/04.091 ( notification via Email recommended)
outside lecture times: by appointment

Biography:

Francesco Di Salvo is a PhD candidate at the Otto-Friedrich University of Bamberg, focused in the field of Explainable Machine Learning (xAI) since 2023. He holds a Bachelor of Science in Computer Engineering from the University of Palermo (Italy), and a Master of Science in Data Science and Engineering from the Polytechnic University of Turin (Italy).

His research interests lie in the intersection of model- and data-efficiency, explainability and robustness.

Before joining the xAI lab, between 2022 and 2023, Francesco worked as a research intern at NATO Center for Maritime Research and Experimentation (La Spezia, Italy), as part of the Young Scientist Internship Programme. He designed machine learning and deep learning algorithms to detect anomalies on underwater acoustic data.

Shortly before, he defended his Master's thesis, focused on Bayesian uncertainty on Contrast-Enhanced Breast CT images. The project was conducted in collaboration with the AXTI Lab of the Radboud University Medical Center (Nijmegen, Netherlands).

Furthermore, as a curricular internship, he spent four months as a Deep Learning Engineer Intern in AIKO - Infinite Ways of Autonomy, an Italian startup working on the autonomous space mission industry. He estimated the trajectory of a rover through monocular visual odometry methods, on planetary-like environments.

In his spare time, Francesco volunteered in academic associations, where he conducted monthly tutoring classes of calculus, linear algebra, and programming. He also mentored and coached incoming freshmen for their admission exams. Additionally, he served as a volunteer Machine Learning Engineer for Omdena, where he co-led a team of more than 20 volunteers. They developed machine learning algorithms for quantifying the impact of forest landscape restoration projects.

Outside of his professional activities, Francesco enjoys reading, cooking, and spending time outdoors playing volleyball, hiking, and doing any kind of water sports.

Profiles: Google scholar, ORCID, personal website, GitHub, LinkedIn, X