Sara Moccia received the M.Sc. degree cum laude in Biomedical Engineering from Politecnico di Milano (POLIMI), Italy, in 2014, and the European Ph.D. degree cum laude in Bioengineering from Istituto Italiano di Tecnologia (IIT), Italy, in 2018. During her PhD, she was hosted at the German Cancer Research Center (DKFZ), Germany. She was Postdoctoral Researcher at Università Politecnica delle Marche (UNIVPM) until 2021, when she became Assistant Professor at Scuola Superiore Sant’Anna (SSSA), Italy, where she leads the «Artificial Intelligence for Medical Image Analysis» laboratory, inside the BioRobotics Institute. In 2022, she got the National Scientific Habilitation (ASN) to apply for permanent positions of Associate Professor in Bioengineering.
The aim of her research activities is to unlock the potential of deep learning for the analysis of a wide range of medical images to support clinicians during the actual clinical and surgical procedures.
She received a number of awards for her research, including the "Gruppo Nazionale di Bioingegneria & Patron" Award for her Ph.D. thesis. In 2021, she was granted with a Marie Skłodowska-Curie Actions – Individual Global Fellowship (MSCA-IF-GF) and in 2022 with a L'Oréal Italia for Women and Science in collaboration with the Italian National Commission for UNESCO fellowship. She is currently leading for SSSA two regional projects.
She is author of more than 50 peer-reviewed ISI journal papers, including Medical Image Analysis and IEEE Transactions on Biomedical Engineering. She serves as Associate Editor for IEEE Transactions on Medical Robotics and Bionics, and Medical and Biological Engineering and Computing.
2018-2021: Post-doc Researcher at the Department of Information Engineering of Universtià Politecnica delle Marche (Ancona, Italy) with Prof. Emanuele Frontoni
2018-2021: Affiliated Researcher at the Department of Advanced Robotics of Istituto Italiano di Tecnologia (Genoa, Italy) with Dr. Leonardo S. Mattos
2020: Visiting Researcher at the Department of Industrial Electronics of Universidade do Minho (Braga, Portugal) with Prof. Cristina Manuela Peixoto dos Santos
2018: PhD cum laude in Bioengineering at the Department of Advanced Robotics of Istituto Italiano di Tecnologia (Genoa, Italy) with Dr. Leonardo S. Mattos, and the Department of Electronics, Information and Bioengineering of Politecnico di Milano (Milan, Italy) with Prof. Elena De Momi
2016: Visiting PhD student at the Computer Assisted Medical Intervention lab of the German Cancer Research Center (Heidelberg, Germany) with Prof. Lena Maier-Hein
2014: MSc cum laude in Biomedical Engineering at Politecnico di Milano (Milan, Italy) with Prof. Giuseppe Baselli
1990: Born in Bari, Italy
2022: L'Oréal Italy for Women and Science in collaboration with Italy's National Commission for UNESCO
2021: Athanasiou ABME Award for the work "A review on advances in intra-operative imaging for surgery and therapy: Imagining the operating room of the future" published in Annals of Biomedical Engineering
2020: Joint WG e-Cardiology ESC – CinC: Clinical Needs Translational Award (CTA) for the work "A Novel Approach based on Spatio-temporal Features and Random Forest for Scar Detection using Cine Cardiac Magnetic Resonance Images" presented during Computing in Cardiology (CinC 2020)
2018: "Gruppo Nazionale di Bioingegneria & Patron" Award for the best Ph.D. thesis
2018: Primaga 2018 - Artificial Intelligence applied to the analysis of medical images and videos for the work "Liver-donor steatosis assessment from smartphone images acquired in the OR" presented during the Sixth National Congress of Bioengineering (GNB 2018)
The mission that drives Sara's research is to improve the quality of healthcare in a data-driven manner. Her research goal is to support physicians throughout the entire process of care, from diagnosis to therapy and follow-up, through artificial intelligence methodologies.
Sara's research topics include supervised and semi-supervised deep-learning algorithms, including generative models, for the analysis of:
- Pre- (MRI, CT, US) and intra-operative (RGB, narrow-band and multispectral) images
- RGB-D images acquired in sensitive spaces (e.g., neonatal intensive care units)
- Neural signals
- Electronic health records
Google Scholar: https://scholar.google.it/citations?user=Nc4WOQ4AAAAJ&hl=it