Prof. Paolo Soda


Paolo Soda is Full Professor of Computer Science and Computer Engineering at University Campus Bio-Medico di Roma, and he is Visiting Professor in AI  Biomedical Engineering at the Department of Radiation Sciences, Umeå University, Sweden.
He is the Research and Third Mission Coordinator of the Department of Engineering at University Campus Bio-Medico di Roma. He is also deputy-coordinator of the Health and Life Sciences specialization area of the National PhD program in AI, for cycles XXXVII and XXXVIII, while he is currently serving as coordinator of the XXXIX cycle.

His research interests include AI, machine learning, and big data analytics, with applications to data, signals, 2D and 3D image and video processing and analysis.
He is member of several Program Committees of international conferences and he serves as referee for several international journals. He has been guest editor of several impacted international journals (e.g. Pattern Recognition, Artificial Intelligence in Medicine, etc.) and, since 2012, he has been associate editor of the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine & Biology Society. From 2018 to 2021 he chaired the Steering Committee of the IEEE International Symposium of Computer-Based Medical Systems. He has been co-PI of several national and international projects.
He was team leader of the research groups that won the two international competitions: “COVID CXR Hackathon” (2022 Dubai Expo) and “All against COVID-19: Screening X-ray Images for COVID-19 Infection” (IEEE 2021). Four research papers he co-authored were awarded at IEEE Life Sciences Conference 2017, IEEE International Conference on Bioinformatics and Biomedicine 2018, IEEE International Conference on Cognitive Informatics & Cognitive Computing 2018, IEEE International Conference on Computer-Based Medical Systems 2021.
His research activity is also certified by >150 scientific publications, >2030 overall citations, h-index 24 (Scopus, November 30, 2023) and an i10-index 57 (Google Scholar, same date).
He is a member of IEEE, CVPL and SIBIM, and he chaired the IEEE International Technical Committee for Computational Life Sciences from 2017 to 2022.


For the full list of publications please follow this Scopus link or this Scholar link. Below there is a short list of the more recent and most significant publications:

  1. Soda, P., D’Amico, N. C., Tessadori, J., Valbusa, G., Guarrasi, V., Bortolotto, C., … & Papa, S. (2021). AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study. MEDICAL IMAGE ANALYSIS, 102216
  2. Guarrasi, V., D’Amico, N.C., Sicilia, R., Cordelli, E., Soda, P. (2021) Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays. PATTERN RECOGNITION, 2022, 121, 108242
  3. Tortora, M., Cordelli, E., Sicilia, R., Miele, M., Matteucci, P., Iannello, G., Ramella, S., Soda, P. (2021) Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma. ARTIFICIAL INTELLIGENCE IN MEDICINE, 2021, 119
  4. Sicilia R, Merone M, Valenti V, Soda P (2021). Rule-based Space Characterization for Rumour Detection in Health. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  5. Sicilia R, Lo Giudice S, Pei Y, Pechenizkiy M, Soda P (2018). Twitter rumour detection in the health domain. EXPERT SYSTEMS WITH APPLICATIONS, vol. 110, p. 33-40, ISSN: 0957-4174
  6. D’Amico NC, Sicilia R, Cordelli E, Tronchin L, Greco C, Michele F, Carnevale A, Iannello G, Ramella S, Soda P (2020). Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest. APPLIED SCIENCES, vol. 10
  7. D’Antoni F, Merone M, Piemonte V, Iannello G, Soda P (2020). Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting. KNOWLEDGE-BASED SYSTEMS, vol. 203
  8. Soda P, Sicilia R, Acciai L, Iannello G (2020). Grasping inter-attribute and temporal variability in multivariate time series. IEEE TRANSACTIONS ON BIG DATA
  9. Merone M, Sansone C, Soda P (2019). A computer-aided diagnosis system for HEp-2 fluorescence intensity classification. ARTIFICIAL INTELLIGENCE IN MEDICINE, vol. 97, p. 71-78
  10. Ramella S, Fiore M, Greco C, Cordelli C, Sicilia R, Merone M, Molfese E, Miele M, Cornacchione P, Ippolito E, Iannello G, D’Angelillo RM, Soda P (2018). A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients. PLOS ONE
  11. Merone M, Pedone C, Capasso G, Antonelli Incalzi R, Soda P (2017). A Decision Support System for Tele-Monitoring COPD-Related Worrisome Events. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 21, p. 296-302
  12. Merone M, Soda P, Sansone M, Sansone C (2017). ECG databases for biometric systems: A systematic review. EXPERT SYSTEMS WITH APPLICATIONS, vol. 67, p. 189-202, ISSN: 0957-4174, doi:
  13. Onofri L, Soda P, Pechenizkiy M, Iannello G (2016). A survey on using domain and contextual knowledge for human activity recognition in video streams. EXPERT SYSTEMS WITH APPLICATIONS, vol. 63, p. 97-111
  14. Acciai L, Soda P, Iannello G (2016). Automated Neuron Tracing Methods: An Updated Account. NEUROINFORMATICS vol. 14, p. 353-367
  15. Silvestri L, Paciscopi M, Soda P, Biamonte F, Iannello G, Frasconi P, Pavone FS (2015). Quantitative neuroanatomy of all Purkinje cells with light sheet microscopy and high-throughput image analysis. FRONTIERS IN NEUROANATOMY, vol. 9, p. 1-11
  16. Frasconi P, Silvestri L, Soda P, Cortini R, Pavone F S, Iannello (2014). Large-Scale Automated Identification of Mouse Brain Cells in Confocal Light Sheet Microscopy Images. BIOINFORMATICS, vol. 30, p. 587-593,
  17. Iannello G, Percannella G, Soda P, Vento M. Mitotic cells recognition in HEp-2 images. PATTERN RECOGNITION LETTERS, vol. 45, p..136-144, 2014.
  18. Foggia P, Percannella G, Soda P, Vento M. Benchmarking HEp-2 Cells Classification Methods. IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 32, p. 1878-1889, 2013


Tel: +3906225419620
Emails: p dot soda at unicampus dot it / paolo dot soda at umu dot se