Prof. Paolo Soda graduated with honours in Biomedical Engineering at Università Campus Bio-Medico (UCBM), Rome, in 2004 and received a Ph.D Biomedical Engineering (Computer Science area) in 2008 from the same University. Currently he is Associate Professor of Computer Science and Computer Engineering at UCBM, and he is co-responsible of the Collaborative Laboratory of Precision Medicine & BioData Analytics between UCBM and Centro Diagnostico Italiano, Milan.
His research interests include artificial intelligence, pattern recognition, machine learning, big data analytics, and data mining applied to data, signal, 2D and 3D image and video processing and analysis. Practical applications of the research activities have impacted on the biomedical applications, with reference to computer-aided diagnosis and decision support systems. 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. His research activity is certified by 120 scientific publications, >1450 overall citations, h-index 20 and an i10-index 43 (Google Scholar data source), further to be co-author of four conference papers receiving an award (IEEE LSC 2018, IEEE BIBM 2018, IEEE ICCI*CC 2019, IEEE CBMS 2021). He led the team winning the competition “All against COVID-19: Screening X-ray Images for COVID-19 Infection”, IEEE, 2021. He is a member of the IEEE, CVPL and SIBIM, and he is chairing the IEEE Technical Committee on Computational Life Sciences.
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
Tortora, M., Cordelli, E., Sicilia, R., …Ramella, S., Soda, P. (2021) Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma. Artificial Intelligence in Medicine, 2021, 119, 102137
Cordelli, E., Soda, P., Iannello, G. (2021) Visual4DTracker: a tool to interact with 3D + t image stacks. BMC Bioinformatics, 2021, 22(1), 53
Sicilia R, Merone M, Valenti V, Soda P (2021). Rule-based Space Characterization for Rumour Detection in Health. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, ISSN: 0952-1976
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.
Soda P, Sicilia R, Acciai L, Iannello G (2019). Grasping inter-attribute and temporal variability in multivariate time series. IEEE TRANSACTIONS ON BIG DATA, ISSN: 2332-7790.
Castiglioni I, Gallivanone F, Soda P, Avanzo M, Stancanello J, Aiello M, Interlenghi M, Salvatore M (2019). AI-based applications in hybrid imaging: how to build smartand truly multi-parametric decision models for radiomics. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
Merone M, Soda P, Sansone M, Sansone C (2019). ECG databases for biometric systems: A systematic review. EXPERT SYSTEMS WITH APPLICATIONS, vol. 67, p. 189-202, ISSN: 0957-4174, doi: http://dx.doi.org/10.1016/j.eswa.2016.09.030
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
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, ISSN: 0957-4174, doi: http://dx.doi.org/10.1016/j.eswa.2016.06.011
Acciai L, Soda P, Iannello G (2016). Automated Neuron Tracing Methods: An Updated Account. NEUROINFORMATICS, vol. 14, p. 353-367, ISSN: 1539-2791, doi: 10.1007/s12021-016-9310-0
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, ISSN: 1367-4803.
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. ISSN: 0278-0062, doi: 10.1109/TMI.2013.2268163.
Onofri L, Soda P, Iannello G. Centromere and cytoplasmic staining pattern recognition: a local approach. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, vol. 51, p. 1305-1314, 2013. ISSN: 0140-0118, doi: 10.1007/s11517-013-1102-1.
Soda P, Iannello G. Aggregation of Classifiers for Staining Pattern Recognition in Antinuclear Autoantibodies Analysis. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, vol. 13, p. 322-329, 2009. ISSN: 1089-7771, doi: 10.1109/TITB.2008.2010855
Soda P, Onofri L, Iannello G. A decision support system for Crithidia Luciliae image classification. ARTIFICIAL INTELLIGENCE IN MEDICINE, vol. 51, p. 67-74, 2011. ISSN: 0933-3657, doi: 10.1016/j.artmed.2010.05.005.
Soda P. A multi-objective optimisation approach for class-imbalance learning. PATTERN RECOGNITION, vol. 44, p. 1801-1810, 2011. ISSN: 0031-3203, doi: 10.1016/j.patcog.2011.01.015.
Soda P, Mazzoleni S, Cavallo G, Guglielmelli G, Iannello G. Human movement onset detection from isometric force/torque measurements: A supervised pattern recognition approach. ARTIFICIAL INTELLIGENCE IN MEDICINE, vol. 50, p. 55-61, 2010. ISSN: 0933-3657, doi: 10.1016/j.artmed.2010.04.008.
Soda P, Iannello G, Vento M. A multiple experts system for classifying fluorescent intensity in antinuclear autoantibodies analysis. PATTERN ANALYSIS & APPLICATIONS, vol. 12, p. 215-226, 2009. ISSN: 1433-755X, doi: 10.1007/s10044-008-0116-z.
Email: p dot soda at unicampus dot it