Biosketch
Rosa Sicilia was born in Cosenza, Italy, in 1993. She graduated with honours in Biomedical Engineering at Università Campus Bio-Medico (UCBM), Rome, in 2016. In 2020 she finished her Ph.D. studies in Biomedical Engineering (Computer Science area) in the same University with a thesis on “Micro-level Rumour Detection on Twitter”. After one year as a Post Doc researcher, Rosa is currently Assistant professor (RTDA) at UCBM, a position co-funded by Regione Lazio to work on the project “We-ease-it: A smart and intelligent outpatient clinic for Hospital 4.0” (more info here). Her main research interests are in the machine learning and multimodal data mining fields. In detail she works on radiomics and radiopathomics, Hospital 4.0, eXplainable AI, automatic detection of rumours in social networks and on multivariate time series analysis. Since the start of her research path, Rosa had the opportunity to participate in international conferences, actively collaborate with other researchers in research projects and article publication on the topics of her work. Rosa has been invited as lecturer to several international conferences, the most recent in 2020, “Exploring Media Ecosystems Conference” at the Massachusetts Institute of Technology (MIT) in Cambridge, MA, USA, with a lecture about “mLevel Rumor Detection on Twitter: Two examples in the health domain”. She also participated as speaker in different international scientific conferences: in 2021 she won the Best Paper Award during the 34th IEEE International Symposium on Computer-Based Medical Systems.
Publications
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 publications:
Guarrasi, V., D’Amico, N. C., Sicilia, R., Cordelli, E., & Soda, P. (2022). Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays. Pattern Recognition, 121, 108242.
Sicilia, R., Merone, M., Valenti, R., & Soda, P. (2021). Rule-based space characterization for rumour detection in health. Engineering Applications of Artificial Intelligence, 105, 104389.
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, 74, 102216.
Tortora, M., Cordelli, E., Sicilia, R., Miele, M., Matteucci, P., Iannello, G., … & Soda, P. (2021). Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma. Artificial Intelligence in Medicine, 119, 102137.
Santucci, D., Faiella, E., Cordelli, E., Sicilia, R., de Felice, C., Zobel, B. B., … & Soda, P. (2021). 3T MRI-Radiomic Approach to Predict for Lymph Node Status in Breast Cancer Patients. Cancers, 13(9), 2228.
Guarrasi, V., D’Amico, N. C., Sicilia, R., Cordelli, E., & Soda, P. (2021, June). A Multi-Expert System to Detect COVID-19 Cases in X-ray Images. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)(pp. 395-400). IEEE.
Liu, C. Z., Sicilia, R., Tortora, M., Cordelli, E., Nibid, L., Sabarese, G., … & Soda, P. (2021, June). Exploring Deep Pathomics in Lung Cancer. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)(pp. 407-412). IEEE.
Sicilia, R., Francini, L., & Soda, P. (2021, June). Representation and Knowledge Transfer for Health-related Rumour Detection. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)(pp. 591-596). IEEE. (Winner of the Best Paper Award)
Francini, L., Soda, P., & Sicilia, R. (2021, June). Describing rumours: a comparative evaluation of two handcrafted representations for rumour detection. In 2021 International Conference on Information and Digital Technologies (IDT) (pp. 311-318). IEEE.
Sicilia R, Cordelli E, Soda P (2021). Categorizing the feature space for two-class imbalance learning. In: 25th International Conference on Pattern Recognition (ICPR), 2020.
Ramella, S., D’Angelillo, R. M., Fiore, M., Greco, C., Ippolito, E., D’Amico, N., … & Soda, P. (2020). 1251P Exploring quantitative biomarkers from different tumour volumes for radiomics in lung cancer. Annals of Oncology, 31, S809.
Prata, F., Esperto, F., Civitella, A., Tuzzolo, P., Cordelli, E., Sicilia, R., … & Papalia, R. (2020). Radiomic analysis of T2 and ADC mpMRI images in the diagnosis of clinical significant prostate cancer: An early experience. European Urology Open Science, 20, S48-S49.
D’Amico NC, Sicilia R, Cordelli E, Tronchin L, Greco C, Fiore M, 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. 2020; 10(18):6425, doi: https://doi.org/10.3390/app10186425.