• Congratulations to Iacopo Colonelli: one of his papers (from his PhD thesis) has been selected by the journal Future Generation Computer Systems as Editor's Choice. These papers are promotionally Open Access for 6 months. 
• Congratulations to Gianluca Mittone: the presentation Efficiently Distributed Machine Learning has been awarded as “Best PhD Symposium Award” at 29-th International European Conference on Parallel and Distributed Computing (Cyprus).  
• Congratulations to Bruno Casella for a merit mention for the Computer and Digital Science area of the "Galielo Galilei International Prize for the Promotion of Humanistic and Scientific Research Reserved for Young Scholars" , sponsored and offered by Rotary International District 2031 in collaboration with the Galileo Galilei Prize Foundation. 
• Congratulations to Matteo Salis: the presentation What's Behind This Water Table Depth Forecasting? RISE Application for Spatial, Temporal, and Spatio-Temporal Explanations has been awarded as “Best AI4CC 2024 Paper Award” at 1st International Workshop on Artificial Intelligence for Climate Change (bergamo)

• Marco Edoardo Santimaria, Samuele Fonio, G. Malenza, I. Colonnelli, M. Aldinucci (2024) Benchmarking Parallelization Models through Karmarkar's Interior-point method.32nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE. 10.1109/PDP62718.2024.00010

• Lorenzo Brescia, M. Aldinucci (2024). Secure Generic Remote Workflow Execution with TEEs. In: Proceedings of the 2nd Workshop on Workflows in Distributed Environments. 8-13. https://dl.acm.org/doi/10.1145/3642978.3652834
• F. Ascolani, Ylenia F. Buttigliero, M. Ruggiero (2024), Non-informative priors in Wright–Fisher smoothing. In: Methodological and Applied Statistics and Demography III SIS 2024, Short Papers, Contributed Sessions 1, 93-97. https://link.springer.com/chapter/10.1007/978-3-031-64431-3_16

• G. Ammirata, M. Arigoni, D. Licastro, G.P. Caviglia, M. Disabato, Ghania Zubair, C. Bezzio, S. Saibeni, A. De Nicolò, J. Cusato, A. Palermiti, A. Manca, E. Tolosano, S. Cozzini, M. Mancini, F. Altruda, A. D’Avolio, D. G. Ribaldone, U. Ala, S. Fagoonee (2024). Extracellular Vesicle-Enclosed Oxidative Stress-and Inflammation-Related microRNAs as Potential Biomarkers of Vitamin D Responsivity: A Pilot Study on Inflammatory Bowel Disease Patients with or without COVID-19. Antioxidants, 13(9), 1047. 10.3390/antiox13091047
• Marco Dalla Pria, S. Montagna (2023) The Hierarchical Beta-Bernoulli Process as Out-of-Scope Query Detector. Book of Short Papers, SIS 2023 - Statistical Learning, Sustainability and Impact Evaluation. Pearson, 2023, ISBN 9788891935618AAVV, 560-565
• Marco Dalla Pria, M. Ruggiero, D. Spanò (2025) Exact sampling of two coagulated partitions. Methodological and Applied Statistics and Demography I - SIS 2024, Short Papers, Plenary and Specialized Sessions. Springer Cham, 2024, ISBN 978-3-031-64346-0
• Samuele Fonio. (2023) Benchmarking Federated Learning Frameworks for Medical Imaging Tasks. International Conference on Image Analysis and Processing. Cham: Springer Nature Switzerland, 2023. 10.1007/978-3-031-51026-7_20
• Samuele Fonio, Lorenzo Paletto, M. Cerrato, D. Ienco, R. Esposito (2023) Hierarchical priors for Hyperspherical Prototypical Networks. ESANN 2023-Proceedings. ESANN, 459-464. 10.14428/esann/2023.ES2023-65
• Samuele Fonio, M. Polato, R. Esposito. (2024) Fedhp: Federated learning with hyperspherical prototypical regularization. ESANN 2024 proceedings. i6doc. 69-74. 10.14428/esann/2024.ES2024-183
• Samuele Fonio, R. Esposito, M. Aldinucci. Hyperbolic Prototypical Entailment Cones for Image Classification. The 28th International Conference on Artificial Intelligence and Statistics (in print).

• Alessandro Pansa, I. Butera, J.J. Gómez-Hernández, B. Vigna, B. (2023). Predicting discharge from a complex karst system using the ensemble smoother with multiple data assimilation. Stochastic Environmental Research and Risk Assessment, 37(1), 185-201, 10.1007/s00477-022-02287-y
• E. Di Nardo, G. D'Onofrio, Tommaso Martini (2023) Approximating the first passage time density from data using generalized Laguerre polynomials.   Commun. Nonlinear Sci. Numer. Simul. 118, 106991, 10.1016/j.cnsns.2022.106991
• Chalachew Muluken Liyew, R. Meo, E. Di Nardo, S. Ferraris (2023) Multivariate Time Series Evapotranspiration Forecasting using Machine Learning Techniques.   In Artificial Intelligence and Agents, Proceedings of the the 38th ACM/SIGAPP Symposium on Applied Computing, ACM editor, 377-380, 10.1145/3555776.3577838
• C. Agnese, G. Baiamonte, E. Di Nardo, S. Ferraris, Tommaso Martini (2022)  Modelling the Frequency of Interarrival Times and Rainfall Depths with the Poisson Hurwitz-Lerch Zeta Distribution. Fractal Fract. 6(9), 509,  10.3390/fractalfract6090509
• Chalachew Muluken Liyew, H.A. Melese (2021). Machine learning techniques to predict daily rainfall amount. Journal of Big Data, 8(1), 1-11, 10.21203/rs.3.rs-801241/v1
• Bruno Casella, A. B. Chisari, M. Aldinucci, S. Battiato, M. V. Giuffrida (2024) Federated Learning in a Semi-Supervised Environment for Earth Observation Data. In: Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, Bruges, Belgium.10.14428/esann/2024.es2024-214
• Bruno Casella, J. Matthias, M. Aldinucci, S. Buschjager (2024). Federated Time Series Classification with ROCKET features. In: Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, Bruges, Belgium.10.14428/esann/2024.es2024-61
• Bruno Casella, I. Colonnelli, Gianluca Mittone, R. Birke, W. Riviera, A. Sciarappa, C. Cavazzoni, M. Aldinucci (2024) A Performance Analysis for Confidential Federated Learning. In: Proceedings of the 2024 Deep Learning Security and Privacy Workshop, IEEE Symposium on Security and Privacy 2024, San Francisco, CA.10.1109/SPW63631.2024.00009
• O. Harrak, Bruno Casella, Samuele Fonio, P. Fariselli, Gianluca Mittone, T. Sanavia, M. Aldinucci (2024) Federated AdaBoost for Survival Analysis. In: Proceedings of the ECML-PKDD Workshop, 2nd workshop on advancements
  in Federated Learning, Vilnius, Lithuania.
• Bruno Casella, R. Esposito, A. Sciarappa, C. Cavazzoni, M. Aldinucci (2024) Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios. In: IEEE Access, 12, 47961-47971. 10.1109/ACCESS.2024.3383783
• Bruno Casella, W. Riviera, M. Aldinucci, G. Menegaz (2024) Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis. In: STAR Protocols. 5(1), pp. 102812. 10.1016/j.xpro.2023.102812
• Bruno Casella, Lorenzo Paletto (2023) Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting. In: Proceedings of the 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-5 May 2023, Dubai. 10.1109/ICBC56567.2023.10174989
• Bruno Casella, Samuele Fonio (2023) Architecture-Based FedAvg for Vertical Federated Learning. In: Proceedings of the 3rd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC), IEEE/ACM UCC 2023, Taormina, Italy. 10.1145/3603166.3632559
• M. Pennisi, F. Proietto Salanitri, G. Bellitto, Bruno Casella, M. Aldinucci, S. Palazzo, C. Spampinato (2023) Experience Replay as an Effective  Strategy for Optimizing Decentralized Federated Learning. In: Proceedings of the 1st Workshop on Visual Continual Learning, ICCV 2023, Paris, France. 10.1109/ICCVW60793.2023.00362
• M. Pennisi, F. P. Salanitri, G. Bellitto, Bruno Casella, M. Aldinucci, S. Palazzo, C. Spampinato (2023) FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis. In: Computer Vision and Image
  Understanding, 238, pp. 103882.10.1016/j.cviu.2023.103882
• Bruno Casella, W. Riviera, M. Aldinucci, G. Menegaz (2023) MERGE: A model for multi-input biomedical federated learning. In: Patterns, pp. 100856 10.1016/j.patter.2023.100856
• O. Filippo, F. Bruno, T. H. Pinxterhuis, M. Gasior, L. Perl, L. Gaido, D. Tuttolomondo, A. Greco, R. Verardi, G. Lo Martire, M. Iannaccone, A. Leone, G. Liccardo, S. Caglioni, R. G. Ferreiro, G. Rodinò, G. Musumeci, G. Patti, I. Borzillo, G. Tarantini, W. Wańha, Bruno Casella, E. H Ploumen, L. Pyka, R. Kornowski, A. Gagnor, R. Piccolo, S. Raposeiras Roubin, D. Capodanno, P. Zocca, F. Conrotto, G. M De Ferrari, C. Birgelen, F. D'Ascenzo (2023) Predictors of target lesion failure after treatment of left main, bifurcation, or chronic total occlusion lesions with ultrathin-strut drug-eluting coronary stents in the ULTRA registry. In: Catheterization and Cardiovascular Interventions. 102(2), 221-232.  10.1002/ccd.30696
• I. Colonnelli, Bruno Casella, Gianluca Mittone, Y. Arfat, B Cantalupo, R. Esposito, A. R. Martinelli, D. Medić, M. Aldinucci (2023) Federated Learning meets HPC and cloud. In: Bufano, Filomena, Riggi, Simone, Sciacca, Eva, Schillirò, Francesco (Ed.): Astrophysics and Space Science Proceedings, 193–199, Springer, Catania, Italy. 10.1007/978-3-031-34167-0_39
• Bruno Casella, R. Esposito, C. Cavazzoni, M. Aldinucci (2022) Benchmarking FedAvg and FedCurv for Image Classification Tasks. In: Anisetti, Marco, Bonifati, Angela, Bena, Nicola, Ardagna, Claudio, Malerba, Donato (Ed.): Proceedings of the 1st Italian Conference on Big Data and Data Science, ITADATA 2022, September 20-21.
• Bruno Casella, A. Chisari, S. Battiato, M. Giuffrida (2022) Transfer Learning via Test-time Neural Networks Aggregation. In: Farinella, Giovanni Maria, Radeva, Petia, Bouatouch, Kadi (Ed.): Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022, Volume 5: VISAPP, Online Streaming, February 6-8.10.5220/0010907900003124
• O. Harrak, Bruno Casella, Samuele Fonio, P. Fariselli, Gianluca Mittone, C. Rollo, M. Aldinucci (2024). Federated AdaBoost for Survival Analysis. In Proceedings of the ECML-PKDD Workshops (pp. 1-9). Springer. In print

• M. Asadullah, Md Murad Hossain, S. Rahaman, M. S. Amin, M. S. A. Sumy, M. Y. A. Parh, M. A. Hossain (2023) Evaluation of machine learning techniques for hypertension risk prediction based on medical data in Bangladesh. Indonesian Journal of Electrical Engineering and Computer Science, 31(3), 1794-1802, 10.11591/ijeecs.v31.i3.pp1794-1802
• Gianluca Mittone, F. Svoboda, M. Aldinucci, N. D. Lane, and P. Lio, (2023) A federated learning benchmark for drug-target interaction, in Companion proceedings of the acm web conference 2023 (www ’23 companion), Austin, Texas, 10.1145/3543873.3587687
• Gianluca Mittone, N. Tonci, R. Birke, I. Colonnelli, D. Medić, A. Bartolini, R. Esposito, E. Parisi, F. Beneventi, M. Polato, M. Torquati, L. Benini, and M. Aldinucci, (2023) Experimenting with emerging RISC-V systems for decentralised machine learning, in 20th ACM international conference on computing frontiers (cf ’23), Bologna, Italy,  10.1145/3587135.3592211 
• Gianluca Mittone, W. Riviera, I. Colonnelli, R. Birke, and M. Aldinucci, (2023) Model-agnostic federated learning, in Euro-par 2023: parallel processing, Limassol, Cyprus, 10.48550/arXiv.2303.04906 
  D. Caldo, Bologna S., Conte L., Saad Amin M., Anselma L., Basile V., Md. Murad Hossain, Mazzei A., Heritier P. , Ferracini R., Kon E., De Nunzio G. (2023) Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain. Scientific Reports, 13(1), 4654, 10.1038/s41598-023-31741-2
• M. S. Khan, T. D. Nath, Md Murad Hossain, A. Mukherjee, H. B. Hasnath, T. M. Meem, U. Khan, (2023) Comparison of Multiclass Classification Techniques Using Dry Bean Dataset, International Journal of Cognitive Computing in Engineering, 4, 6-20, 10.1016/j.ijcce.2023.01.002
• Y. Arfat, M. Aldinucci, Gianluca Mittone et al.  (2022) Towards extreme scale technologies and accelerators for eurohpc hw/sw supercomputing applications for exascale: the textarossa approach, Microprocessors and microsystems, 95, 104679, 10.1016/j.micpro.2022.104679
• Alice Battiston, E. Massaro, C.R.  Binder and R. Schifanella High spatial resolution dataset of La Mobilière insurance customers. (2022) Scientific Data, 9, 81, 10.1038/s41597-022-01174-z
• Md Murad Hossain, M. Asadullah, M. A. Hossain, M. S. Amin  (2022). Prediction of depression using machine learning tools taking consideration of oversampling. Malaysian Journal of Public Health Medicine, 22(2), 244-253, 10.37268/mjphm/vol.22/no.2/art.1564
• A. Mazzei, L. Anselma, M. Sanguinetti, A. Rapp, D. Mana, Md Murad Hossain, V. Patti,R. Simeoni, L. Longo (2022) Anticipating User Intentions in Customer Care Dialogue Systems, IEEE Transactions on human-machine systems, 1-11, 10.1109/THMS.2022.3184400
• G. Gallone,  J. Kang, F. Bruno, J.K. Han, O. De Filippo, H. Yang, M. Doronzo, K. Park, Gianluca Mittone, H. Kang et al. (2022) Impact of left ventricular ejection fraction on procedural and long-term outcomes of bifurcation percutaneous coronary intervention, The American Journal of Cardiology,172, 18-25, 10.1016/j.amjcard.2022.02.015
• Gianluca Mittone et al., TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale, (2021) in 24th Euromicro Conference on Digital System Design (DSD), 286-294, 10.1109/DSD53832.2021.00051
• M. Aldinucci, V. Cesare, I. Colonnelli, A. R. Martinelli, Gianluca Mittone, B. Cantalupo, C. Cavazzoni, M. Drocco (2021) Practical parallelization of scientific applications with OpenMP, OpenACC and MPI,  Journal of parallel and distributed computing, 157, 13-29, 10.1016/j.jpdc.2021.05.017
• Y. Arfat, Gianluca Mittone, R. Esposito, B. Cantalupo, G. De Ferrari, M. Aldinucci (2021) Machine learning for cardiology Minerva Cardiology and Angiology,1-23, 10.23736/S2724-5683.21.05709-4
• F. D’Ascenzo, O.De Filippo, G.Gallone, Gianluca Mittone, I. Colonnelli, Y. Arfat et al. (2021) Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets, The Lancet,397, (10270), 199-207, 10.1016/S0140-6736(20)32519-8

• Yasir Arfat, M. Aldinucci, G. Mittone et al.  (2022) Towards extreme scale technologies and accelerators for eurohpc hw/sw supercomputing applications for exascale: the textarossa approach, Microprocessors and microsystems,  95, 104679, 10.1016/j.micpro.2022.104679
• Davide Tricarico, M. Calandri, M. Barba, C. Piatti, C. Geninatti, D. Basile, M. Gatti, M. Melis, A. Veltri (2022) Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary "Real Life" Results. Diagnostics, 12(3), 570,  10.3390/diagnostics12030570
• D. Giordano, F. Giobergia, E. Pastor, A. La Macchia, T. Cerquitelli, E. Baralis, M. Mellia, Davide Tricarico (2022) Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case, Computers in Industry, 134, 103554, 10.1016/j.compind.2021.103554.
• Yasir Arfat, G. Mittone, R. Esposito, B. Cantalupo, G. De Ferrari, M. Aldinucci (2021) Machine learning for cardiology Minerva Cardiology and Angiology,1-23, 10.23736/S2724-5683.21.05709-4
• F.D’Ascenzo, O.De Filippo, G.Gallone, G. Mittone, M. A. Deriu, M. Iannaccone, I. Colonnelli, Yasir Arfat et al. (2021) Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets, The Lancet, 397, (10270), 199-207, 10.1016/S0140-6736(20)32519-8
• J.Ito, Emanuele Lucrezia, G.Palm, S.Gruen (2019) Detection and evaluation of bursts in terms of novelty and surprise. Mathematical Biosciences and Engineering, 16, 6990-7008, 10.3934/mbe.2019351
• Daniele Rama, T. Piccardi, M. Redi, R. Schifanella (2022) A Large Scale Study of Reader Interactions with Images on Wikipedia. EPJ Data Science, 11(1),1, 10.1140/epjds/s13688-021-00312-8
• Shuyi Yang, D.Ienco, R.Esposito, R.G.Pensa (2021) ESA*: A Generic Framework for Semi-supervised Inductive Learning, Neurocomputing, 447, 102-117, 10.1016/j.neucom.2021.03.051
• C. Berloco, G. De Francisci Morales, D. Frassineti, G. Greco, H. Kumarasinghe, M. Lamieri, E. Massaro, A. Miola, Shuyi Yang, (2021) Predicting corporate credit risk: Network contagion via trade credit. PLoS ONE 16(4), e0250115, 10.1371/journal.pone.0250115
• Shuyi Yang, D. Ienco, R. Esposito, R. G. Pensa (2021) ESA☆: A generic framework for semi-supervised inductive learning, Neurocomputing, 447, 102-117, 10.1016/j.neucom.2021.03.051
• Shyi Yang (2020) Data Scientist: from zero to hero. Towards Data Science. A Medium publication sharing concepts, ideas and codes. (On line contribution).  

Papers

• Duilio Balsamo, P.Bajardi, A.Salomone, R.Schifanella (2021) Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions J. Med. Internet Res. 23(1), e21212, 10.2196/21212
• Duilio Balsamo, P.Bajardi, A.Panisson (2019) First hand Opiates Abuse on Social Media: Monitoring Geospatial Patterns of Interest Through a Digital Cohort. In the World Wide Web Conference 2019  (WWW'19) Association for Computing Machinery, New York, NY, USA, 2572-2579, 10.1145/3308558.3313634
• B.Gobbo, Duilio Balsamo, M.Mauri, P.Bajardi, A.Panisson, P.Ciuccarelli (2019) Topic Tomographies (TopTom): a visual approach to distill information from media streams. Computer Graphics Forum, Vol.38, 609-621. 10.1111/cgf.13714
• M. Aldinucci, D. Atienza, F. Bolelli, M. Caballero, I. Colonnelli, J. Flich, J. A. Gómez, D. González, C. Grana, M. Grangetto, S. Leo, P. López, D. Oniga, R. Paredes, L. Pireddu, E. Quiñones, T. Silva, E. Tartaglione, and M. Zapater (2022) The DeepHealth toolkit: a key european free and open-source software for deep learning and computer vision ready to exploit heterogeneous HPC and Cloud architectures, in Technologies and applications for big data value, E. Curry, S. Auer, A. J. Berre, A. Metzger, M. S. Perez, and S. Zillner, Eds., Cham: Springer international publishing, 183–202, 10.1007/978-3-030-78307-5_9
• Iacopo Colonnelli, M. Aldinucci, B. Cantalupo, L. Padovani, S. Rabellino, C. Spampinato, R. Morelli, R. Di Carlo, N. Magini, C. Cavazzoni, (2022) Distributed workflows with Jupyter, Future Generation Computer Systems, 128,  282–298, 10.1016/j.future.2021.10.007
• M. Aldinucci, V. Cesare, I. Colonnelli, A. R. Martinelli, G.Mittone, B. Cantalupo, C. Cavazzoni, M. Drocco (2021) Practical parallelization of scientific applications with OpenMP, OpenACC and MPI.  Journal of parallel and distributed computing, 157, 13-29, 10.1016/j.jpdc.2021.05.017
• O. D. Filippo, J. Kang, F. Bruno, J. Han, A. Saglietto, H. Yang, G. Patti, K. Park, R. Parma, H. Kim, L. D. Luca, H. Gwon, M. Iannaccone, W. J. Chun, G. Smolka, S. Hur, E. Cerrato, S. H. Han, C. di Mario, Y. B. Song, J. Escaned, K. H. Choi, G. Helft, J. Doh, A. T. Giachet, S. Hong, S. Muscoli, C. Nam, G. Gallone, D. Capodanno, D. Trabattoni, Y. Imori, V. Dusi, B. Cortese, A. Montefusco, F. Conrotto, I. Colonnelli, I. Sheiban, G. M. de Ferrari, B. Koo, F. D’Ascenzo (2021), Benefit of extended dual antiplatelet therapy duration in acute coronary syndrome patients treated with drug eluting stents for coronary bifurcation lesions (from the BIFURCAT registry), The american journal of cardiology, 156, 16-23, 10.1016/j.amjcard.2021.07.005
• Iacopo Colonnelli et al., "TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale," (2021) 24th Euromicro Conference on Digital System Design (DSD), 286-294, 10.1109/DSD53832.2021.00051
• Iacopo Colonnelli (2021) Towards Cloud-HPC Continuum: Container-native workflow manager for hybrid infrastructures
• Iacopo Colonnelli, B.Cantalupo, R.Esposito, M.Pennisi, C.Spampinato, M. Aldinucci (2021) HPC Application Cloudification: The StreamFlow Toolkit, In 12th workshop on parallel programming and run-time management techniques for many-core architectures and 10th workshop on design tools and architectures for multicore embedded computing platforms (Ditam 2021), Dagstuhl, Germany, 5, 1–13,  10.4230/OASIcs.PARMA-DITAM.2021.5
• F.D’Ascenzo, O.De Filippo, G.Gallone, G. Mittone, M. A. Deriu, M. Iannaccone, Iacopo Colonnelli, Y.Arfat et al. (2021) Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets, The Lancet 397, 10270, 199-207. 10.1016/S0140-6736(20)32519-8
• V.Cesare, Iacopo Colonnelli, M.Aldinucci (2020) Practical parallelization of scientific applications, In Proc. of 28th euromicro intl. conference on parallel distributed and network-based processing, Västerås, Sweden, 376-384, 10.1016/j.jpdc.2021.05.017
• Iacopo Colonnelli, B.Cantalupo, I.Merelli, M.Aldinucci (2020) Streamflow: cross-breeding cloud with HPC, IEEE Transactions on Emerging Topics in Computing. 9(4), 1723-1737, 10.1109/TETC.2020.3019202
• M.Drocco, P.Viviani, Iacopo Colonnelli, M.Aldinucci, M.Grangetto,(2019) Accelerating spectral graph analysis through wavefronts of linear algebra operations In Proc. of 27th euromicro intl. conference on parallel distributed and network-based processing, Pavia, Italy, 9-16, 10.1109/EMPDP.2019.8671640
• P.Viviani, M.Drocco, D.Baccega, Iacopo Colonnelli, M.Aldinucci (2019) Deep learning at scale In Proc. of 27th euromicro intl. conference on parallel distributed and network-based processing, Pavia, Italy, 124-131, 10.1109/EMPDP.2019.8671552
• Elena Travaglia, V.L. Morgia, E.T. Venturino (2020) Poxvirus, red and grey squirrel dynamics: Is the recovery of a common predator affecting system equilibria? Insights from a predator-prey ecoepidemic model. Discrete and continuous dynamical systems. Series B. 25(6), 2023-2040, 10.3934/dcdsb.2019200
• M. Mazza, M. Semplice, S. Serra-Capizzano, Elena Travaglia (2021) A matrix-theoretic spectral analysis of incompressible Navier–Stokes staggered DG approximations and a related spectrally based preconditioning approach. Numerische Mathematik, 149(4), 933-971, 10.1007/s00211-021-01247-y
• M. Semplice, Elena Travaglia, G. Puppo (2021) One- and Multi-dimensional CWENOZ Reconstructions for Implementing Boundary Conditions Without Ghost Cells. Communications on Applied Mathematics and Computation. 1-27, 10.1007/s42967-021-00151-4

• C. Rubina Nava, Luigi Riso , M.G. Zoia (2022) Automatic variable selection for MIDAS regressions: an application. Book of Short Papers SIS 2022, Pearson editor, 1841-1846, ISBN: 9788891932310
• Luigi Riso, M. Guerzoni (2022) Concept Drift Estimation with Graphical Models. Information Sciences, 606, 786-804 10.1016/j.ins.2022.05.056
• Claudia Berloco, G. De Francisci Morales, D. Frassineti, G. Greco, H. Kumarasinghe, M. Lamieri, E. Massaro, A. Miola, S. Yang (2021) Predicting corporate credit risk: Network contagion via trade credit. PLoS ONE 16(4), e0250115, 10.1371/journal.pone.0250115
• Adane Nega Tarekegn, M. Giacobini, K. Michalak (2021) A review of methods for imbalanced multi-label classification. Pattern recognition,118, 107965, 10.1016/j.patcog.2021.107965
• Adane Nega Tarekegn, K. Michalak, M. Giacobini (2020) Cross Validation Approach to Evaluate Clustering Algorithms: An Experimental Study using Multi-label Datasets. SN Computer Science, 1, 1-9, 10.1007/s42979-020-00283-z
• Adane Nega Tarekegn, F.Ricceri , G.Costa, E.Ferracin, M. Giacobini (2020) Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics, 8(6), e16678,10.2196/16678