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Doctoral school

PhD in Modeling and Data Science

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PhD Students Representatives

  • Lucrezia Emanuele (35-th cycle)
  • Battiston Alice (36-th cycle)
  • Murad Hossain (36-th cycle)
  • Martini Tommaso (37th cycle)

PhD Students

  • Chiara Bovier 
  • Bruno Casella
  • Lidia Chiarati 
  • Ishrat Fatima
  • Sandro Lancellotti
  • Chalachew Muluken Liyew
  • Tommaso Martini
  • Lorenzo Paletto
  • Alessandro Pansa
Papers
  • Liyew, C. M., Melese, H. A. (2021). Machine learning techniques to predict daily rainfall amount. Journal of Big Data, 8(1), 1-11, doi.org/10.1186/s40537-021-00545-4

 
Papers
  • 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, https://dx.doi.org/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, doi: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) 24th Euromicro Conference on Digital System Design (DSD), pp. 286-294, doi: 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 doi:10.1016/j.jpdc.2021.05.017
  • Y. Arfat, Gianluca Mittone, R. Esposito, B. Cantalupo, G. De Ferrari, M. Aldinucci (2021) A review of machine learning for cardiology Minerva Cardiology and Angiology doi: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 Vol. 397, (10270), 199-207.

 

Papers
  • Yasir Arfat 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), 2021, pp. 286-294, doi: 10.1109/DSD53832.2021.00051
  • Yasir Arfat, G. Mittone, R. Esposito, B. Cantalupo, G. De Ferrari, M. Aldinucci (2021) A review of machine learning for cardiology Minerva Cardiology and Angiology doi: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 Vol. 397, (10270), 199-207.
  • J.Ito, Emanuele Lucrezia, G.Palm, S.Gruen (2019) Detection and evaluation of bursts in terms of novelty and surprise. Mathematical Biosciences and Engineering. Vol. 16, 6990-7008.
  • Daniele Rama, Y.Mejova, M.Tizzoni, K.Kalimeri, I.Weber (2020) Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide The World Wide Web Conference 2020 (WWW'20) Association for Computing Machinery, New York, NY, USA, 327-338.
  • Shuyi Yang, D.Ienco, R.Esposito, R.G.Pensa (2021) ESA*: A Generic Framework for Semi-supervised Inductive Learning, Neurocomputing. , Vol. 447, 102-117.
  • 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 Vol. 16, No. 4, e0250115
  • 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. Vol. 23, No. 1, e21212.
  • 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.
  • 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.
  • 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, “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, 2022, p. 183–202. doi: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, vol. 128, pp. 282–298
  • 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 doi: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, doi:https://doi.org/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), 2021, pp. 286-294, doi: 10.1109/DSD53832.2021.0005
  • 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, Vol5, 1–13.
  • 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 Vol. 397, (10270), 199-207.
  • 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.
  • Iacopo Colonnelli, B.Cantalupo, I.Merelli, M.Aldinucci (2020) Streamflow: cross-breeding cloud with HPC, IEEE Transactions on Emerging Topics in Computing.
  • 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.
  • 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
  • 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. Vol.25, No. 6 2023-2040.
  • 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, doi: 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. doi: 10.1007/s42967-021-00151-4

  • Claudia Berloco
  • Luigi Riso
  • Adane Nega Tarekegn
 
Papers
  • 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 Vol. 16, No. 4, e0250115
  • Adane Nega Tarekegn, M. Giacobini, K. Michalak (2021) A review of methods for imbalanced multi-label classification. Pattern recognition Vol. 118, 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. Vol.1, issue 5, No.263.
  • 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. Vol.8, No.6.

 

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