Ritratto di Agostino.Diciaccio@uniroma1.it

Didattica 2024 / 2024 Teaching:

Salvo diverse indicazioni, le lezioni si svolgeranno in presenza./ Unless otherwise indicated, the lessons will take place in person.

 

- Data Mining e Classificazione (inizia il 26 Febbraio alle 10:00, room XI)

- Big Data Analytics

  first part starts 27 February at 12:00 with prof. U. Ferraro Petrillo (room XI)

 

- Laboratory of Machine Learning (starts February 29 at 10:00 am, room XII)

 

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Le informazioni relative allo svolgimento delle lezioni saranno disponibili tramite la piattaforma Moodle elearning.uniroma1.it  a tutti gli iscritti al corso. La password di accesso al corso on-line viene comunicata il primo giorno di lezione. Tutto il materiale resterà disponibile su Moodle.  Chi avesse problemi di accesso a Moodle può contattarmi tramite e-mail: agostino.diciaccio@uniroma1.it

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Information relating to the lessons will be available through the Moodle platform  elearning.uniroma1.it  to all course participants. The password to access the on-line course is communicated on the first day of class. All the material will remain available on Moodle. Anyone who has problems logging into Moodle can contact me by e-mail: agostino.diciaccio@uniroma1.it

 

Insegnamento Codice Anno Corso - Frequentare Bacheca
LABORATORY OF MACHINE LEARNING AAF1883 2023/2024
DATA MINING E CLASSIFICAZIONE 1022798 2023/2024
BIG DATA ANALYTICS 1047773 2023/2024
LABORATORY OF MACHINE LEARNING AAF1883 2023/2024
BIG DATA ANALYTICS 1047773 2023/2024
LABORATORY OF MACHINE LEARNING AAF1883 2022/2023
DATA MINING E CLASSIFICAZIONE 1022798 2022/2023
BIG DATA ANALYTICS 1047773 2022/2023
LABORATORY OF MACHINE LEARNING AAF1883 2022/2023
BIG DATA ANALYTICS 1047773 2022/2023
LABORATORY OF MACHINE LEARNING AAF1883 2021/2022
DATA MINING E CLASSIFICAZIONE 1022798 2021/2022
BIG DATA ANALYTICS 1047773 2021/2022
LABORATORY OF MACHINE LEARNING AAF1883 2021/2022
BIG DATA ANALYTICS 1047773 2021/2022
LABORATORY OF MACHINE LEARNING AAF1883 2020/2021
DATA MINING E CLASSIFICAZIONE 1022798 2020/2021
BIG DATA ANALYTICS 1047773 2020/2021
LABORATORY OF MACHINE LEARNING AAF1883 2020/2021
BIG DATA ANALYTICS 1047773 2020/2021
LABORATORY OF MACHINE LEARNING AAF1883 2019/2020
DATA MINING 10589571 2019/2020
DATA MINING E CLASSIFICAZIONE 1022798 2019/2020
BIG DATA ANALYTICS 1047773 2019/2020
LABORATORY OF MACHINE LEARNING AAF1883 2019/2020
BIG DATA ANALYTICS 1047773 2019/2020
LABORATORY OF MACHINE LEARNING AAF1883 2018/2019
DATA MINING E CLASSIFICAZIONE 1022798 2018/2019
BIG DATA ANALYTICS 1047773 2018/2019
LABORATORY OF MACHINE LEARNING AAF1883 2018/2019
DATA MINING 10589571 2018/2019
DATA MINING E CLASSIFICAZIONE 1022798 2018/2019
altre conoscenze utili per l'inserimento nel mondo del lavoro AAF1152 2017/2018
altre conoscenze utili per l'inserimento nel mondo del lavoro AAF1149 2017/2018
LABORATORY OF DATA MINING AAF1840 2017/2018
altre conoscenze utili per l'inserimento nel mondo del lavoro AAF1149 2017/2018
ALTRE CONOSCENZE UTILI PER L'INSERIMENTO NEL MONDO DEL LAVORO AAF1152 2017/2018
DATA MINING E CLASSIFICAZIONE 1022798 2017/2018
BIG DATA ANALYTICS 1047773 2017/2018
ALTRE CONOSCENZE UTILI PER L'INSERIMENTO NEL MONDO DEL LAVORO AAF1152 2016/2017
altre conoscenze utili per l'inserimento nel mondo del lavoro AAF1149 2016/2017
PER STAGES E TIROCINI PRESSO IMPRESE, ENTI PUBBLICI O PRIVATI, ORDINI PROFESSIONALE AAF1176 2016/2017
DATA MINING E CLASSIFICAZIONE 1022798 2016/2017
altre conoscenze utili per l'inserimento nel mondo del lavoro AAF1152 2016/2017
altre conoscenze utili per l'inserimento nel mondo del lavoro AAF1149 2016/2017
BIG DATA ANALYTICS 1047773 2016/2017

Per prenotare un ricevimento inviare una e-mail a agostino.diciaccio@uniroma1.it
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To book a reception send an e-mail to agostino.diciaccio@uniroma1.it

Agostino Di Ciaccio
Phone: +39 06 49910709 (off.)
Agostino.diciaccio@uniroma1.it

Work experience
Current position: full professor of Statistics at Sapienza University of Rome, dept. of Statistics (since 2001).
Previous positions: assistant professor (1990-1992) of Statistics at Sapienza University of Rome and associate professor (1992-2001) of Statistics at University of Urbino.
Education:
Graduated in Statistics at Sapienza University of Rome, dept. of Statistics (1982);
PhD in Methodological Statistics, Sapienza University of Rome, dept. of Statistics (1988), with the thesis " Maximum Association Criteria and Optimal Scaling - a unified approach to the analysis of qualitative and mixed measurement level variables ".

Teaching in master s degrees:
Data Mining and classification;
Big Data Analytics;
Laboratory of Machine Learning.

Other teaching activities
Deep learning with Python for the analysis of Big-Data (professional course, university of Rome, La Sapienza);
SAS Viya Visual Data Mining and Machine Learning (Master Big Data. Metodi statistici per la società della conoscenza , university of Rome, La Sapienza);
Machine learning with Python (Master Customer Experience & Social Media Analytics , university of Tor Vergata, Rome);
Organizer of summer school, SAS-Campus and SAS certifications for machine learning.

Scientific interests:
Machine Learning, Deep Learning, Data mining, Text and Web mining, Analysis of Big Data, Statistical Inference, Multivariate Statistics.

Academic offices:
President of the didactic council Statistics and decision sciences (2009 - 2017 );
member of the PhD board in Methodological Statistics (2001 - 2012);
member of the Doctoral School in Statistics, Sapienza University of Rome (2012 - 2017);
coordinator of European Master in Official Statistics (EMOS) for the Dept. of Statistics of Sapienza University (2012-2019);
member of the faculty board (2008-2011);
member of the SIS board (2006-2010);
director of the computing center, faculty of Economics, Urbino university (1995-2001).

Memberships:
Italian Statistical Society (SIS);
ISI (International Statistical Institute);
IASC (International Association for Statistical Computing);
IFCS (International Federation of Classification Societies).

Authorship:
Author of over 50 papers published in journals, conference proceedings, volumes.
Author of the most widespread book in Italy of introduction to statistics (McGraw Hill eds.)

Activities in scientific institutions and Journals:
Organizer of the international scientific meeting: Non-linear methods and data analysis (Rome, Sapienza, 2000);
organizer of the international scientific meeting: Statistical Methods for the analysis of large datasets. (SIS, 2009);
guest Editor of the special issue of the journal Computational Statistics and Data Analysis "Software and Applications for Multiway Data Analysis", 1994;
guest Editor of a special issue of the journal Computational Statistics and Data Analysis (with S. Borra) entitled "Nonlinear Methods and Data Mining", February 2002;
team manager of the EU project VL-C@TS , Virtual Library for Computer assisted Training in Statistics (2000-2002);
member of the organizing committee of the international conference "MULTIWAY '88" University of Rome "La Sapienza" (1988);
organizer and chairman of the session "Resampling techniques in nonparametric methods" at the XLIII Scientific Meeting of the SIS (2006);
member of the scientific committee of the 2011 SIS meeting "Statistics in the 150 years of the Unification of Italy";
member of the scientific committee of the 10th Scientific Meeting of the Classification and Data Analysis Group (2015);
coordinator of the scientific committee and of the organizing committee of the international meeting "Nonlinear methods and Data Mining", Rome, CNR (2000);
member of the editorial board of the Journal of Italian Statistical Society (1991-1995);
referee for the journal METRON, JISS, CSDA, SOCI, INS;
member of the Advisory Board of the international magazine Metron (since 2014).
scientific director of the course "Deep learning with Python for the analysis of Big-Data".

Computer skills
Good knowledge of the most common programming languages (C, C++, R, Python) and major statistical packages (SAS, SPSS, JMP, IMSL, Weka).

Selected publications
Bove G., Di Ciaccio A. (1986). Typologies and associative structures: a comparison between different methods, COMPSTAT 86 - Short communications and posters, Univ. "La Sapienza", ROMA 1986.
Di Ciaccio A. (1986). Representation of a new association measure between categories using Multidimensional Scaling" in Data Analysis and Informatics, E. Diday and al. (eds.), North Holland 1986.
Di Ciaccio A. (1986). Un nuovo metodo di c1uster per l'individuazione di sistemi a livello sub-regionale, in Atti della XXXIII Riunione Scientifica della S.I.S., Bari, 1986.
Bove G., Di Ciaccio A. (1987). A factorial method for the analysis ofthree way data in tensor spaces, in Data Analysis and Informatics, E. Diday and al. (eds.), North Holland 1987.
Di Ciaccio A. (1988). Some considerations on the quantification of categorical data, Research-Report, Department of Data Theory, University of Leiden, 1988.
Di Ciaccio A. (1988). Criteri e metodi di quantificazione dei caratteri qualitativi, Atti della XXXIV Riunione Scientifica della SIS, Siena, 1988, Nuova Editrice Italiana.
Di Ciaccio A., De Leuuw J. (1988). Cluster Analysis per matrici di dati a due o più indici e con livello di misurazione misto, in atti della riunione italiana soci IFCS, Erice 1988.
Di Ciaccio A. (1988). Multiway'88 - Software Guide. (A. Di Ciaccio and G. Bove eds.), Dip. Statistica, Probabilità e Stat. Applicate, Roma, 1988.
Bove G., Di Ciaccio A. (1989). Comparison among three factorial methods for the analysis of three-mode data, in "MULTIWAY DATA ANALYSIS" (Coppi & Bolasco eds.), 103-113, North Holland, Amsterdam, ISBN:0-444-87410-0.
Di Ciaccio A. (1990). Metodi di classificazione per l'analisi simultanea di caratteri qualitativi e quantitativi, Atti della XXXV Riunione Scientifica della SIS, Padova 1990, CEDAM.
Di Ciaccio A. (1990). L'analisi simultanea dei caratteri qualitativi e quantitativi attraverso la parametrizzazione dei dati, Metron vol. XLVIII n.1-4 1990, 333-364.
Di Ciaccio A. (1992). Simultaneous c1ustering of qualitative and quantitative data with missing observations, Rivista di Statistica Applicata, V. 4, n. 4, 1992, 599-610.
Coppi R., Di Ciaccio A. (1992). The metodological impact of nonlinear analysis, Rivista di Statistica Applicata V. 4, n. 4, 1992, 407-428.
Coppi R., Di Ciaccio A. (1994). Modelli multi lineari per l'analisi di dati qualitativi, atti della XXXVII Riunione Scientifica della SIS, San Remo, voI. 1, 1994, 369-380.
Bove G., Di Ciaccio A. (1994). A user-oriented overview of multiway methods and software, Computational Statistics & Data Analysis, special issue "Software And Applications For Multiway Data Analysis" (R. Coppi and A. Di Ciaccio eds.), North Holland, n. 18, 1994, 15-37.
Di Ciaccio A. (1994). Multiway Data Analysis - Software and Applications, Computational Statistics and Data Analysis, special issue (R. Coppi and A. Di Ciaccio eds.), North Holland, n. 18, 1994.
Borra S., Di Ciaccio A. (1996). Introduzione alla Statistica descrittiva - volume e ipermedia - McGraw Hill, Milano 1996.
Borra S., Di Ciaccio A. (1998). Non-parametric regression models for the conjoint analysis of qualitative and quantitative data, in "Advances in data science and classification", editors A. Rizzi, M. Vichi, H.H.Bock, Springer Verlag, Berlin, 1998.
Borra S., Di Ciaccio A. (1999). Using qualitative information and neural networks for forecasting purposes in financial time series, in Classification and Data Analysis, Theory and Application (Vichi and Opitz eds), Springer, Berlin 1999.
Borra S., Di Ciaccio A. (1999). Bagging and Boosting performance in Projection Pursuit Regression, proceedings of ISI'99, Helsinki 1999.
Borra S, Di Ciaccio A. (2000). Aggregazione di funzioni di approssimazione nei metodi regressivi non-parametrici. In: Proceedings XL Riunione Scientifica SIS. Firenze, 26-28 Aprile 2000, Padova: CLEUP.
Borra S., Di Ciaccio A. (2001). Performance evaluation of Bagging and Boosting in nonparametric regression. METRON, vol. LIX , 3-4; p. 141-156, ISSN: 0026-1424.
Di Ciaccio A. (2001). MIXISO: a non-hierarchical clustering method for mixed mode data. In: Borra S., Rocci R., Vichi M., Schader M.. Advances in classification and data analysis, Studies in Classification, Data Analysis and knowledge Organization. p. 245-253, BERLIN: Springer-Verlag, ISBN/ISSN: 978-3540414889.
Borra S, Di Ciaccio A. (2001). Reduction of Prediction Error by Bagging Projection Pursuit Regression. In: Borra S., Rocci R., Vichi M., Schader M., Advances in Classification and Data Analysis; Studies in Classification, Data Analysis, and Knowledge Organization. p. 241-248, BERLIN: Springer-Verlag, ISBN/ISSN: 978-3-540-41488.
Di Ciaccio A., Montanari G.E. (2001). A nonparametric regression estimator of a finite population mean. In: CLADAG - Book of Short Papers. Palermo, 2001, p. 173-176.
Borra S, Di Ciaccio A. (a cura di) (2002). Nonlinear Methods and Data Mining. (Special issue of Computational Statistics and Data Analysis). Amsterdam: North Holland, vol. 38-4.
Borra S, Di Ciaccio A. (2002). Improving nonparametric regression methods by bagging and boosting. Computational Statistics and Data Analysis, vol. 38-4; p. 407-420, ISSN: 0167-9473.
Di Ciaccio A., Montanari G.E (2002). Non parametric methods and data mining applications. In: Proceedings SIS 2002, Plenary and specialized sessions, Padova: Cleup, vol. 1, p. 339-348.
Bove G, Di Ciaccio A. (a cura di) (1994). Software and application for Multiway Data Analysis (special issue of CSDA). Amsterdam: North-Holland, vol. 18.
Borra S., Di Ciaccio A. (2004). Methods to compare nonparametric classifiers and to select the predictors. In: M. Vichi, P. Monari et al, New Developments in classification and data analysis (Studies in Classification, Data Analysis and Knowledge Organization). p. 11-20, BERLIN: Springer-Verlag, ISBN/ISSN: 3-540-23809-3.
Di Ciaccio A., Vallely T (2007). Use of non-parametric methods for the imputation of missing data. A comparison based on extensive Montecarlo simulations. In: Proceeding of SCo.o 2007. Venezia, September 6-8, 2007.
Borra S., Di Ciaccio A. (2008). Statistica. Metodologie per le scienze economiche e sociali. 2a edizione. MILANO: McGraw Hill, p. 1-513, ISBN: 978-88-386-6428-1.
Di Ciaccio A., Borra S (2008). Estimators of extra-sample error for non-parametric methods. A comparison based on extensive simulations. Roma: Dip. di Statistica, Prob. e Statistiche Applicate, p. 1-40.
Borra S., Di Ciaccio A. (2008). The estimation of prediction error for neural networks: a simulation study. In: Proceedings ERCIM'2008. Neuchâtel, Switzerland, 19-21 June 2008.
Di Ciaccio A. (2008). Bootstrap and Nonparametric Predictors to Impute Missing Data. In: Proceedings of joint meeting of Societe Francophone de Classification and Classification and Data Analysis Group, Book of short papers. 2008, Edizioni Scientifiche Italiane, p. 147-150, ISBN/ISSN: 978-88-495-1656-2.
Borra S., Di Ciaccio A. (2010). Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Computational Statistics & Data Analysis, V. 54, 12, p. 2976-2989, ISSN: 0167-9473, doi: 10.1016/ j.csda.2010.03.004
Borra S.; A. Di Ciaccio (2010) Variable selection in a predictive approach. in :CLADAG 2009, book of abstracts, 8 - 10 September 2010 Firenze.
Di Ciaccio A. (2011). Bootstrap and Nonparametric Predictors to Impute Missing Data. In: Fichet D., Piccolo D., Verde R., Vichi M., Classification and Multivariate Analysis for Complex Data Structures. Series: Studies in Classification, Data Analysis, and Knowledge Organization. vol. 1, p. 301-309, BERLIN: Springer, ISBN/ISSN: 978-3-642-13311-4.
Di Ciaccio A., Giorgi G.M.(2012). Una nuova procedura di imputazione di dati mancanti basata sugli alberi di decisione, Italian Review of Economics, Demography and Statistics (ISSN:0035-6832), 149- 156, 66 n.1;
Di Ciaccio A.; Coli M.; Angulo Ibanez J. M. eds. (2012) Advanced Statistical Methods for the Analysis of Large Datasets. Studies in Theoretical and Applied Statistics. Springer-Verlag Heidelberg (Germany). ISBN:978-36-422-1036-5.
Di Ciaccio A., Giorgi G.M. (2012). La scelta delle variabili in un modello di regressione lineare. Italian Review of Economics, Demography and Statistics, v. 66, n. 3-4, 87-94.
Di Ciaccio A., Giorgi G.M.(2013). Statistical analysis of social networks. Italian Review of Economics, Demography and Statistics, v. 67, n. 3-4, 103-110.
Di Ciaccio A., Giorgi G.M. (2014). Machine Learning and text mining to classify tweets on a political leader. Italian Review of Economics, Demography and Statistics, v. 68, n. 3-4.
Di Ciaccio A., Giorgi G.M. (2016). La statistica nell'era dei big-data. In Book of abstracts 53ma riunione scientifica SIEDS, 26-28 Maggio, Roma.
Di Ciaccio A., Giorgi G.M. (2016). Deep learning for supervised classification. Italian Review of Economics, Demography and Statistics, vol. LXX, n. 1. 157-166.
Di Ciaccio A., Cialone G. (2018). Insolvency prediction by deep learning, in proceedings of Statistics and Data Science - New developments for business and industrial applications.
Di Ciaccio A., Crobu F. (2019). 2019. An application of deep learning to chest disease detection using images and clinical data, in Statistical evaluation sistems at 360°: techniques, technologies and new frontiers, pp.397-401, ISBN:978-88-86638-65-4.
Di Ciaccio A., Crobu F. (2019). Classify X-RAY images using convolutional neural networks, in Cladag 2019, Book of short-papers, pp. 144-147.
Di Ciaccio Damiano, Maroder Edoardo, Ambrosio Sara, Rossini Francesco Livio, Di Ciaccio Agostino (2019). AI applications in the building process. In: The human dimension of building energy performance, conference proceedings Aicarr (Venezia, 20-22 february 2019), ISBN: 978-88-95620-63-3
Di Ciaccio A., Cialone G. (2019). Insolvency prediction by deep learning. International Journal of Data Mining & Knowledge Management Process, vol. 9 n.6, p.1-12.
Di Ciaccio A. (2020). Categorical Encoding for Machine Learning, in Book of short papers SIS2020, A. Pollice et al. eds., ISBN 9788891910776, Pearson Italia.
Di Ciaccio A., Crobu F. (2021). Deep learning to jointly analyze images and clinical data for disease detection, Statistical Learning and Modeling in Data Analysis in Studies in Classification, Data Analysis and Knowledge Organization, S. Balzano, G.C. Porzio, R. Salvatore, D. Vistocco, M. Vichi (Eds.), Springer Berlin. ISBN 978-3-030-69944-4.
Di Ciaccio A. (2021). Neural Networks for high cardinality categorical data. Book of Abstracts and Short Papers, G.C. Porzio, C. Rampichini, C. Bocci eds., 320-323. Firenze University Press, Firenze, ISSN 2704-601X, ISBN 978-88-5518-340-6, DOI 10.36253/978-88-5518-340-6
Di Ciaccio A. (2022). Representing the association among categorical variables by Neural Networks, ECDA 2022 meeting, E-Book of abstracts, Napoli.
Gava M., De Fausti F., Mommi G., Di Ciaccio A. (2022) Multi-input deep Neural Networks to Estimate Population from Satellite Imagery, Conference of European Statistics Stakeholders CESS 2022, ID-79, E-Book of abstract, https://cess2022.dss.uniroma1.it/event/3/, Roma.
Di Ciaccio A. (2023). Optimal Coding of categorical data in machine learning. In L. Grilli, M. Lupparelli, E. Rocco, C. Rampichini, M. Vichi (Eds.), Statistical Models and Methods for Data Science. Studies in Classification, Data Analysis and Knowledge Organization. ISBN 978-3-031-30163-6. ISSN 1431-8814. Springer.
Gava M., De Fausti F., Mommi G., Di Ciaccio A. (submitted, 2023) Multi-input deep Neural Networks to Estimate Population from Satellite Imagery, Special Issue of ADAC, New methods using Statistics, Data Science and AI for timely, and high-quality statistics Lausen, Klein, Vichi eds., Springer.
Di Ciaccio A. (2023). Optimal Coding of categorical data in machine learning. In L. Grilli, M. Lupparelli, E. Rocco, C. Rampichini, M. Vichi (Eds.), Statistical Models and Methods for Data Science, pp. 39-51. Studies in Classification, Data Analysis and Knowledge Organization. ISBN 978-3-031-30163-6. ISSN 1431-8814. Springer, Cham.
Abdi H., Di Ciaccio A., Saportà G. (2023) Old and New Perspectives on Optimal Scaling , to be published in Analysis of Categorical Data from Historical Perspectives Essays in Honour of Shizuhiko Nishisato , edited by: Eric J. Beh, Rosaria Lombardo, Jose G. Clavel, book series Behaviormetrics: Quantitative Approaches to Human Behavior 17, DOI 10.1007/978-981-99-5329-5_9, Springer Nature, Singapore.