Antonio D'Ambrosio is Associate Professor in Statistics at the department of Economics and Statistics of the University of Naples Federico II.

On January 30, 2020 he achieved the National Scientific Qualification for the position of Full Professor in Statistics according to the Italian University Professor position recruiting system based on scientific qualification criteria.

He was Assistant Professor at the Department of Economics and Statistics of the University of Naples Federico II from July 2015 to Augusto 2018.
He was Assistant Professor at the Department of Industrial Engineering of the University of Naples Federico II from January 2013 to June 2015.
He was Assistant Professor at the Department of Mathematics and Statistics of the University of Naples Federico II from November 2008 to December 2014.

From Academic Year 2015/2016 he teaches Statistical learning methods and models, Computational statistics and Advanced Statistics at University of Naples Federico II.

He was visiting researcher at the University of Granada (Spain) in March 2018, March 2019 and February-March 2022.

He was visiting researcher at Leiden University (The Netherlands), Department of Psychology (section methods and statistics) from September to December 2010, from September to December 2011 and from September to December 2012.

He took the Ph.D. in Statistics at University of Napoli Federico II, by defending a Ph.D. thesis named “Tree based methods for data editing and preference rankings” in the February 2008 (supervisor prof. Dr. Roberta Siciliano). During the this period he was visiting student at Charles University of Prague working with prof. dr. Jaromìr Antoch. He also was visiting student at Leiden University working with prof. dr. Willem Heiser and with prof. dr. Ab Mooijaart.

From September 2021 he is co-principal investigator of the of the research project “The impact of administrative decentralization and oncological networks on the flows of cancer patients within and across Italian regions” (ONCMOB), funded by the University Federico II as part of the competitive FRA 2020 projects (University Research Funding).
From June 2016 to September 2020 he has been senior researcher for the European Project “Moving Towards Adaptive Governance in Complexity: Informing Nexus Security” (MAGIC), Project ID: 689669, Funded under: H2020-EU.3.5.4. - Enabling the transition towards a green economy and society through eco-innovation, principal investigator: Prof. Dr. Roberta Siciliano, University of Naples Federico II, Naples – Italy. Coordinator: UNIVERSITAT AUTONOMA DE BARCELONA – Spain
January – December 2020: Senior researcher for the project “Bioptic Adavanced Robotic Technologies in OncoLOgy” B.A.R.T.O.L.O. (B41C17000090007), Interdepartmental Center for Advances in RObotic Surgery, action POR Campania FESR 2014/2020
From July 2013 to December 2016 he was senior researcher for the European Research Project RoDyMan (RObotic DYnamic MANipulation), agreement number 320992, funded under: FP7-IDEAS-ERC, scientific coordinator Prof. Dr. Bruno Siciliano. Coordinator: C.R.E.A.T.E. CONSORZIO DI RICERCA PER L'ENERGIA L AUTOMAZIONE E LE TECNOLOGIE DELL'ELETTROMAGNETISMO, Naples – Italy
June 2012: Teacher of the module of Generalized Linear Models for the project "Highway Design and Management: Curricular Reform for Russian Federation Design and Implementation of Higher Education Master Courses in Russia", Project n. 516888 HDMCuRF.
From November 2007 to November 2008 he had a grant as senior researcher for the European Research project “integrated Web Services Platform for the facilitation of fraud detection in health care e-government service” iWebCare (IST-2004-028055), scientific responsible prof. dr. Roberta Siciliano. Coordinator: INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS, Athens – Greece.

He is member of:
The International Statistical Institute (ISI);
the International Association for Statistical Computing (IASC);
the Italian Statistical Society (SIS);
the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);
the American Statistical Association (ASA).

He is Associate Editor of the journal “Machine Learning with Applications” (ISSN: 2666-8270).
He is a member of the editorial board of the journal “Electronic Journal of Applied Statistical Analysis” (ISSN: 2070-5948).
He is creator and author of the statistical package ConsRank, freely available both in R (https://cran.fhcrc.org/web/packages/ConsRank/index.html) and MatLab
(http://www.mathworks.com/matlabcentral/fileexchange/52235)

He is creator and author of the statistical package ConsRankClass, freely available in R (https://cran.r-project.org/web/packages/ConsRankClass/index.html)

Areas of interests research are: Preference learning; Rank aggregation problem; Preference rankings theory and modeling; Computational statistics; Multivariate analysis; Regression modeling; Recursive partitioning methods; Cluster analysis and Multidimensional Scaling; Missing data imputation and Data fusion.

He has been speaker in many international conferences. Main lectures as invited speaker are:

March 2019: “Median Constrained Bucket Order: a way to think about tied rankings”, invited talk at the European Conference on Data Analysis (ECDA 2019), Bayreuth University, (Germany).

July 2018: “Ordinal Unfolding of Preference Rankings Using the Kemeny Distance”, invited talk at the International Meeting of Psychometric Society (IMPS 2018), Columbia University, New York (USA).

May 2018: “Detecting and Interpreting Median Constrained Bucket Orders Within the Kemeny Axiomatic Framework”, invited talk at the Symposium on Data Science and Statistics (SDSS 2018), Reston (USA).

September 2017: “Constrained consensus bucket order”, invited talk at the 11th Scientific Meeting of the Classification and Data Analysis Group (CLADAG 2017), Milan (Italy).

February 2016: “A recursive partitioning method for preference rankings based upon Kemeny distances”, invitation colloquium at the Department of Statistics and Operations Research, Granada University (Spain).

October 2015: “Parsimonious clustering of time series”, invited talk at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG 2015), S. Margerita di Pula (CAGLIARI).

July 2015: “An extension of the Adjusted Rand Index for fuzzy partitions”, invited talk at the International Federation of Classification Societies (IFCS), Bologna (Italy).


October 2012: “K-median cluster component analysis”, invitation colloquium at the Mathematical Institute, Leiden University (the Netherlands).

November 2010: “Distance-based multivariate trees for rankings”, invitation colloquium at the Department of Psychology of the Leiden University (the Netherlands).

 

Publications:

Condittionally accepted papers

D’Ambrosio, A. & Pandolfo, G. Non-parametric depth-based clustering of directional data.

Baldassarre, A., Dusseldorp, E., D’Ambrosio, A., de Rooij, M. & Conversano, C. The Bradly-Terry Regression Trunk approach for modelling preference data with small trees (Available as preprint at https://arxiv.org/abs/2107.13920)

Journal papers

D’Ambrosio, A., Vera J.F. & Heiser, W.J. (2021). Avoiding degeneracies in ordinal Unfolding using Kemeny-equivalent dissimilarities for two-way two-mode preference rank data. Multivariate Behavioral Research, https://doi.org/10.1080/00273171.2021.1899892.

Cannavacciuolo, L., Ponsiglione, C., & D’Ambrosio, A. (2021). How to improve the Triage: A dashboard to assess the quality of nurses’ decision-making. International Journal of Engineering Business Management https://doi.org/10.1177/18479790211065558

Pandolfo, G. & D'Ambrosio, A. (2021). Depth-based classification of directional data. Expert Systems with Applications,  Vol. 161, 1, 114433, https://doi.org/10.1016/j.eswa.2020.114433

D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G. & Siciliano, R. (2021). Adjusted concordance index: an extension of the adjusted Rand index to fuzzy partitions. Journal of Classification. vol. 38(1), pp. 112-128, https://doi.org/10.1007/s00357-020-09367-0.

Pandolfo, G., D'Ambrosio, A., Cannavacciuolo, L. & Siciliano, R. (2020). Logic AGgregation of Crisp Data Partitions as Learning Analytics in Triage Decisions. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.113512.

Iorio, C., Pandolfo, G., Frasso, G. & D'Ambrosio, A. (2020). A combined clustering and multi-criteria approach for portfolio selection. Statistica & Applicazioni. DOI: 10.26350/999999_000018.

Pandolfo, G., Iorio, C., Siciliano, R. & D'Ambrosio, A. (2019). Robust mean-variance portfolio through the weighted Lp depth function. Annals of Operations Research, https://doi.org/10.1007/s10479-019-03474-x

Iorio, C., Pandolfo, G., D'Ambrosio, A. & Siciliano, R. (2019). Mining big data in tourism. Quality & Quantity, https://doi.org/10.1007/s11135-019-00927-0

Scandurra, A., Alterisio, A., Di Cosmo, A., D’Ambrosio, A. & D’Aniello, B. (2019). Ovariectomy impairs socio-cognitive functions in dogs. Animals, 9(2), 58, pp. 1-7.

Iorio, C., Aria, M., D'Ambrosio, A. & Siciliano, R. (2019). Informative Trees by Visual Pruning. Expert Systems with Applications, vol. 127, pp. 228-240, https://doi.org/10.1016/j.eswa.2019.03.018

D'Ambrosio, A., Iorio, C., Staiano, M. & Siciliano, R. (2019). Median constrained bucket order rank aggregation.  Computational Statistics, vol. 34(2), pp. 787–802, https://doi.org/10.1007/s00180-018-0858-z

D'Ambrosio, A. & Heiser, W.J. (2019). A Distribution-free Soft Clustering Method for Preference Rankings. Behaviormetrika, vol. 46(2), pp. 333–351, DOI: 10.1007/s41237-018-0069-5

Morrone, A., Piscitelli, A. & D'Ambrosio, A. (2019). How Disadvantages Shape Life Satisfaction: an Alternative Methodological Approach. Social Indicators Research, vol. 141(1), pp. 477-502, https://doi.org/10.1007/s11205-017-1825-8

Pandolfo, G., D'Ambrosio, A. & Porzio, G. (2018). A note on depth-based classification of circular data.  Electronic Journal of Applied Statistical Analysis, vol. 11(2), pp. 447-462, DOI: 10.1285/i20705948v11n2p447

Aria, M., D'Ambrosio, A., Iorio, C., Siciliano, R. & Cozza, V. (2018). Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images.  Statistical papers, DOI: 10.1007/s00362-018-0997-x

Iorio, C., Frasso, G., D'Ambrosio, A. & Siciliano, R. (2018). A P-spline based clustering approach for portfolio selection. Expert systems with Applications, vol. 95, pp. 88-103. DOI: 10.1016/j.eswa.2017.11.031..

D'Ambrosio, A., Mazzeo, G., Iorio, C. & Siciliano, R. (2017). A differential evolution algorithm for finding the median ranking under the Kemeny axiomatic approach. Computers and Operations Research, vol. 82, pp. 126-138. DOI: 10.1016/j.cor.2017.01.017.

D'Ambrosio, A., Aria, M., Iorio, C. & Siciliano, R. (2017). Regression trees for multivalued numerical response variables. Expert systems with applications, vol. 62, pp. 21-28, DOI: 10.1016/j.eswa.2016.10.021

Siciliano, R., D'Ambrosio, A., Aria M. & Amodio, S. (2017) Analysis of web visit histories, part II: Predicting navigation by Nested Stump Regression Trees. Journal of Classification, vol. 34(3), pp. 473-493. DOI: 10.1007/s00357-017-9239-5.

D'Ambrosio, A. & Heiser W.J. (2016). A recursive partitioning method for the prediction of preference rankings based upon Kemeny distances. Psychometrika, vol. 81 (3), pp.774-94. DOI: 10.1007/s11336-016-9505-1.

Iorio, C., Frasso, G., D'Ambrosio, A. & Siciliano R. (2016). Parsimonious Time Series Clustering using P-Splines. Expert Systems with Applications, vol. 52, pp. 26-38. DOI: 10.1016/j.eswa.2016.01.004

Siciliano, R., D'Ambrosio, A., Aria, M. & Amodio, S. (2016) Analysis of web visit histories, part I: Distance-based visualization of sequence rules. Journal of Classification, vol. 33(2), pp. 298-324 DOI: 10.1007/s00357-016-9204-8.

Amodio, S., D'Ambrosio, A. & Siciliano, R. (2016) Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach. European Journal of Operational Research, vol. 249(2), pp. 667-676. DOI: 10.1016/j.ejor.2015.08.048.

D'Ambrosio, A., Amodio, S. & Iorio, C. (2015) Two algorithms for finding optimal solutions of the Kemeny rank aggregation problem for full rankings. Electronic Journal of Applied Statistical Analysis, vol. 8(2), 197-212. DOI: 10.1285/i20705948v8n2p197.

Catuogno, S., Allini, A. & D'Ambrosio, A. (2015). Information Perspective and Determinants of Proportionate Consolidation in Italy. An ante IFRS 11 analysis. Rivista dei Dottori Commercialisti, Fasc. 4, pp. 555-577.

Amodio, S., Aria, M. & D'Ambrosio, A. (2014). On concurvity in nonlinear and nonparametric regression models. Statistica, vol. 24(1), 81-94. DOI: 10.6092/issn.1973-2201/4599

D'Ambrosio A., Aria M. & Siciliano R. (2012). Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm. Journal of Classification, vol. 29(2), pp. 227-258. DOI: 10.1007/s00357-012-9108-1.

Montella A., Aria M., D'Ambrosio A. & Mauriello F. (2012). Data Mining Techniques for Exploratory Analysis of Pedestrian Crashes. Transportation Research Record - Journal of Transportation Research Board. Vol. 2237/2011, pp.107-116. DOI: 10.3141/2237-12.

Montella A., Aria M., D'Ambrosio A. & Mauriello F. (2011). Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. Accident Analysis & Prevention, vol. 49, pp 58-72, DOI: 10.1016/j.aap.2011.04.025

Montella A., Aria M., D'Ambrosio A., Galante F., Mauriello F. & Pernetti, M. (2011). Simulator evaluation of drivers' speed, deceleration and lateral position at rural intersections in relation to different perceptual cues. Accident Analysis & Prevention, vol. 43(6), pp. 2072-2084, DOI: 10.1016/j.aap.2011.05.030.

Montella A., Aria M., D'Ambrosio A., Galante F., Mauriello F. & Pernetti, M. (2010). Perceptual Measures to Influence Operating Speeds and Reduce Crashes at Rural Intersections. Transportation Research Record - Journal of Transportation Research Board, vol. 2149, pp. 11-20. DOI: 10.3141/2149-02

Galante F., Mauriello F., Montella A., Pernetti M., Aria M. & D'Ambrosio A. (2010). Traffic Calming Along Rural Highways Crossing Small Urban Communities: a Driving Simulator Experiment. Accident Analysis & Prevention, vol. 42(6), pp. 1585-1594. DOI: 10.1016/j.aap.2010.03.017

D'Ambrosio A. & Tutore V.A. (2009). Kemeny's axiomatic approach to find consensus ranking in tourist satisfaction. Statistica Applicata (Italian Journal of Applied Statistics), vol 20(1), pp. 21-32

Book Chapters

Sciandra, M., D'Ambrosio, A. & Plaia, A. (2020). Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix. In: Makrides A., Karagrigoriou A., Skiadas C.H. (eds). Data Analysis and Applications 3, Chapter 11, pp. 215-229. Iste-Wiley, London (UK).

Iorio C., Frasso G., D'Ambrosio A. & Siciliano R. (2018). P-Splines Based Clustering as a General Framework: Some Applications Using Different Clustering Algorithms. In: Mola F., Conversano C., Vichi M. (eds). Classification, (Big) Data Analysis and Statistical Learning, pp 183-190. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. DOI: 10.1007/978-3-319-55708-3_20.

Iorio, C., Aria, M. & D'Ambrosio, A. (2015). A New Proposal for Tree Model Selection and Visualization, in Morlini, I, Minerva, T., Vichi, M. (Eds.), Advances in Statistical Models for Data Analysis, pp. 149-156. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag, DOI 10.1007/978-3-319-17377-1_16.

 Heiser W.J. & D'Ambrosio A. (2013). Clustering and Prediction of Rankings within a Kemeny Distance Framework. In Berthold, L., Van den Poel, D, Ultsch, A. (eds). Algorithms from and for Nature and Life, pp-19-31. Springer international. DOI: 10.1007/978-3-319-00035-0_2.

Siciliano R. & D'Ambrosio A. (2012). Statistical monitoring of tourism in the knowledge era. In Morvillo A. (Ed.). Advances in Tourism Studies. McGrow-Hill, pp. 231-258.

Siciliano R., Aria M., D'Ambrosio A. & Tutore V.A. (2011). Indagine statistica sulle aspettative e priorità per soddisfare il turista a Napoli, in Becheri E., Maggiore G. (a cura di), XVII rapporto sul turismo italiano, Franco Angeli, pp. 449-470.

D'Ambrosio A. & Tutore V.A. (2011). Conditional classification trees by weighting the Gini impurity measure, in New Perspectives in Statistical Modeling and Analysis. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization, DOI10.1007/978-3-642-11363-5_31, Springer-Verlag Berlin Heidelberg, pp. 273-280

D'Ambrosio A. & Pecoraro M. (2011). Multidimensional Scaling as Visualization tool of Web Sequence Rules, in B. Fichet et al. (eds.), Classification and Multivariate Analysis for Complex Data Structures. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, pp. 307-314. DOI: 10.1007/978-3-642-13312-1_32

Siciliano, R., Aria, M. & D'Ambrosio, A. (2008). Posterior Prediction Modelling of Optimal Trees, in Proceedings in Computational Statistics (COMPSTAT 2008), 18th Symposium Held in Porto, Portugal, Brito, Paula (Ed.), Springer-Verlag, pp. 323-334

D'Ambrosio A., Aria M. & Siciliano R. (2007), Robust Tree-based Incremental Imputation Method for Data Fusion. Lecture notes in computer science 4723 (Advances in Intelligent Data Analysis), Springer-Verlag, pp 174-183.

Siciliano R., Aria. & D'Ambrosio A. (2006), Boosted incremental tree-based imputation of missing data, in Data Analysis, Classification and the Forward Search. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag, pp. 271-278.                                                                                         

Proceedings

Baldassarre, A., Concersano, C., D'Ambrosio, A., De Rooij, M & Dusseldorp, E. (2020). Discovering Interaction Effects Between Subject-Specific Covariates: A New Probabilistic Approach For Preference Data. In Pollice, A., Salvati, N & Schirripa Spagnolo, F. (Eds.), Proceedings of the 50th Scientific Meeting Of The Italian Statistical Society, pp. 1166-1170, Pearson Italia, Milano.

Nai Ruscone, M. & D'Ambrosio, A. (2020). Non-metric unfolding on augmented data matrix: a copula-based approach. In Pollice, A., Salvati, N & Schirripa Spagnolo, F. (Eds.), Proceedings of the 50th Scientific Meeting Of The Italian Statistical Society, pp. 1189-1193, Pearson Italia, Milano.

Feijt A.A.., Mol S.E., Espin C.A., D'Ambrosio A. & Heiser W.J. (2019), Instructional factors that influence learning from university lectures: Opinions of students with and without disabilities. 1st SRLD Conference, Padua. conference paper: refereed.

Sciandra, M., D'Ambrosio, A. & Plaia, A. (2018). A Projection Pursuit Algorithm for Preference Data. In Christos H. Skiadas (Ed.), Proceedings of the 5th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop, p. 101, ISAST, Athens.

Iorio, C. & D'Ambrosio, A. (2017). Time Series Clustering for Portfolio Selection. In F. Greselin, F. Mola, Ma. Zenga (Eds.), 11th Scientific Meeting of the CLAssification and Data Analysis Group of the Italian Statistical Society, p. 11-16, Universitas Studiorum, Mantova

D'Ambrosio, A., Iorio, C. & Siciliano, R. (2017). Constrained consensus bucket order. In F. Greselin, F. Mola, Ma. Zenga (Eds.), 11th Scientific Meeting of the CLAssification and Data Analysis Group of the Italian Statistical Society, p. 1-6, Universitas Studiorum, Mantova

D'Ambrosio, A., Frasso, G., Iorio, C. & Siciliano, R (2015). Probabilistic boosted-oriented clustering of time series. In Mola, Coversano (Eds.), 10th scientific meeting of the Classification and Data Analysis Group, Book of abstracts, p. 61-64, CUEC Editrice.

Iorio, C., D'Ambrosio, A., Frasso, G & Siciliano, R. (2015). Parsimonious clustering of time series. In Mola, Coversano (Eds.), 10th scientific meeting of the Classification and Data Analysis Group, Book of abstracts, p. 226-229, CUEC Editrice.

Mazzeo, G., D'Ambrosio, A. & Siciliano, R. (2015). Accurate algorithms for consensus ranking detection. In Mola, Coversano (Eds.), 10th scientific meeting of the Classification and Data Analysis Group, Book of abstracts, p. 255-258, CUEC Editrice.

Iorio, C., Aria, M. & D'Ambrosio, A. (2013). Visual model representation and selection for classification and regression trees. In Minerva, Morlini, Palumbo (Eds.), 9th meeting of the Classification and Data Analysis Group, Book of short papers, p. 276-279, CLEUP.

D'Ambrosio A. (2012). Missing Data Imputation within the Statistical learning Paradigm. Proceedings of the 46th Scientific Meeting Of The Italian Statistical Society.

Piscitelli A. & D'Ambrosio A. (2012). Assessing assumptions for data fusion procedures. Proceedings of the 46th Scientific Meeting Of The Italian Statistical Society.

Siciliano R., Tutore V.A., Aria M., D'Ambrosio A. (2010). Trees with leaves and without leaves. In 45th scientific meeting of the Italian Statistical Society.

D'Ambrosio A. & Heiser W.J. (2009). Decision Trees for Preference Rankings. Invited talk: Classification and Data Analisys 2009, Book of short papers (Catania, September 9-11, 2009), CLEUP Padova, 133-136.

Tutore V.A. & D'Ambrosio A. (2009).Three-Way Data Analysis by Tree-Based Partitioning. Classification and Data Analisys 2009, Book of short papers (Catania, September 9-11, 2009), CLEUP Padova, 641-644.

D'Ambrosio, A. & Pecoraro M. (2008). Web Structure Mining through implicit behaviors via Multidimensional Scaling, in Proceedings of the First joint meeting of the Sociètè Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society (SFC-CLADAG 2008), pp. 261-264.

Aria M. & D'Ambrosio A. (2008). A non parametric pre-grafting procedure for data fusion, Proceedings of the MTISD 2008 (Metodi, Modelli e Tecnologie dell'Informzione a Supporto delle Decisioni), Coordinamento SIBA, Università del Salento, pp. 333-336

Giordano G. & D'Ambrosio A. (2008). Multi-Class Budget Tree as weak learner for ensemble procedures, proceedings della XLIV riunione scientifica della Società Italiana di Statistica.

Aria M., D'Ambrosio A. & Siciliano R. (2007), Robust Incremental Trees for Missing Data Imputation and Data Fusion. Classification and Data Analisys 2007, Book of short papers (Macerata, September 12-14, 2007), EUM macerata, 287-290.

Siciliano R., Aria. & D'Ambrosio A. (2005), Boosted stump algorithm for missing data incremental imputation. Invited talk: CLADAG 2005, Book of Short Papers (Parma, June 6-8, 2005), MUP, Parma, 161-164.