Paolo Giudici

Professor of Data Science at the University of Pavia

 

Paolo Giudici is a Full Professor of Data Science and Statistics at the Department of Economics and Management of the University of Pavia, where he has supervised about 150 Master’s students and 12 Ph.D. students. Most of them currently work in the financial industry, in IT/consulting companies or as academic researchers.

Researcher author of about 200 scientific publications, among which a Wiley book on “Applied data mining” and 31 papers in A-level journals (2017 ANVUR classification), with 3070 total citations and an h-index of 25 (see Google Scholar for the details). The corresponding research profile is that of a data scientist, focused on statistical modelling, especially in Bayesian analysis, Computational statistics, Graphical network models; and on machine learning applications in finance, especially in Customer scoring, Operations quality and Credit risk measurement.

Director of the University of Pavia Data Science laboratory (formerly Data Mining laboratory) which, since 2001, carries out research and consulting projects, for leading institutions such as the European Research Council, the Bank of Italy, Consob, ISTAT, the Italian Banking Association, Cariplo Foundation, Intesa San Paolo, Unicredit, BancoBPM, UBI, MPS, Banca Popolare di Sondrio, Credito Valtellinese, Accenture, KPMG, Mediaset, Mondadori, SAS Institute, Sky.

Board of Directors member of the Credito Valtellinese Banking group; Advisory Board member of BABB, the Blockchain Based Account Bank; Research fellow at the Bank for International Settlements; Honorary member and President of the scientific committee of the Association of the Italian Financial Industry Risk Managers.

RESEARCH PAPERS

37. (2017) Paolo Giudici, Peter Sarlin, Alessandro Spelta. The interconnected nature of financial systems: direct and common exposures. To appear in Journal of Banking and Finance.

36. (2017) Paolo Giudici, Laura Parisi. Sovereign risk in the Euro area: a multivariate stochastic process approach. To appear in Quantitative Finance.

35. (2017) Raffaella Calabrese, Johan Elkink, Paolo Giudici. Measuring bank contagion using binary spatial regression models. To appear in Journal of the Operational Research Society, 1-9.

34. (2017) Paola Cerchiello, Paolo Giudici, Giancarlo Nicola: Twitter data models for bank risk contagion. To appear in Neurocomputing

33. (2016) Shatha Hashem, Paolo Giudici. NetMES: a network based marginal expected shortfall measure. Journal of network theory in finance, 2(3), 1-36

32. (2016) Paola Cerchiello, Paolo Giudici. Conditional graphical models for systemic risk estimation. Expert systems with applications. Vol. 43, pp. 165-174

31. (2016) Paolo Giudici, Alessandro Spelta. Graphical network models for international financial flows. Journal of Business and Economic Statistics, 34 (1), pp. 126-138.

30. (2016) Paola Cerchiello, Paolo Giudici: Categorical network models for systemic risks. Quality and Quantity Volume 50, Issue 4, pp 1695–1713

29. (2016) Paola Cerchiello, Paolo Giudici A Bayesian h-index: how to measure research impact. Statistical analysis and data mining.

28.(2015) Paola Cerchiello, Paolo Giudici: How to measure the quality of financial tweets. Quality and Quantity , 50(4), 1-19

27. (2015) (0.46) Silvia Figini, Lijun Gao, Paolo Giudici. Bayesian operational risk. The journal of operational risk, 10 (2), pp. 1-16.

26. (2015) Raffaella Calabrese, Paolo Giudici. Estimating bank default with generalised extreme value regression models. Journal of the Operational Research society (2015) , pp. 1-10.

25. (2014) Paola Cerchiello, Paolo Giudici. On a statistical H-index. Scientometrics, 99, pp. 299-312.

24.(2013) (Silvia Figini, Paolo Giudici) Measuring risk with ordinal variables. The journal of operational risk,vol.8, n.2, pp.35-43.

23.(2012) (Paola Cerchiello, Paolo Giudici). Fuzzy methods for variable selection in operational risk management. The Journal of operational risk, vol. 7, n.4

22.(2012) (Paola Cerchiello, Paolo Giudici). Non parametric statistical models for on-line text classification. Advances in Data Analysis and Classification. vol. 6, issue 4, pages 277-288

21 .(2011) (Silvia Figini, Paolo Giudici). Statistical merging of rating models, Journal of the operational research society, vol- 62, pp. 1067-1074.

20. (2008) (Silvia Figini, Paolo Giudici) Statistical models for e-learning data. Statistical methods and applications, vol. 18, n.2, pp. 293-304.

19. (2008) (Luciana Dalla Valle, Paolo Giudici). A Bayesian approach to estimate the Marginal loss distributions in Operational Risk management. Computational Statistics and data analysis, 52, 3107-3127

18. (2007) (Elvio Bonafede, Paolo Giudici), Bayesian networks for enterprise risk assessment. Physica A: Statistical Mechanics and its applications vol. 382, n.1, pp 22-28.

17. (2007) (Silvia Figini, Paolo Giudici, Pierpaolo Uberti, Any Sanyal). A statistical method to optimize the combination of internal and external data in operational risk management. The Journal of Operational Risk, vol.2 n.4, pp. 69-78.

16. (2007) (Elvio Bonafede, Paola Cerchiello, Paolo Giudici), Statistical models for Business Continuity Management, The Journal of Operational Risk, vol 2, n. 4, pp. 79-96.

15. (2004) (Chiara Cornalba, Paolo Giudici) Statistical models for operational risk management. Physica A: Statistical Mechanics and its applications, vol. 338, n.1-2, pp.166-172 Impact Factor 1.88

14. (2004) (Eva Fronk, Paolo Giudici). Markov Chain Monte Carlo model determination for Gaussian DAG models. Statistical Methods and Applications, vol. 13, n. 3, pp. 259-273

13. (2003) (Roberto Castelo, Paolo Giudici) Improving MCMC model search for data mining. Machine learning, vol. 50, n. 1-2, pp 127-158

12. (2003) (Steve Brooks, Paolo Giudici, Gareth Roberts). Efficient construction of reversible jump MCMC proposal distributions (with discussion). Journal of The Royal Statistical Society, series B, vol. 1 (65), pp 3-55.

11. (2003) (Steve Brooks, Paolo Giudici, Anne Philippe). Non parametric convergence assessment for MCMC model selection. Journal of computational and graphical statistics, vol. 12, n.1, pp.1-22

10. (2003) (Petros Dellaportas, Paolo Giudici, Gareth Roberts). Bayesian inference for non-decomposable Gaussian graphical models. Sankhya, series A, vol. 65, n.1, pp. 43-55.

9. (2003) (Paolo Giudici) Applied data mining: statistical methods for business and industry. Wiley, London. Chinese translation, 2005. Second edition (with Silvia Figini), 2009. Italian editions: Mc Graw Hill 2001, 2005.

8. (2002) (Paolo Giudici, Gianluca Passerone) Data Mining of association structures to model consumer behaviour. Computational Statistics and data analysis, vol. 38, n.4, pp 533-541

7. (2001) (Robert Castelo, Paolo Giudici) Association models for web mining. Data mining and Knowledge discovery, vol. 5, n.3, pp. 183-196

6. (2001) (Paolo Giudici, Elena Stanghellini) Bayesian Inference for graphical factor analysis models. Psychometrika, vol. 66, n.4, pp. 577-592

5. (2000) Paolo Giudici, Tobias Ryden, Pierre Vandekerkhove, 32). Likelihood-ratio tests for hidden Markov models. Biometrics, vol. 56, pp. 742-747

4. (2000) (Steve Brooks, Paolo Giudici) MCMC Convergence Assessment via Two-way ANOVA. Journal of computational and graphical statistics, vol. 9, pp. 266-273

3. (2000) (Paolo Giudici, Leo Knorr-Held, Gunter Rasser) Modelling categorical covariates in Bayesian disease mapping. Statistics in medicine, vol. 19, pp. 2579-2593.

2. (1999) (Paolo Giudici, Peter Green). Decomposable graphical gaussian model determination. Biometrika, vol. 86, n.4, pp 785-801.

1.(1995) (Paolo Giudici) Bayes factors for zero partial covariances. Journal of Statistical Planning and Inference, vol. 46, n.3, pp. 161-174.

RESEARCH FIELDS

Computer Science, Economics, Finance, Statistics

KEYWORDS

Graphical models, Financial networks, Systemic risk

AFFILIATIONS

University of Pavua

Link to personal website