Giulia Vilone

Education

2018-ongoing

PhD in Artificial Intelligence

Technological University Dublin

A four-year project focusing on Explainable Artificial Intelligence.

2011-2013

Diploma in Actuarial Sciences

British Institute and Faculty of Actuaries

Partly-qualified actuary.

2005-2007

MSc degree in Mathematical Engineering

Polytechnic of Milan

Master degree in financial mathematics with thesis on stochastical differential equations.

2002-2005

BSc degree in Mathematical Engineering

Polytechnic of Milan

Undergraduate degree covering different aspects of applied mathematics with final thesis on game theory.

Experience

2012-2020

Senior Data Scientist

Creme Global, Dublin, Ireland

I was involved in data wrangling and analyses, development of predictive models implemented on cloud-based software tools and writing of scientific reports.

2011-2012

Financial Analyst

Mediolanum International Funds Ltd., Dublin, Ireland

I carried out profitability analyses of new life insurance products and supervised the implementation of the Solvency II procedures to meet risk management standards.

2010-2011

Policy Administrator

The Lawrence Life Assurance Co. Ltd., Dublin, Ireland

My main task involved portfolio administration activites and policy payments.

2008-2010

Financial Services Consultant

Capgemini Italia, Milano, Italy

I carried out operational risk analyses, prepared budget, forecast reports and investment planning reports and supervised portfolio administration activities.

Skills

Python

80^%

R

60^%

MySQL

75^%

Keras & Tensorflow

70%

Matlab

55%

MS Excel

95%

Data Visualization Tools

85%

Google Cloud

60%

GIT

50%

Scientific Publications

April 2013

Compiled European Food Consumption Database

O’ Mahony C., & Vilone G.

External Scientific Report, EFSA Journal, Supporting Publications 2013: EN-415, DOI: https://doi.org/10.2903/sp.efsa.2013.EN-415

September 2014

Statistical method to assess usual dietary intakes in the European population

Vilone G., Comiskey D., Heraud F., & O'Mahony C.

Food Additives & Contaminants: Part A, 31(10), 1639-1651, DOI: https://doi.org/10.1080/19440049.2014.955886

November 2017

Critical and systematic evaluation of data for estimating human exposures to 2,4-dichlorophenoxyacetic acid (2,4-D) – quality and generalizability

LaKind, J. S., Burns, C. J., Naiman, D. Q., O’Mahony, C., Vilone, G., Burns, A. J., & Naiman, J. S.

Journal of Toxicology and Environmental Health, Part B, 20(8), 423-446, DOI: https://doi.org/0.1080/10937404.2017.1396704

December 2018

Exposure to selected preservatives in personal care products: case study comparison of exposure models and observational biomonitoring data

Aylward, L., Vilone, G., Cowan-Ellsberry, C., Arnot, J. A., Westgate, J. N., O’Mahony, C., & Hays, S. M.

Journal of exposure science & environmental epidemiology, 30, 28-41, DOI: https://doi.org/10.1038/s41370-018-0104-3

March 2020

Probabilistic modelling of exposure to pesticide residues in foods and tobacco

Gedge, L. M., Hawryluk, I., Skelly, M. G., & Vilone, G.

International Journal of Environment, Agriculture and Biotechnology, 5(2). Retrieved from http://journal-repository.com/index.php/ijeab/article/view/1726

December 2020

A comparative analysis of rule-based, model-agnostic methods for explainable artificial intelligence

Vilone, G., Rizzo, L., & Longo, L.

Proceedings of The 28th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Republic of Ireland, December 7-8, 2020. Retrieved from http://ceur-ws.org/Vol-2771/AICS2020_paper_33.pdf

May 2021

Notions of explainability and evaluation approaches for explainable artificial intelligence

Vilone, G., & Longo, L.

Information fusion, DOI: https://doi.org/10.1016/j.inffus.2021.05.009

August 2021

Classification of Explainable Artificial Intelligence Methods through Their Output Formats

Vilone, G., & Longo, L.

Machine learning and knowledge extraction, DOI: https://doi.org/10.3390/make3030032

Other Publications

August 2016

Data dining

Vilone G.

The Actuary, pp. 27-29. Retrieved from https://www.theactuary.com/features/2016/08/2016/08/02/data-dining