Research

My research interests include optimal design of experiments applied on Big Data and sustainable supply chain risk modelling, with main focus on the Marine line of business. 

In the past, I used different Statistical Machine Learning models and Data Visualization Tools for the pricing and monitoring phases of sales and promotions in retails and the application of graphical models in risk assessment.

Articles

L. Deldossi, E. Pesce, C. Tommasi (2023): Accounting for outliers in optimal subsampling methods. Statistical Papers, 1-17.

E. Pesce, F. Rapallo, E. Riccomagno, H.P. Wynn (2022). Generation of all randomizations using circuits. Annals of the Institute of Statistical Mathematics, 1-22.

E.Pesce, F. Porro, E. Riccomagno (2022). Large datasets, bias and model‐oriented optimal design of experiments. Quality and Reliability Engineering International.

F. Carli, E . Pesce, E. Riccomagno, A. Mazza (2022). Combination of Autoregressive Graphical Models and Time Series Bootstrap Methods for Marine Losses Forecast: a Pilot Study. Submitted.

E. Pesce, E. Riccomagno, H.P. Wynn (2019). Experimental Design Issues in Big Data. The Question of Bias. In: Greselin F., Deldossi L., Bagnato L., Vichi M. (eds) Statistical Learning of Complex Data. CLADAG 2017. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham, pp. 193-201.

Talks

An interactive Shiny dashboard to quantify the carbon footprint of marine insurers' Hull and Machinery portfolios, Zurich R User Meetup on Re-Insurance and R (PartnerRe, Zurich, 5 July 2023).

Sustainable supply chains: Supporting the transition starting with marine decarbonisation, ETH MAS Sustainability Programme (Centre for Global Dialogue, Zurich, 28 June 2023).

Optimal subset selection without outliers. Data Science, Statistics and Visualisation Conference (National Cheng Kung University, Tainan, Taiwan, 27-29 June 2022).

Quantum Cities: A data-centric approach to sustainable cities. Nordic Data Science & Machine Learning Summit (Online, May 2021).

Large Datasets, Bias and Model Oriented Optimal Design of Experiments. Incontro di Statistica Matematica (Sestri Levante, 27-28 January 2020).

Sample Selection for Biased Models. PhD Seminar (Genoa, 25 September 2019).

Large Datasets, Bias and Model Oriented Optimal Design of Experiments. International Workshop mODa 12 (Smolenice, 23-28 June 2019).

Optimal Design of Experiments for Large Datasets. 16th Workshop on Quality Improvement Methods (Dortmund, 1-2 June 2018).

Posters 

Optimal Design of Experiments for Large Datasets. Advance School on Complexity and Emergence: Ideas, Methods, with a special attention to Economics and Finance (Como, 22-27 July 2018)