Monday, May 27

❖ Pre-workshop

➢ Proof of concept special session (10:30-12:30)

➢ Lunch break (12:30-13:45)

❖ First Day 

➢ Introduction (13:45-14:00)

➢ Keynote Session (14:00-15:00)

■ Speaker: Bruno Carballa Smichowski

■ Speaker: Nathalie Baracaldo [online]

➢ Paper session (15:00-16:30), 20-minute presentation + 10 Q&A

Roberto Leombruni, Federated Learning for policy evaluation: a privacy-preserving collaborative environment for causal data modelling

Giovanni Cerulli, Data-driven policy learning, the role of FOSSR

● Alessandro Buratto, Strategic Participatory Sensing for Age of Federated Information in Privacy and Energy Aware Scenarios

➢ Coffee Break (16:30-17:00)

➢ Paper session (17:00-18:30), 20/10-minute presentation + 10/5 Q&A 

Carlos Daniel Santos, Machine Learning Counterfactual Imputation with Endogenous Covariates: The Pandemic’s Effect on Peer-to-Peer Accommodation

● Raffaele Perego, Learning to Rank for Non-Independent and Identically Distributed Datasets

● Fabrizio Patriarca, Federated Learning for the Analysis of Educational and Labor Market Trajectories of Italian University Students

Gergely Németh, Observations of Using Model Complexity Reduction as a Defense Against Membership Retrieval [online] 

➢ Social Dinner (19:00)

Tuesday, May 28

❖ Second Day

➢ Keynote Session (TBA) (9:30-10:30)

■ Speaker: Gabriele Tolomei

■ Speaker: Jermain Kaminski

➢ Coffee Break (10:30-11:00)

➢ Paper session (11:00-12:30), 20-minute presentation + 10 Q&A

Maria Cristina Maurizio, Machine learning for the Italian public employment system

● John Darrell Van Horn, Pain and Pleasure in Human Neuroimaging Data Sharing

● Junaid Ahmed, Classifying Bot and Human Driven Content Among Pro and Anti Communities of Vaccine and Environmental Activism

➢ Social Lunch (12:30-14:00)

➢ Paper session (14:00-15:30), 20-minute presentation + 10 Q&A

● Ge Zheng, An Adaptive Federated Learning System for Information Sharing in Supply Chains

● Brian Wright, Evolution of Data Science as an Academic Field

José A. Cafiero, Analyzing export performance using machine learning models [online] 

➢ Final remarks and farewell (15:30)