Young Bayesians and Big Data for Social Good
Jeunes bayésiens et Big Data pour le bien de l’humanité

(event 1911)
Dates: 23-26 November 2018
Place: CIRM (Marseille Luminy, France)


Le concept de  » données et statistiques pour le bien social  » suscite un intérêt et un engagement international croissant, les volontaires et les organisations travaillant sur des questions telles que les droits de l’homme, les migrations, la justice sociale etc. Il existe un intérêt international proportionnel pour la modélisation et le calcul bayésien, en particulier dans le domaine des Big Data.
Le but de ce workshop  est de réunir ces deux secteurs d’activité.

There is increasing international interest and engagement in the concept of ‘data and statistics for social good’, with volunteers and organisations working on issues such as human rights, migration, social justice and so on. There is commensurate international interest in Bayesian modelling and computation, particularly in the area of Big Data. 

The purpose of this workshop is to build on an emerging endeavour to bring these two areas of activity together. Participants in the workshop will share research into Bayesian methods and big data for social good, as well as discuss related problems that might be addressed using Bayesian approaches. In addition to formal presentations, participants will form collaborative groups to work on new problems and present their findings at a final session on the last day of the workshop which overlaps with a more general conference on ‘Bayesian Statistics in the Big Data Era’.

The workshop will provide opportunities for early career researchers around the world who are interested in issues of social good to form lifelong international networks for their immediate and future careers. The semi-structured environment will encourage and progress new research opportunities. The anticipated outputs of the workshop include outlines of journal articles and/or chapters of a new book on this topic, as well as a starting place for  practical solutions to problems posed. ​

  • David Corliss (Peace Work) – Bayesian Capture – Recapture in Social Justice Research
  • Ethan Goan (Queensland University of Technology) – Deep Learning we can trust
  • Charles Gray (La Trobe University) – Open and reproducible data analysis
  • Jacinta Holloway (Queensland University of Technology) – Statistical analysis of big data to measure sustainable development goals
  • Brunero Liseo (Sapienza University Roma) – A Bayesian approach for Normal regression with deduplicated data
  • Kerrie Mengersen (QUT Brisbane) – Bayesian Networks for conservation
  • Christian P. Robert  (Université Paris-Dauphine) – Inference in generative models using the Wasserstein distance
  • Em Rushworth (Queensland University of Technology)Working With Constraint: Towards a Bayesian Approach for Compositional Data
  • Andrea Tancredi (Sapienza University Roma) – A unified framework for de-duplication and population size estimation
  • Gajendra Vishwakarma (Indian Institute of Technology Dhanbad) – Bayesian State-Space Modeling in Gene Expression Data Analysis: An Application with Biomarker Prediction
  • Alexander Volfovsky (Duke University) – Design of experiments, networks and social good
  • Bihan Zhuang (Duke University) – Entity Resolution with an Application: the El Salvadorian Conflict Data