Join us for a three-day intensive training organized by the Statistical Leaning Laboratory (SaLLy; www.SaLLy.ufba.br) and the State University of Campinas (UNICAMP), sponsored by the Applied Malaria Modelling Network (AMMnet; https://ammnet.org/), aiming to equip participants with the necessary skills and knowledge to employ artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in modeling malaria (and infectious diseases in general) spread and intervention strategies in Brazil and Latin America. This training aligns with AMMnet’s mission, leveraging advanced ML and DL methodologies to enhance disease surveillance, prediction, and control efforts, ultimately contributing to reducing malaria transmission and burden in the region.
Dates: August 28-30, 2024
Time: From 09:00 to 16:45 every day
Venue: Hebe de Azevedo Biagioni Auditorium, Institute of Mathematics, Statistics and Scientific Computing (IMECC), Rua Sérgio Buarque de Holanda, 651, 13083-859, Campinas, SP, Brazil. https://www.ime.unicamp.br/administracao/informacoes-para-visitantes/como-chegar
Statistical Learning Laboratory - UFBA
IMECC, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo
- Veronica Gonzalez-Lopez (Co-Chair)
- O. Olawale Awe (Co-Chair)
- Paulo Canas Rodrigues
- João Vitor Rocha Silva
- Deborah Awe
- O. Olawale Awe, Research Coordinator of the SaLLy; President/National Coordinator of AMMnet in Brazil, Vice-President of the International Association for Statistical Education (IASE)
- Paulo Canas Rodrigues, Director of the SaLLy, President of the International Society for Business and Industrial Statistics (ISBIS)