Summer School on Data Science

09h00 - 18h30

About Event

The School of Applied Mathematics of Fundação Getulio Vargas (FGV EMAp) promotes, between January 23rd and 27th, the “Summer School in Data Science.” The event aims to bring together academics and researchers in the area to discuss and present breakthroughs in the theory and application of data science. 

Data science has become central to most analytical and decision-making processes. We’ll bring together leading experts to present and share the latest advances in data science theory and applications. Researchers and professionals will be present in various academic data science disciplines, including statistics, machine learning, big data, and computer science.

The meeting will have technical content for students, teachers, and researchers. The program includes four short courses and twelve plenary talks. The short courses will use the methodology in which students will learn in practice; the topics are: Topological Data Analysis, Gaussian Processes, GNN for Network Medicine, and Machine Learning applied to Earth observations. Plenary talks will be given by national and international researchers who are experts in various areas of data science.

Apoiadores / Parceiros / Patrocinadores


Claudio T. Silva

New York University

Explainability, Interpretability and Visualization of Machine Learning Models

Juliana Freire

New York University

Dataset Search for Data Discovery, Augmentation and Explanation

Luis G. Nonato

University of São Paulo

Graph Signal Processing: from data science to machine learning

Raul Queiroz Feitosa

Pontifical Catholic University of Rio de Janeiro

Uncertainty in Deep Learning  

João Dorea

University of Wisconsin-Madison

Computer Vision and Machine Learning for Optimal Farm Management  

Dário Oliveira

Getulio Vargas Foundation

Machine learning to approach sustainability using scarcely labeled to unlabeled earth observation data  

Haiyuan Yu

Cornell University

3D structural modeling of whole interactomes leads to better understanding of disease mechanisms and better drug design  

João Carlos Setubal

University of São Paulo

Prediction of bacterial phenotypes from genomes using machine learning  

Helder Nakaya

University of São Paulo

Network Medicine: Integrating data towards a better understanding of human diseases and vaccination  

Luis Lamb

Federal University of Rio Grande do Sul

Frédéric Chazal

INRIA Saclay

Measure Vectorization for Automatic Topologically-Oriented Learning with guarantees

Diego Mesquita

Getulio Vargas Foundation

An Introduction Graph Neural Networks and how to explain them  


Centro Cultural FGV

Acesso pelo Edifício Sede da FGV

Praia de Botafogo, 186

Botafogo, Rio de Janeiro - RJ, 22250-900


Acesso pelo Edifício Sede da FGV

Praia de Botafogo, 190

Botafogo, Rio de Janeiro - RJ, 22250-900

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