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
Speakers
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
Location
Endereço
Acesso pelo Edifício Sede da FGV
Praia de Botafogo, 190
Botafogo, Rio de Janeiro - RJ, 22250-900