Pesquisadores do DCC são premiados no programa de Bolsas de Pesquisa do Google para a América Latina

sex, 25/08/2017 - 10:45
Português, Brasil

Pelo quinto ano seguido, o Google anunciou  uma nova classe de 27 pesquisadores na área de Ciência da Computação que receberão, ao longo dos próximos doze meses, aproximadamente 2 milhões de reais para avançar seus estudos. Desde seu lançamento, em 2013, o Latin American Research Awards -- ou LARA --  já beneficiou 46 projetos, e mais de 100 pesquisadores, entre alunos de pós-graduação e orientadores.

Os bolsistas LARA 2017 do DCC são:
 
Marcos André Gonçalves
Clebson C. A. de Sá
Universidade Federal de Minas Gerais, Brasil
Optimizing Ensembles of Boosted Additive Bagged Trees for Learning-to-Rank
The goal is to optimize a ranked list of documents related to specific information needs by training a model with documents already defined as relevant by specialists  using a "Learning-to-Rank"  process based on a combination of Machine Learning techniques.
 
 
Wagner Meira Junior
Roberto C. S. N. P. Souza
Universidade Federal de Minas Gerais, Brasil
Hot Spot Mining from Case-Control Trajectories
The goal is to detect hotspots, that is, regions where the chance of occurrence of a target event (e.g., being infected by a disease) is higher compared to the rest of the area under analysis, based on trajectories recorded by personal devices.
 
Pedro Olmo Stancioli Vaz de Melo
Túlio Corrêa Loures
Universidade Federal de Minas Gerais, Brasil
Discussion-Based Entity Representation
The goal is to develop a method for extracting relevant information through comments from discussions made online. Eventually will create a tool that could create a summary about comments from the same topic, and group them with other related subjects.
 
 
Fernando Magno Quintão Pereira
Junio C. Ribeiro da Silva
Universidade Federal de Minas Gerais, Brasil
Intelligent DVFS
This project aims to create a prototype for reducing energy consumption of Android applications at a minimum price in program’s performance. This prototype will use reinforcement learning to adapt itself to the different ways in which those applications can be used.