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Optimization Methods for Data Analysis and Machine Learning

Grant number: 18/07551-6
Support Opportunities:Scholarships abroad - Research
Start date: October 01, 2018
End date: January 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Mathematics - Applied Mathematics
Principal Investigator:Paulo José da Silva e Silva
Grantee:Paulo José da Silva e Silva
Host Investigator: Jose Claudio Teixeira e Silva Junior
Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Institution abroad: New York University, United States  
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

Data science is a very active area that tries to uncover the informationhidden in the vast amount of data collected each day. It can be seen as acombination of techniques from Statistics, Computer Science, and MachineLearning, that results in models that must be optimized using mathematicalalgorithms.In this project, we propose to use state-of-the-art optimization techniques todevelop computer solutions for Data Analysis problems. In particular, we planto focus on $\ell_1$-regularized models, like lasso, and on variations ofSupport Vector Machines. This endeavor must be taken in tight collaborationwith other experts of Data Analysis in order to accomplish results for realworld problems.

News published in Agência FAPESP Newsletter about the scholarship:
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Scientific publications
(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)
DORAISWAMY, HARISH; TIERNY, JULIEN; SILVA, PAULO J. S.; NONATO, LUIS GUSTAVO; SILVA, CLAUDIO. TopoMap: A 0-dimensional Homology Preserving Projection of High-Dimensional Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v. 27, n. 2, p. 561-571, . (18/07551-6, 13/07375-0, 16/04190-7, 18/24293-0)