Convex optimization and extensions applied in Data Science

• Grant number: 22/00380-7
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: September 01, 2022
• End date: March 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Mathematics - Applied Mathematics
• Principal Investigator: Paulo José da Silva e Silva
• Grantee: Somayeh Khezri
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 18/24293-0 - Computational methods in optimization, AP.TEM

Abstract

Data Science problems have attracted tremendous interest in recent years due to society's unprecedented ability to gather data thatmust be processed.In this context, there has been a resurgence of convex optimization techniques and their extensions, which have been proven to be essential for training of the machine learning models that are fundamental in modern Artificial Intelligence (AI).This project seeks to investigate the use of modern convex optimization techniques in problems in the field of Data Science and Machine Learning such as learning from noisy data or using decomposition methods to induce parallelism or to decouple the problem allowing its solution