Mercado Livre - Brazil
Institute of Mathematical and Computer Sciences, Universidade de São Paulo - USP at São Carlos, Brazil
I explore fair, explainable, and human-centered ways to use machine learning to make technology truly serve people. At Mercado Livre, I lead strategy and research on foundation models, AI agents, and scalable machine learning systems for production. As an Associate Professor at the University of São Paulo (ICMC/USP), my work focuses on deep and representation learning across multiple data modalities. I am a recipient of the Google Latin America Research Award and a CNPq Productivity Fellow since 2018, and have been listed among the top 2% most influential scientists worldwide (Stanford–Elsevier C-score).
Main Research fields: Machine Learning/Representation Learning; Multimodal domains: audio, speech, image and tabular data.
Recent/highlighted papers (see Publication for more details):
YourTTS: Towards zero-shot multi-speaker TTS and zero-shot voice conversion for everyone (ICML 2022)
Sketchformer: Transformer-based representation for sketched structure. (CVPR 2020)
Everything you wanted to know about Deep Learning for Computer Vision but were afraid to ask (SIBGRAPI Tutorials 2017: papers, slides, code)
Check out my Book on Machine Learning (Theory and Practice), details below!
Previous experiences/education:
Academic visitor (2016-2017); Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK.
Assistant Professor (2009-2010); Universidade Federal de Viçosa, Rio Paranaíba, MG, Brazil.
PhD (2008) and MSc (2004) at the Universidade Federal de São Carlos (UFSCar), SP, Brazil.
 PhD internship (2007) at IEETA, Universidade de Aveiro, Portugal.
External sites (papers, code and citations):
Github - GoogleCitations- SCOPUS - DBLP - Researcher-ID - ORCID - Linkedin - Lattes Platform CV (in Portuguese) - CV in English (pdf)
Address:
Instituto de Ciências Matemáticas e de Computação
Universidade de São Paulo - Campus de São Carlos
P.O. Box 668 / 13566-590 /  São Carlos, SP, Brasil
Rodrigo F. Mello . Moacir A. Ponti
Presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible.
Moacir A. Ponti
Uma sistematização das pesquisas e trabalhos realizados pelo autor, delineando suas linhas de pesquisa, em particular no estudo das diversas etapas que compõe o pipeline do processamento de sinais, imagens e vídeos com vistas a melhoria dos sistemas de conhecimento de padrões nesses domínios. Primeiramente, são descritas as contribuições no Pré-processamento de imagens. Em segundo lugar, são apresentadas as contribuições na área de Extração de Espaços de Características. A seguir, são descritos os estudos sobre Reconhecimento de Padrões. Neste documento, são apresentados os fundamentos e os pressupostos com os quais as abordagens têm sido exploradas, destacando as contribuições e os desenvolvimentos realizados anos entre 2010-2017.