Daniel Carlos Guimarães Pedronette

Associate Professor
Computer Science
Department of Statistics, Applied Mathematics and Computing (DEMAC)
São Paulo State University (UNESP)

Education
2009-2012:     PhD in Computer Science, Institute of Computing, UNICAMP
2006-2008:     MsC in Computer Science, Institute of Computing, UNICAMP
2001-2005:     BSc in Computer Science, DEMAC, UNESP

Research
Information Retrieval; Content-Based Image Retrieval; Re-Ranking; Rank Aggregation; Unsupervised Learning; Semi-Supervised Learning; Parallel Computing

I received the BSc in Computer Science from the São Paulo State University (UNESP), Brazil, in 2005 and received the MSc and PhD degrees in Computer Science from the University of Campinas (UNICAMP), Brazil, in 2008 and 2012.
I am currently an Associate Professor at the Department of Statistics, Applied Mathematics and Computing of São Paulo State University (UNESP) and a Collaborator Researcher at Institute of Computing - UNICAMP. I also serve as an Associate Editor of Pattern Recognition journal.

Selected and Recent Articles/Lectures:

Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning

VALEM, L. P. ; PEDRONETTE, DANIEL C. G. ; LATECKI, L. J. . Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning., IEEE Transactions on Image Processing, 2023.

[Code UDLF - C++] [Code Python - pyUDLF] [Arxiv pdf]


pyUDLF: A Python Framework for Unsupervised Distance Learning Tasks

LETICIO, G. R.; VALEM, L. P.; LOPES, L. T.; PEDRONETTE, DANIEL C. G. . pyUDLF: A Python Framework for Unsupervised Distance Learning Tasks., ACM Multimedia, 2023.

[Code] [Presentation]


A BFS-Tree of Ranking References for Unsupervised Manifold Learning

PEDRONETTE, D. C. G.; VALEM, L. P.; TORRES, R. S. . A BFS-Tree of Ranking References for Unsupervised Manifold Learning., Pattern Recognition, 2021.



Rank-based self-training for graph convolutional networks

PEDRONETTE, D. C. G.; LATECKI, L. J. . Rank-based self-training for graph convolutional networks., Information Processing and Management, 2021.



Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking.

PEDRONETTE, D. C. G.; VALEM, L. P.; ALMEIDA, J., TORRES, R. S. . Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking., IEEE Transactions on Image Processing, 2019.



Unsupervised Manifold Learning through Reciprocal kNN Graph and Connected Components for Image Retrieval Tasks

PEDRONETTE, D. C. G.; GONÇALVES, F. M. F.; GUILHERME, I. R. . Unsupervised Manifold Learning through Reciprocal kNN Graph and Connected Components for Image Retrieval Tasks, Pattern Recognition, 2018.





* Rank-based Unsupervised Learning for Image Retrieval
Seminar at Polytechnique Montréal, 2022

* Lecture: Unsupervised Distance Learning for Image Retrieval

  Presented at following universities:
Institute of Science and Technology (ICT), Federal University of São Paulo (UNIFESP), 2020.
Instituto de Ciências Matemáticas e de Computação (ICMC), University of São Paulo (USP), 2019.
Department of Computer Science (DCC), Federal University of Minas Gerais (UFMG), 2015.
Institute of Computing (IC), University of Campinas (UNICAMP), 2015.