Gabriel J. Aguiar

I'm a

About

Currently, I am Ph.D. Candidate at Virginia Commonwealth University. My research relies on Data Streams, Imbalanced data and Active Learning.

Data Scientist & Machine Learning Engineer.

  • Age: 27
  • Degree: Ph.D. Candidate
  • Email: aguiargj@vcu.edu

Resume

This is a brief description of my resume and skills. Here is a complete version of my resume.

Education

Ph.D. in Computer Science

2021 - 2024

Virginia Commonwealth University, Richmond, VA, USA

Developing research on Data Stream and Online Learning.

M.Sc. in Computer Science

2018 - 2020

State University of Londrina, Londrina, Brazil

Conduced research on Meta-Learning and Digital Image Processing [Final dissertation]

Bachelor in Computer Science

2014 - 2017

State University of Londrina, Londrina, Brazil

[Final dissertation] (PT/BR)

Professional Experience

Research Assistant

2021 - Present

VCU @ Richmond, VA, USA

  • Collaborated with a team of researchers to develop and implement cutting-edge algorithms and models for data stream analysis and online learning, with a focus on identifying and addressing challenges related to imbalanced data and semi-supervised learning.

Resident Researcher

2020 - 2021

SENAI @ Londrina, PR, Brazil

  • Developed and deployed machine learning models for the Brazilian industry, leveraging advanced algorithms and techniques to optimize production processes and improve efficiency.

Publications

Journals

  1. A comprehensive analysis of concept drift locality in data streams [Suplementary material]
    G.J. Aguiar, A. Cano; Knowledge-Based Systems, 2024.
  2. Dynamic budget allocation for sparsely labeled drifting data streams
    G.J. Aguiar, A. Cano; Information Sciences, 2023.
  3. A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework [Suplementary material]
    G.J. Aguiar, B. Krawczyk, A. Cano; Machine Learning, 2023
  4. Multiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners
    S. Barbon, R.C. Guido, G.J. Aguiar, E.J. Santana, M.L.P. Junior, H.A; Patil.Speech Communication, 2023
  5. Using Meta-Learning for Multi-target Regression
    G.J. Aguiar, E.J Santana, A.C.P.F.L. de Carvalho, S. Barbon; Information Sciences, 2022.
  6. A meta-learning approach for selecting image segmentation algorithm
    G.J. Aguiar, R.G. Mantovani, S.M. Mastelini, A.C.P.F.L. de Carvalho, G.F.C. Campos, S. Barbon; Pattern Recognition Letters, 2019.
  7. Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization
    G.F.C. Campos, S.M. Mastelini, G.J. Aguiar, R.G. Mantovani, L.F. de Melo, S. Barbon; EURASIP Journal on Image and Video Processing, 2019.

Conferences

  1. Enhancing Concept Drift Detection in Drifting and Imbalanced Data Streams through Meta-Learning
    G.J. Aguiar, A. Cano; IEEE Conference on Big Data Workshops, 2023.
  2. An active learning budget-based oversampling approach for partially labeled multi-class imbalanced data streams
    G.J. Aguiar, A. Cano; ACM/SIGAPP Symposium on Applied Computing, 2023.
  3. Towards meta-learning for multi-target regression problems
    G.J. Aguiar, E.J. Santana, S.M. Mastelini, R.G. Mantovani, S. Barbon. Brazilian Conference on Intelligent Systems (BRACIS), 2019

Contact

Feel free to contact me regarding anything or possible colaborations.

Location:

401 West Main St, Richmond, VA, USA