Márcus Costa 🇧🇷
Márcus Costa

PhD Student in Computer Science

University of São Paulo (ICMC-USP)

About Me

Márcus Costa is a Ph.D. Student at the University of São Paulo (USP) - Brazil, in the Laboratory of Graphics, Imaging, Visualization and Analytic (GIVA). His research concerns multimodal vision language models to representation learning from 3D data. He received his M.Sc. degree in Computer Science from the University of São Paulo (USP) in 2024. He completed his B.Sc. in Computer Science at the Federal University of Rondônia (UNIR). His main research interest includes Machine Learning, Deep Learning, Computer Vision and Data Science. Also, he has technical skills in C/C++, Java, Python, Git, TensorFlow, Pytorch, Scikit-learn, OpenCV, Docker and LaTeX.

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Interests
  • Data Science 🎲
  • Computer Vision 🖥
  • Machine Learning 🤖
  • Vision Language Models 💬
Education

  • PhD Computer Science, ongoing

    University of São Paulo (ICMC-USP)

  • MSc Computer Science

    University of São Paulo (ICMC-USP)

  • BSc Computer Science

    Federal University of Rondônia (UNIR)

Featured Publications
Márcus v. L. Costa, Erikson J. De Aguiar, Lucas S. Rodrigues, Caetano Traina, Agma J. M. Traina (2024). DEELE-Rad: exploiting deep radiomics features in deep learning models using COVID-19 chest X-ray images. Health Information Science and Systems (HISS).
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Márcus v. L. Costa, Erikson J. De Aguiar, Lucas S. Rodrigues, Jonathan S. Ramos, Caetano Traina, Agma J. M. Traina (2023). A Deep Learning-based Radiomics Approach for COVID-19 Detection from CXR Images using Ensemble Learning Model. 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS).
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Erikson J. De Aguiar, Márcus v. L. Costa, Caetano Traina, Agma J. M. Traina (2023). Assessing Vulnerabilities of Deep Learning Explainability in Medical Image Analysis Under Adversarial Settings. 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS).
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Jonathan S. Ramos, Erikson J. De Aguiar, Ivar v. Belizario, Márcus v. L. Costa, Jamilly G. Maciel, Mirela T. Cazzolato, Caetano Traina, Marcello H. Nogueira-Barbosa, Agma J. M. Traina (2022). Analysis of vertebrae without fracture on spine MRI to assess bone fragility: A Comparison of Traditional Machine Learning and Deep Learning. 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS).
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Teaching
Example Talk
Example Talk

An example talk using Hugo Blox Builder's Markdown slides feature.

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DEELE-Rad: exploiting deep radiomics features in deep learning models using COVID-19 chest X-ray images

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Projects

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Pandas
Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures.

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