Erikson Aguiar

PhD Candidate

University of São Paulo -- ICMC/USP

About

Erikson Aguiar is a Ph.D. candidate at the University of São Paulo (USP), Brazil, in the Database and Image Group (GBDI). His research concerns security and privacy in Machine Learning for healthcare applications. He received his M.Sc. degree in Computer Science from the University of São Paulo (USP) in 2021. He completed his B.Sc. in Computer Science at the State University of Northern Paraná (UENP). His main research interest includes Security & Privacy, Machine Learning, Deep Learning, and Computer Vision. Also, he has technical skills in C, Golang, Node.js, Python, Git, TensorFlow, Pytorch, Scikit-learn, OpenCV, Docker, GCP, and AWS.

I relish coffee, sports, and music. When I leave the office, I enjoy watching football (supporting Flamengo🔴⚫ from Brazil), playing video games, and running.

Interests

Machine Learning & Deep Learning
Computer Vision
Security & Privacy
Distributed Systems

Education

PhD in Computer Science, ongoing
University of São Paulo -- ICMC/USP
MSc in Computer Science, 2021
University of São Paulo -- ICMC/USP
BSc in Computer Science, 2017
State University of Nortern Parana -- UENP

Skills

Python

Golang

Pytorch/Tensorflow

Scikit-learn

OpenCV

Git

Recent Publications

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Erikson J de Aguiar, Márcus VL Costa, Caetano Traina, Agma JM Traina (2023). Assessing Vulnerabilities of Deep Learning Explainability in Medical Image Analysis Under Adversarial Settings. CBMS'23.

Márcus VL Costa, Erikson J de Aguiar, Jonathan S Ramos, Lucas Santiago, Caetano Traina, Agma JM Traina (2023). A Deep Learning-based Radiomics Approach for COVID-19 Detection from CXR Images using Ensemble Learning Model. CBMS'23.

Erikson J de Aguiar, Caetano Traina, Agma JM Traina (2023). Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges. IMIA'23.

Jonathan S Ramos, Erikson J de Aguiar, Ivar V Belizario, Márcus VL Costa, Jamilly G Maciel, Mirela T Cazzolato, Caetano Traina, Marcello H Nogueira-Barbosa, Agma JM Traina (2022). Analysis of vertebrae without fracture on spine MRI to assess bone fragility: A Comparison of Traditional Machine Learning and Deep Learning. CBMS'22.

PDF Cite DOI

Erikson J Aguiar, Alyson J Dos Santos, Rodolfo I Meneguette, Robson De Grade, Jó Ueyama (2022). A blockchain-based protocol for tracking user access to shared medical imaging. FGCS.

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Projects

Security and privacy in machine learning models to medical images against adversarial attacks

This project was financed by Sao Paulo Research Foundation (FAPESP) as Dotoracte Scholarship in Brazil (grant #21/08982-3) from Mach 01, 2022, to now.

A trust protocol with multiple levels of access – case study with blockchain in medical applications

This project was financed by Sao Paulo Research Foundation (FAPESP) as Master Scholarship in Brazil (grant #18/18187-3) from April 01, 2019, to October 31, 2020.