Júnia Maísa Oliveira
Also known as: Oliveira, J. M., Júnia Maísa Oliveira
UNIBO, Fundação de Amparo à Pesquisa do Estado de São Paulo, University of Bologna, Universidade Federal de Minas Gerais, Fundação de Amparo à Pesquisa do Estado de Minas Gerais
About
Doctoral researcher in Electronics, Telecommunications, and Information Technologies Engineering at the University of Bologna. My research focuses on secure and efficient deep learning solutions for cyber-physical systems, with special attention to RISC-V-based platforms, Edge AI, and 5G/6G networks. I have contributed to international research projects such as JU-KDT ISOLDE (Europe), PORVIR (Brazil), FAPESP SFI2 (Brazil), and serve as reviewer for IEEE and SBC conferences in the field of network analytics and AI. My publication on Google scholar: https://scholar.google.com/citations?user=fqLlBosAAAAJ&hl=pt-BR
Employment
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UNIBO High Performance, Safe, Secure, Open-source Leveraged RISC-V Domain-Specific Ecosystems2024 - Present
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Fundação de Amparo à Pesquisa do Estado de São Paulo Technical training2023 - 2025
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Fundação de Amparo à Pesquisa do Estado de Minas Gerais Volunteer scientist2023 - 2025
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University of Udine Traineeship2023 - 2024
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Fundação de Amparo à Pesquisa do Estado de Minas Gerais Master degree scholarship2021 - 2023
Education
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University of Bologna Ph.D2024 - Present
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Universidade Federal de Minas Gerais Master2020 - 2024
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Federal University of São João del-Rei Bachelor2014 - 2021
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Federal University of São João del-Rei Bachelor2014 - 2019
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Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerais High school2008 - 2012
Projects & Funding
Projects & funding information is unavailable.
Publications (14)
- Improving Model-AI Performance with Correlation-based to Distance Matrices Save
- A Protocol-Based Framework for AIaaS Lifecycle Management in 6G via NWDAF Save
- Deep Neural Networks Study for Advanced Code-reuse Attacks Detection Save
- Tolerância a Falhas com Aprendizado por Reforço para Tomada de Decisão em Cenários Distribuídos Save
- A Comparative Analysis of Feature Selection and Machine Learning Algorithms for Enhanced Anomaly Detection in 5G Core Networks Save
- Anomaly Detection Employing a 5G Core Data Analytics Framework Save
- Um framework NWDAF para algoritmos de análise de dados de rede 5G e além Save
- Comparative Analysis of Unsupervised Machine Learning Algorithms for Anomaly Detection in Network Data Save
- Data Consumption and User Experience in Video Lecture Sessions via Mobile Telephony Network Save
- Improved Video QoE in Wireless Networks Using Deep Reinforcement Learning Save