Luiza Pozzobon

Luiza Pozzobon has a diverse work experience in various roles and companies. Luiza started their career as a Projects Analyst at Base Jr in 2014. Luiza later became a Projects Manager and then a Quality Analyst at the same company. In 2017, they worked as an Undergraduate Research Fellow at Qiron Robotics. Luiza continued their research work at Qiron Robotics in 2019, where they were a Software Engineer and later an Undergraduate Research Fellow in Deep Learning. In 2020, they joined idwall as a Data Scientist, where they developed machine learning algorithms for identification documents OCR. In 2021, they joined Kunumi as a Machine Learning Engineer and worked on developing machine learning solutions, data pipeline orchestration, and deployment of machine learning models. At Kunumi, they also served as a Tech Lead, providing support and guidance to the data science team and managing project planning. Currently, they are working at Cohere as a Research Scholar.

Luiza Pozzobon has a diverse education history. Luiza obtained a Master's Degree in Computer Engineering from the Universidade Estadual de Campinas, which they pursued from 2022 to 2024. Prior to that, from 2015 to 2020, Luiza completed their Control and Automation Engineering Bachelor's degree at the Universidade Federal de Santa Maria. Before focusing on automation, they obtained a Civil Engineering Bachelor's degree from the same university between 2013 and 2015.

In addition to their formal education, Luiza has also acquired several certifications. In 2022, they obtained the "AI for Good - Damage Inspection Challenge" certification from FruitPunch AI. In 2021, Luiza earned certifications in various fields, including "Generative Adversarial Networks (GANs)" from DeepLearning.AI, "Kanban System Design" from Kanban University, and "Cultura Ágil" from Management 3.0. Prior to that, in 2020, they completed the "Machine Learning Engineer Nanodegree" from Udacity. Luiza's earliest certification, earned in 2019, is the "Deep Learning Specialization" from DeepLearning.AI.

It should be noted that no assumptions have been made beyond the information provided.