A highly skilled professional in Electronic Engineering with a strong focus on Computer Vision and Artificial Intelligence, possessing robust qualifications through a Bachelor's and ongoing Master's degree in Electrical Engineering. Demonstrated expertise in image processing, object detection, anomaly classification, and semantic segmentation utilizing advanced frameworks such as PyTorch, TensorFlow, and OpenCV. Current research as an AI & Computer Vision Researcher involves developing software to inspect visual defects in notebooks using sophisticated techniques aligned with Agile methodology. Proficient in handling various communication protocols and embedded systems, combined with practical experience in natural language processing and thermographic imaging solutions. A commitment to innovation is evidenced by participation in international educational programs and ongoing professional development in cutting-edge technologies.

With a robust academic background and extensive post-doctoral experience at the intersection of biology and technology, this candidate currently contributes to innovative projects at Tulane University, USA, utilizing neural networks to enhance the world's largest fish image database for AI applications. Previous tenure at EMBL-EBI in the UK included the development of Python applications that revolutionized biological signal interpretation through machine learning and computer vision for analyzing cardiac rhythms and caudal movements. Experience at the National Institute for Amazonian Research in Brazil established a foundational expertise in scientific research, focusing on advanced technologies for analyzing captive animal behavior. Proficient in technologies such as Python, OpenCV, Scikit-learn, and TensorFlow, combined with a strong command of algorithms and data structures, positions this candidate to adeptly tackle complex challenges in the fields of data science and biology.

Possessing a Bachelor's and a Master's degree in Physics from a state university, this candidate demonstrates expertise in applying mathematical tools, logical reasoning, and scientific methods to practical problem-solving across various domains that utilize modeling and data analysis. The individual showcases substantial proficiency in programming as applied to fields such as Analytics, Machine Learning, and finite element simulations, as well as in control and automation systems. With robust experience in developing and implementing AI, Computer Vision, and advanced machine learning solutions, this candidate has led multidisciplinary teams and adopted MLOps practices in the sector. Furthermore, they are well-versed in documenting projects effectively and delivering impactful oral presentations in both Portuguese and English, making them particularly suited for interdisciplinary collaborations.

This candidate possesses a robust academic foundation in Computer Science, complemented by a Master's degree and ongoing doctoral studies in the same field. With extensive experience in Data Science projects concentrated on Machine Learning applications, they have demonstrated proficiency in a wide array of tools including pandas, numpy, scipy, matplotlib, seaborn, sklearn, pytorch, and various AutoML frameworks such as H2O and TPOT. Current roles include leading AI initiatives focused on vulnerability analysis in information systems and conducting research on imbalanced regression problems. Previous engagements encompass collaborative research on time series and machine learning for renewable energy generation forecasting, as well as academic positions teaching critical computer science subjects, reflecting a strong blend of practical and theoretical expertise.

A highly skilled data scientist and computer engineer with a Bachelor’s degree in Computer Engineering from Universidade de Araraquara, a Master’s in Computer Science and Computational Mathematics from the Instituto de Ciências Matemáticas e de Computação at USP, and a recent MBA in Data Science from USP/Esalq. Currently a doctoral candidate, engaged in advanced research focusing on machine learning applications for data quality assessment. Proven ability to apply theoretical knowledge to practical challenges, illustrated by substantial experience at Ford, where innovative facial recognition systems were developed utilizing advanced programming skills in Python and machine learning frameworks. Demonstrates strong analytical capabilities, collaborative spirit, and effective communication skills while mentoring MBA students.

Facebook BYOL (Bootstrap Your Own Latent) is a self-supervised learning approach for training neural networks without labeled data. It leverages a dual-network architecture to bootstrap representations, enabling the model to learn useful features from unlabeled data by predicting the output of one network using another.

A Facebook BYOL expert must have skills in deep learning, self-supervised learning, neural network architectures, and Python programming. Proficiency in frameworks like PyTorch or TensorFlow is essential, along with experience in handling large-scale datasets and fine-tuning hyperparameters for model optimization.

How to hire a Facebook BYOL (Bootstrap Your Own Latent) expert

Facebook BYOL (Bootstrap Your Own Latent) was introduced in 2020 by researchers at Facebook AI Research (FAIR). It was developed to advance self-supervised learning techniques, enabling models to learn from unlabeled data without requiring extensive labeled datasets.

About Facebook BYOL (Bootstrap Your Own Latent)

Strengths of Facebook BYOL included its ability to learn from unlabeled data and its effectiveness in various computer vision tasks. Weaknesses involved potential inefficiency in computational resources and challenges in tuning hyperparameters. Competitors included other self-supervised learning methods like 

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