Rafael Toledo
Verified Expert in Engineering
Computer Vision Developer
Rafael has been working for almost ten years in artificial intelligence. Half of these years were focused on computer vision, in which he developed solutions for OCR, color detection, face manipulation, and semantic segmentation of countryside roads. Most of his work was developing real-world applications from scratch. In production, he has built solutions with PyTorch, TensorFlow, TensorFlow Serving, and Flask, using cloud resources from Azure and AWS.
Portfolio
Experience
Availability
Preferred Environment
Python, TensorFlow, PyTorch, Amazon Web Services (AWS), Computer Vision, Deep Learning, OpenCV, Git, Azure, Flask
The most amazing...
...project I've worked on was an autoML that trained, built, and deployed optimum models remotely and handled cloud resources by demand, achieving 98% accuracy.
Work Experience
Computer Vision Engineer
MIME
- Built and trained a custom neural network to predict skin tones and complexions from selfies, achieving more than 80% accuracy, a very high standard for color. It followed a server deployment that answered more than 2,000 predictions daily.
- Adapted a computer vision algorithm to estimate a scene illumination, using spherical harmonics radiance mapping and face 3D mesh, relighting the faces afterward.
- Built a face skin segmentation model that distinguished skin from face members for foundations virtual try-on. It was gradient-boosting based and achieved more than a 95% accuracy.
- Adopted cutting-edge deep learning algorithms like Variational AutoEncoder (VAE), Weak Supervised Learning, Physics-guided learning, and built custom architecture with multiple custom losses and outputs.
- Deployed multiple applications for production on AWS, using EC2, S3, Elastic Beanstalk, Elastic Inference, and TensorFlow Serving.
Data Scientist
Athena Analytics
- Invented multiple new grade features and developed an autoregressive model that predicted grades from more than 100,000 students with a 93% accuracy in many Irish schools.
- Created an autoML system that generated optimum new models as more samples were added. The optimization was based on the hyper-tuning bayesian search.
- Automated crontab service to feed predictions on the database as more data were added.
Data Scientist
Khomp
- Developed an AI service that distinguished human voices from machine/records voices with 98% accuracy. This service predicted more than 150,000 calls daily for tens of customers on production.
- Developed an autoML service for independent custom training of clients that delivered optimum models in less than one hour, requiring them only to send data to the cloud.
- Implemented an autoML service that instantiated resources only by demand on Azure. Everything (Blobs and Batch Computing) on automatically instantiated and automatically destroyed.
- Implemented a biometric voice system for more than 100 people from scratch using Gaussian mixture models, gradient boosted tree models, hypothesis tests, and a custom file database system.
- Trained models to recognize a speaker's emotion, age, and gender, using the Gaussian mixture and gradient boosted trees models.
- Implemented Continuous Integration and Continuous Deployment (CI/CD) configurations in five GitLab projects and the webhooks in the Docker registries.
Computer Vision Engineer | Data Scientist
Neoway
- Trained more than 200 CAPTCHA's recognition solutions, achieving performance superior to 80%. Used classical approaches with SVM and a deep learning approach with VGG19 and Caffe.
- Applied reverse engineering to formulate numbers from more than 50 Brazilian government registers of open sources to internet bots.
- Formulated hundreds of specific queries on PostgreSQL databases to answer customers' questions and analyses automatically.
- Conducted tens of statistical analyses of internal services to detect bottlenecks and failures and to guide the subsequent engineering decisions.
Experience
Face Reconstruction with Variational Autoencoder and Face Masks
https://github.com/tldrafael/FaceReconstructionWithVAEAndFaceMasksThis work investigated how face masks can help the training of VAEs (variational autoencoders) for face reconstruction by restricting the learning to the pixels selected by the face mask. An evaluation of the proposal using the CelebA dataset shows that the reconstructed images are enhanced with the face masks, especially when SSIM loss is used either with L1 or L2 loss functions.
Facial Attributes Manipulation with Variational Autoencoder
https://github.com/tldrafael/ManipulateFacialAttributesUsingVAEThe project enables to augment a small face database and make the same face available in many distinct expressions. This augmented data can help in the training of other algorithms.
It is based on the CelebA dataset that contains more than 40 facial attributes with labels.
Dashboard for Urban, Rural, and Indigenous Populations in Brazil
https://github.com/tldrafael/brazil_urban_rural_indigenous_censusThe application can be found at the link below:
• https://toledo-rafael.shinyapps.io/urban-rural-indigenous-census-BR/
Azure Services Presentation
https://github.com/tldrafael/AzurePresentationWithShinyAppThe presentation is presented at https://github.com/tldrafael/AzurePresentationWithShinyApp/blob/master/helpers/AzurePresentation.pdf
Implementing a Neural Net from Scratch
https://github.com/tldrafael/implementing-MLP-from-scratchThe project includes gradient computation, gradient-checking, and the backpropagation algorithm.
Skills
Languages
Python, R, Bash, Java
Frameworks
Flask, RStudio Shiny, Caffe
Libraries/APIs
XGBoost, TensorFlow, PyTorch, OpenCV, Tidyverse, Pandas, NumPy, Keras, REST APIs
Tools
Git, Jupyter, Vim Text Editor, GitLab CI/CD, Tmux
Paradigms
Data Science, Continuous Integration (CI), Functional Programming, Continuous Delivery (CD)
Platforms
Jupyter Notebook, Amazon Web Services (AWS), Docker, RStudio, Debian, Linux, AWS Elastic Beanstalk, Amazon EC2, Azure
Other
Gradient Boosted Trees, Artificial Intelligence (AI), Machine Learning, Deep Learning, Computer Vision, Computer Vision Algorithms, Image Processing, Convolutional Neural Networks (CNN), Variational Autoencoders, Data Analysis, Data Visualization, Autoregressive Integrated Moving Average (ARIMA), Speech Recognition, Blob Storage, Gradient Boosting, Biometrics, Emotion Recognition, Supervised Learning, Labeling, Decision Tree Regression, Engineering, SSH, Digital Signal Processing, Natural Language Processing (NLP), Generative Adversarial Networks (GANs), SVMs, Neural Networks, GPT, Generative Pre-trained Transformers (GPT)
Storage
PostgreSQL, Amazon S3 (AWS S3), Azure Blobs, PSQL
Education
Doctorate Degree in Computer Vision
Federal University of Santa Catarina - Florianópolis, Brazil
Master's Degree in Computer Vision
Santa Catarina Federal University - Florianópolis, Brazil
Bachelor's Degree in Mechatronics Engineering
Amazon State University - Amazonas, Brazil
Certifications
Generative Adversarial Networks (GANs) Specialization
Coursera
TensorFlow: Data and Deployment Specialization
Coursera
TensorFlow in Practice Specialization
Coursera
Deep Learning Specialization
Deeplearning.ai via Coursera
Developing Data Products
Coursera
Practical Machine Learning
Coursera
Machine Learning
Coursera
Regression Models
Coursera
Statistical Inference
Coursera
Image and Video Processing: From Mars to Hollywood With a Stop at the Hospital
Coursera
Reproducible Research
Coursera
Getting and Cleaning Data
Coursera Course Certificates
Exploratory Data Analysis
Coursera
The Data Scientist's Toolbox
Coursera
R Programming
Coursera
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