Digital Signal Processing Developer in Toronto, ON, Canada

Created this classifier to help the hospitals to triage the suspects of Covid-19 from a simple blood test! It is faster and spares many SARS-CoV-2 test kits. It can save lives and costs at the same time during the peak of the coronavirus pandemic.

What if we could speed up the diagnosis process? A novel classifier was trained with a dataset of X-ray images from children's chests. Now, it labels any x-ray image as either normal, bacterial pneumonia, or viral pneumonia. The result is an automated diagnosis that can accelerate the clinical care for children with world-class precision.

Trained generative adversarial networks (GAN) using a dataset of celebrities' photos to learn how to create a fake human face. This same technique can be used to generate any real content like images, texts, simulations, or signals.

Applied computer vision and digital signal processing techniques on the radar for classification. The novel model finds meaningful contrasts between weapons and decoys of a person in movement. CNN can be used to analyze images and any other signals such as video, audio, radar, and sonar.

What if artificial intelligence could kickstart an original text, so you do not have to start a new one from scratch? I created this website that the user enters a few keywords, and it gives back some related suggestions of text with titles. I used Heroku, RESTful, and asynchronous tasks to deploy this model online.

Created a recommendation engine for a cloud-service online retailer based on a collaborative filter. Any online business must leverage its database to find out and deploy a better user experience to drive consumption. Unsupervised learning is ideal to find customer segments, which can be used to predict common preferences.

The passengers on the world's busiest airways now know if their flight will delay with 15 minutes of margin. I created this supervised learning model considering a 2-year long hourly dataset of airport departures, arrivals, and weather.

The Parrot quadcopter just gained an autopilot. This new reinforcement learning knows how much power each rotor must apply in order to stabilize the drone thanks to thousands of simulated runs. Let me know if you need specific maneuvers or autopilot for any other vehicle.

Now, the owner of a bike-sharing company from Washington can make better decisions on how many bikes to buy. This forecast helps the owner to avoid losing revenue by having too few or skyrocketing the operational costs by having too many. I created this model by training an RNN over a 2-year hourly rental and weather data. RNNs are suitable to predict any time-series data such as demand, weather, and stock options.

A retailer from Portugal wanted to lower the frequency of deliveries to reduce cost without considerably disturbing its clients. It was achieved by finding its customer segments and selecting which would not be significantly impacted by three weekly deliveries instead of five. Unsupervised learning is ideal to find customer segments and understand their specific purchase behaviors.

The style transfer technique allows the convolutional neural network to learn independently the content from one picture and the style from another. Later, I gradually applied the style from the painting to the content of the picture with the octopus.

Designed, embedded, and tested in the field with telemetry during real launches algorithms for missiles and smart-bombs, including aided navigation (GPS, BaroAltimeter, and IMU), transfer alignment, data fusion, target position estimation, and Extended Kalman Filter.

Created a predictive model to read a movie review and classify it as POSITIVE or NEGATIVE. The model applies text processing techniques and a neural network. This technique is also reusable for other applications such as product reviews or stock market discussions.

Online advertisement opens room for custom experience based on the user's known preferences. This opportunity is explored by offering an automated way to change the image on website banners with the brand on-the-go based on which user is accessing the website.

This methodology is achievable by reusing a trained ResNet101 (Transfer Learning) for image segmentation, followed by some image processing to find the best spot on the background to place the logo.

For instance, if we know that a user is prone to buy Microsoft products then we can automatically add the Microsoft logo to this image:
https://i0.gmx.ch/image/710/34441710,pd=3,f=sdata43/instagram-hype-instagram-feed-hype-produkte-haartu.jpg

The result would be this one here:
https://i.ibb.co/Cwg6Cgc/brandify-microsoft.png