Machine Learning

Unsupervised Domain Adaptation (UDA) is a machine learning methid used to tackle the drop in performance due to a mismatch between training and testing conditions of models. Popular UDA techniques, inspired by Generative Adversarial Networks (GANs), include Domain Adversarial Neural Networks (DANNs) and Conditional Domain Adversarial Networks (CDANs) upon multiple other methods. GANs involve a game where two neural networks, the generator and the discriminator, compete against each other, with the generator creating synthetic data and the discriminator trying to distinguish between real and synthetic data, thereby improving their abilities through this adversarial process....

At BlablaConf, I had the privilege to give a talk all about MLflow, a handy tool that helps make machine learning projects run smoother. I broke down how it keeps track of different experiments you do, how it bundles up your models neatly, and how it makes deploying them a breeze. I stressed how important it is for teams to work well together in data science, and MLflow makes that happen by keeping everything organized and reproducible....

During my phd years, I made a demo of indoor human activity recognition using FMCW radar and machine learning. The schematic below shows the end to end system from the sensor node to the cloud. I was involved in the different stages of this work from the sensor configuration and the signal processing to building a convolutional neural network for Doppler maps classification. This schematic shows the workflow of the demo from the sensor edge to the cloud....