Machine Learning (ML)

ML Applications in Wireless Communication Networks

The project aims to detect user anomalies and inference patterns in wireless networks, targeting to improve the data rates of communication systems.

*The results of the tests were published at an international conference. The statistical method was adopted by a local startup company (GOHM) for vehicle localization and data.

All the slides:


ML Applications in Magnetic Domain Pattern Images

Benefit: An opportunity to create domains that have out-of-range parameters (you can’t execute in micromagnetic simulation). 

In order to extract parameters from and/or generate new images of magnetization patterns using micromagnetic simulation data, the images of simulated magnetization patterns will be input into Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for training.

*Worked on the latest technology - Heat Assisted Magnetic Recording (HAMR), the leading high-capacity drive technology. 


Advanced Reinforcement Learning Agent for Pong Using TensorFlow and Keras

Keras implementation of a blog post (Deep Reinforcement Learning: Pong from Pixels) that originally used Python's numpy library for neural network operations.

Training process.

During testing, the trained model was executed using pure Python in Jupyter Notebook.

Representation Learning using Multi-Layer Perceptrons (MLPs)

Here's a summary of the MLP results as compared to other methods:

* Results obtained after randomly flipping sensitive attributes to augment the data.