Elizabeth C
- Research Program Mentor
PhD candidate at Stanford University
Expertise
Machine Learning, Artificial Intelligence, Deep Learning, MRI, Medical AI, Image Processing, Signal Processing, Healthcare, Programming
Bio
Hi! My name is Elizabeth. I received a B.S. in Electrical Engineering from UMass Amherst in 2017 and an M.S. in Electrical Engineering from Stanford in 2019. I am currently a PhD candidate in Electrical Engineering in the Magnetic Resonance Systems Research Laboratory. I work on applying machine learning to solve MRI problems with the main purpose of speeding up the scans. In my free time, I enjoy horseback riding, playing tennis, and weight lifting. I also love to travel, ice skate, read, and hike. Each week, I volunteer at a pet shelter. I'm excited to mentor you in a research project!Project ideas
Coil Compression using Neural Networks
In MRI, receiver arrays use many coil elements. Multiple coils can provide high signal-to-noise ratio. However, the growing number of coils results in large datasets and high computation time. Coil compression algorithms are effective in solving this problem by compressing the data into less virtual coils. In this project, you will train a neural network to learn coil compression. You will learn about MRI, as well as how machine learning models are built and trained.
Learn and Apply Tensorflow!
Tensorflow is a very common programming tool for machine learning. In this project, I will help you learn the basics of Tensorflow. Together, we will discover how it differs from other programming languages, and how we can apply it to train a machine learning model for image recognition.
Foundations of Machine Learning
In this project, you will be introduced to machine learning, starting from basic intuition and moving into mathematical theory. Then, you will apply what you've learned (such as linear regression) to model a relationship of your choice.