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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

Project ideas are meant to help inspire student thinking about their own project. Students are in the driver seat of their research and are free to use any or none of the ideas shared by their mentors.

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.

Coding skills

Pytorch, Python, Tensorflow, Shell, Matlab

Languages I know

Mandarin (beginner), Spanish (beginner)

Teaching experience

In high school, I worked as a tutor for K-12 kids in all subjects. I have previously been a teaching assistant for three classes: circuit analysis I, circuit analysis II, and differential equations. I evaluated students' work and helped them with problem sets as well as Matlab exercises. Additionally, I have also mentored two undergraduate students as part of a women in STEM mentorship program. I also mentored an undergraduate last summer who was doing ML and MRI research in my lab.

Credentials

Work experience

Apple (2022 - Current)
Research Intern (PhD)
Systems and Technology Research (2018 - 2019)
Research Intern (PhD)
Google (2022 - 2022)
Research Intern (PhD)

Education

University of Massachusetts Amherst
BS Bachelor of Science (2017)
Electrical Engineering
Stanford University
MS Master of Science (2019)
Electrical Engineering
Stanford University
PhD Doctor of Philosophy candidate
Electrical Engineering

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