Machine Learning Techniques to Understand Genetic Cardiomyopathies

Tissue engineered cardiac tissue is a useful tool to understand genetic cardiomyopathies and the changes in contractile function mutations bring about. To better understand the differences in function between these 3D engineered heart tissues, this project aims to use tools in computer vision and signal processing to extract contractile function and calcium handling. We will adapt these techniques for the goal of classifying subtypes of cardiac diseases and drugs that modulate cardiac function. The student will be involved in all steps of the machine learning/ tissue engineering pipeline: create 3D cardiac tissues, generate imaging data, preprocess and label the data, and train/test various deep learning models to predict cardiac phenotypes.

Lab: Lab for Laboratory for Stem Cells and Tissue Engineering

Direct Supervisor: Sue Halligan

Position Dates: Summer 2022

Hours per Week: 20

Number of positions: 1

Qualifications: Student must have basic knowledge of cell culture, sterile technique and machine learning systems. 

Eligibility: Master's (SEAS only)

Sue Halligan, [email protected]