MIT EECS | Lincoln Laboratory Undergraduate Research and Innovation Scholar
Applications of Machine Learning for Cryo-Electron Microscopy (Cryo-EM)
- Computational Biology
Discovering the structure of large biomolecules to atomic resolution is critical for understanding biological processes and developing new medicines. One increasingly popular method of structure discovery is to directly image particles with an electron microscope (EM) and computationally reconstruct the macromolecule 3D structure from these 2D projections. However, this process is time-consuming and places a significant computational burden on biochemistry labs. For my SuperUROP project, I plan to use machine learning and other statistical techniques to replace the steps in the EM image-analysis pipeline that normally require expert human intervention. My goal is to streamline the reconstruction pipeline to make EM a more approachable technique for research groups.
In the past, I have enjoyed UROPs in molecular biology and coursework in computer science. I am excited to explore an intersection of these two fields through my SuperUROP project. This year, I look forward to diving into a fascinating research project and working with the wonderful people in the Drennan Lab.