Alex’s Single-Cell Genomics Deep Learning Project (Summer ’19)

Structure of this site

The content is organized under the articles tab in the top menu.

If you want to jump straight into my computational work, see the Deep Learning Workflow and Results page.

Otherwise, here is a list of the various pages and what they offer:

• Background: informal overviews of basic information, but enough to understand the broad strokes of my work.
• Deep Learning Workflow and Results: a formal write-up of my work from the summer, showing my data, workflow, and results. It is relatively involved and will be tricky to understand in full without significant prior knowledge.
• Deep Learning Benchmarking - Dropout: a semi-formal analysis of dropout as a feature in training neural networks. For most people, this section should contain a good balance of interesting science and reasonable readability.
• Personal Takeaways: a highly informal diary-style discussion of my personal takeaways from the summer. (contains unapologetic candor; reader discretion advised)

Thank you to my mentors

This project is a result of my internship with Raphael Gottardo’s Lab at the Fred Hutch. Huge thanks to Dr. Gottardo and the team for taking me on. Specifically, Rob Amezquita took me under his wing and mentored me. Thank you Rob for being so helpful and fun! Etienne Becht and Evan Greene get honorable mention for their help and general involvement with my work. This entire field of computational biology was completely new to me, and I feel blessed to have such intelligent and patient teachers to help me navigate my various challenges. With their help, I managed to learn a great deal, and even do some sorta-cool science!