January 2018 – My First Intersession.
Intersession is a three-week period before Spring Semester when you can take intriguing classes that aren’t offered during the regular Fall/Spring semesters!!
I decided to go all-in and take 3 credits (max) for my first Intersession:
- Introductory RNAseq Analysis with R (1 credit) – Caitlin Pozmanter
WHY? I took this class because I was unsure about taking a biology focussed class during Fall/Spring, and intersession seemed to be an amazing medium to take classes in disciplines that were of interest to me! The result? I absolutely loved it.
HOW? We analyze and interpret next-generation sequencing data using tools based in R, a statistical programming language. Our data comes from the Van Doren Lab and is based off of sex determination in Drosophila Melanogaster, the common fruit fly. We use gene abundance measurements from RNA-seq experiments to develop original, reproducible analyses as a capstone project!
WHAT? As a CS/AMS student, I wanted to contribute to healthcare and computational biology based efforts and this class proved to be a perfect choice. I learnt that computation tools can be used extensively to unravel biological mysteries and be used to contribute to ongoing research efforts in the sciences.
- Mapping the Brain: An Introduction to Connectomics (2) – William Gray Roncal
Photo credits: Gina El Nesr, JHU Class of 2021
WHY? This class is notoriously famous for being a lot more work than other intersession classes and as an ambitious Hopkins student, I wanted to take it.
HOW? This course introduces the novel field of Connectomics through really cool projects! For our first group project (Billions for Big Brains), we prepared a research grant proposal to identify differences between brain connections in the amygdala of autistic and non-autistic patients, under a $10 billion budget. Turns out spending money is actually really hard! Then, we implement features of machine learning to find connections in nanoscale imaging volumes of brain tissue!
WHAT? I learnt about approaches to extract graphs from large image volumes and the importance of computer science in addressing modern neuroscience and big-data challenges. This class also introduced me to the art of reading research papers!