My name is Brenna Lee and I’m about to start my senior year in biomedical engineering at Boston University. I have been very involved at BU, as I have worked as a tour guide, tutor, and Learning Assistant, and I’m currently the president of BU’s Irish dance team. This summer I finally got involved in research at BU in the Chand Lab in the Department of Anatomy & Neurobiology and the Department of Psychological and Brain Sciences.
My summer project involved writing code to classify and assess behavior using experimentally obtained videos of animals from the lab performing tasks. I used these videos in DeepLabCut, the Python toolbox from the Mathis Lab at Harvard, which uses deep learning to create labeled videos to track the position of certain animal body parts in the videos. I was then able to analyze the data and make calculations by writing Python code in a Jupyter Notebook.
To start my project, I first had to install DeepLabCut, which I struggled with at first. I only knew how to code in Matlab and I had no experience yet with Linux, Python, and all the software involved in using DeepLabCut. However, I was able to resolve the issues simply by verifying I had the correct corresponding versions of each software installed. I was then able to figure out how to use DeepLabCut most efficiently by practicing on stock videos of animals before beginning my project. When I first started working on the videos from the lab, I had a few challenges with the analysis because coding in Python was still new to me. There were some errors with indexing, but this was easy to fix. I would also come across issues with keeping the code generalized. I had to make sure the code could work on all videos I ran the code on, rather than just on the one I was testing. However, I liked working through these issues because I got to try different approaches and determine which is best and most efficient.
Being able to work with Chand this summer allowed me to learn a lot, and I’ve gained so much experience that will definitely be beneficial to me for when I start looking into graduate school. I learned how to code in a new programming language and work in an operating system I hadn’t used before. I also learned what deep learning is and about how it works. I feel much more confident in my programming abilities and problem solving skills, and I look forward to returning to the lab in the fall.