My junior year led me to reconsider my academic interests and adopt a more interdisciplinary academic plan. I am still studying computer science and statistics, and I have greatly enjoyed the contrast between these two fields and their approaches to problem solving. Following my sophomore year, I had the privilege to participate in the Undergraduate Program in Neural Computation at the Center for the Neural Basis of Cognition, a joint cognitive science institute between Carnegie Mellon University and the University of Pittsburgh. I worked with another student under Professor Robert Kass, a joint professor of statistics and machine learning. During this work, I analyzed neural data recorded from mouse brains using dense arrays of probes through the visual cortex and related areas of the thalamus. I discovered some interesting dynamics exhibited by these populations of neurons in response to various types of visual stimuli, and I learned quite a bit about statistical properties of machine learning techniques in the process. Before the summer of 2021, I intended to pursue a PhD in some deep learning subfield with an emphasis on creating trustworthy deep neural networks. After a closer experience with neuroscience, I have the same general interest, but I would like to approach the problem from a different angle: instead of developing analytical and machine learning techniques to characterize the vulnerability of deep neural networks to malicious inputs, I am now motivated to seek inspiration for new neural network architectures by studying biological brains and understanding their dynamics better. Other students in this summer program informed me of faculty who do exactly this, and I currently intend to pursue a PhD under the supervision of such a person.