I have used multiple variations of Principal Component Analysis (PCA) in my research on microbial community analysis. To explain the core theory and assumptions of PCA to my lab group, I fleshed out an analysis of a toy example (eigenfaces) that I had originally seen in class. This analysis reasons about the assumptions of PCA and the effects of applying it to out-of-distribution data by using PCA to perform facial recognition on a dataset of my classmates’ faces.

I was invited to present this project at Łódź University’s SP2021 [virtual] MathUp conference at Łódź University, Poland. The conference was an incredibly fun opportunity to meet fellow researchers and data scientists!

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A group of research students had tried to use a 2D discrete Fourier Transform to characterize the pattern of repeating protein units on a bacteria surface layer image. Since their spectral graph looked unusually messy and their pattern estimates seemed very wrong, my research professor asked me to take a crack at interpreting and cleaning it. This was a neat application of understanding the mathematical assumptions of a technique to properly isolate and interpret results.

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