Lucie Yang’s STA299 Journey

Hello! My name is Lucie Yang, and I am excited to share my experience with my ROP project this summer! I’m heading into my second year, pursuing a Data Science specialist. While I have been interested in statistics for a long time, I was not sure exactly what field to pursue. Over the past year, I became fascinated with machine learning and decided to apply to Prof. Tyrrell’s posting, despite being in my first year and not having any previous experience with machine learning or medical imaging. To my surprise, I was accepted and thus began my difficult, yet incredibly rewarding journey at the lab.

I remember Prof. Tyrrell had warned me during my interview that the research process would be challenging for me, but still, I was excited and confident that I could succeed. The first obstacle I encountered was choosing a research project. Despite spending hours scrolling through lessons on Coursera and YouTube and reading relevant papers to build my understanding, I struggled to come up with a topic that was feasible, novel, and interesting. I would go to the weekly ROP meetings thinking I had come up with a brilliant idea, only to realize that there was some problem that I had not even considered. After finally settling on an adequate project, I was met with another major obstacle: actually implementing it.

My project was about accelerating the assessment of heterogeneity on an X-Ray dataset with Fourier-transformed features. Past work done in the lab had shown that cluster analysis of features extracted from CNN models could indicate dataset heterogeneity, therefore, I wanted to explore whether the same would hold for Fourier-transformed features and whether it would be faster to use them. With the help of a previous student’s code, implementing the CNN pipeline was relatively straightforward; however, I struggled to understand how to apply the Fast Fourier Transform to images and extract the features. As deadlines loomed near and time was quickly ticking away, I was unsure of whether my code was even correct and became very frustrated. Prof. Tyrrell and Mauro gave me immense help, helping me refine my methodology and answering my many questions. After that, I was able to get back on track and thankfully, completed the rest of my project in time.

I learned a lot from this journey, far more than I have in any class I’ve taken, from the exciting state-of-the-art technologies being developed to the process of conducting research and writing code for machine learning. Above all, I gained a deeper appreciation of the bumpy road of research, and I am incredibly grateful to have had the opportunity to get a taste of it. I am very thankful to all the helpful lab members, and I look forward to continuing my journey in data science and research in the coming years!

Lucie Yang