My name is Qifan Yang, and I am an incoming third-year student with Statistics Major and Mathematical Applications in Finance and Economics Specialist at the University of Toronto. This past summer, I had the opportunity to work on an ROP299 research project with Professor Tyrrell, and I would like to share my four-month journey in research, a completely new experience for me.
When I started, I was a complete novice in medical imaging and unfamiliar with the full process of scientific research. Before our first meeting, I felt quite nervous. I still remember Professor Tyrrell, during the interview, warning me about the potential challenges ahead. Coming from a statistics and mathematics background, I initially found both machine learning concepts and medical terminology quite intimidating. Although I had completed a few Kaggle courses, I lacked hands-on experience with building models from raw datasets and running end-to-end training and testing.
My research journey began along two paths: first, learning the fundamentals of machine learning and medical imaging, where review papers became my best starting point, and second, exploring rheumatic heart disease (RHD) and its potential for automated diagnosis using transthoracic echocardiography (TTE). The first obstacle I encountered was the lack of publicly available, large-scale datasets for RHD with detailed labels. This led me to pivot toward studying image quality in TTE, since I found a large echocardiography database with quality labels. However, a second challenge soon emerged: I struggled to identify a research question that was both technically meaningful and scientifically impactful.
This is where Professor Tyrrell’s mentorship made all the difference. In one group meeting, he mentioned severe motion blur he had observed in knee ultrasound images. That sparked the idea for my project: detecting and correcting non-uniform motion blur in echocardiography using deep learning. This was the turning point when the project truly began to take shape.
The real research work involved splitting and labeling datasets, designing a neural network model, training and testing on GPUs, and visualizing and evaluating results. Each of these steps was entirely new to me, requiring both technical learning and persistent problem-solving. I am deeply grateful for the guidance of Professor Tyrrell, as well as the support from Giuseppe, Noushin, and other members of the lab, including previous students whose work provided valuable reference points.
By the end of the summer, I had taken full charge of the project, running it from start to end. This responsibility taught me far more than technical skills. I developed a stronger sense of self-motivation, learned to manage my time effectively, and built the resilience needed to handle research setbacks. I realized that research is not just about repetitive lab work; it is about thinking critically, asking meaningful questions, and telling a compelling story through data and results.
The experience was more than an introduction to the research world; it taught me to think boldly and work carefully. I learned not to let ideas live only in conversation or in my head, but to translate them into small, testable experiments that turn speculation into evidence. Each modest prototype, whether a quick data split, a minimal model, or a rough visualization, sharpened my questions, exposed constraints, and informed the next step. Gradually, those incremental wins compounded into a coherent pipeline and credible results. The discipline I gained is simple but powerful: think wild, start small, measure honestly, and move steadily. This balance of wild curiosity with careful craftsmanship now guides how I approach complex, unfamiliar problems, and it’s the mindset I’ll carry into future research and professional work.