Yuveshen Mooroogen
Why did you decide to pursue a graduate degree?
Simply put: I am happiest when I am learning and teaching, and being a graduate student means that I get to spend my time doing both of those things.
Why did you decide to study at UBC?
During my undergraduate studies at the University of Toronto I was lucky to get to know several of my professors quite well. They knew what kind of work I was most interested in doing and strongly recommended the research group at UBC.
What is it specifically, that your program offers, that attracted you?
The harmonic analysis group in the mathematics department is excellent. I had heard of the work that the faculty were doing long before I knew who they were or where they were based.
What was the best surprise about UBC or life in Vancouver?
The best surprise about the program was definitely how cool my advisor turned out to be. I had heard about her, but never met her, before moving to Vancouver, and she is in so many ways the academic I aspire to be someday.
What aspects of your life or career before now have best prepared you for your UBC graduate program?
My undergraduate academic training at UofT was extremely challenging but during my graduate studies I've often felt grateful for how well it equipped me for what I'm working on today.
What do you like to do for fun or relaxation?
For the last couple of years I've been going to the gym regularly. I had a wonderful personal trainer at UBC Recreation who got me into it.
What advice do you have for new graduate students?
Get enough sleep! Chronic sleep deprivation is the devil: it tempts you with power, but it will steal your soul.
Learn more about Yuveshen's research
In recent years, signal processing has seen transformative advancements, making MRI scans, wireless communications, and satellite imaging faster, sharper, and more cost-effective. Many of these improvements were driven by “compressive sensing,” a technique that reconstructs high-quality signals using fewer samples than traditional methods require. The mathematical underpinning of compressive sensing is the “uncertainty principle,” a powerful tool in harmonic analysis. This principle shows that if a signal contains repeating patterns, it can be accurately reconstructed using fewer measurements than scientists previously believed. Such signals are said to have sparse geometry. In my research, I am developing new ways to exploit the sparse geometry that powers uncertainty principles, thereby extending its use to broader contexts.