I’m a graduate student in applied mathematics at the Center of Applied Mathematics at Cornell University, and will be graduating, ostensibly, sometime early next year. I drink lots of coffee, used to run a pub trivia night, and enjoy being self-deprecating but don’t do it very well.
Things I do in my spare time (listed in the order in which I thought of them):
- Watch football
- Watch basketball
- Pet and tend to two cats
- Make lists
- Lately, plan a wedding (which also involves making lists).
About my Research
I work on problems in computational fluid dynamics and acoustics, which practically speaking means I spend a lot of time debugging code that I have broken. The applications I work on are mostly related to modeling the propagationg of oceanic internal waves, but I work on other, smaller, projects involving sound propagation through environmental flows like tornados as well. Besides the application, there are tons of fun computational problems, too. The codes we write run on thousands of processors for many months at a time, so we spend a lot of time designing efficient algorithms to solve linear systems quickly in parallel. When optimizing an algorithm can result in saving weeks or months of time, you find yourself extremely motivated to construct fast algorithms. Beyond the challenges of good algorithm designs, there are plenty of challenges in implementation. As a computational scientist I have to be aware of things like CPU architecture, network topology, data/memory management and file I/O, and other things that are really more computer engineering than computer science. And finally these simulations can be on grids with hundreds of millions (or billions!) of nodes, generating tens of terabytes of data that must be managed and processed (again efficiently and in parallel). Putting all this together, the field of computational physics really requires some expertise in physics, mathematics, computer science, and computer engineering. And lots of coffee.