What initially made you interested in Data Science?

I really enjoy math, and I think that's where my interest in 'data science' started. While considering my future options, I realized that I would really love to be in a field that is grounded in and inspired by math, yet can be applied to problems that are familiar to me. I had really enjoyed my Algebra and Topology classes in my undergraduate, and during my senior year, I had stumbled upon this subfield of 'data science' called Topological Data Analysis that was partially pioneered by Gunnar Carlsson. I had hoped to learn more about this field, and hopefully, do research in it. It was my dumb luck that Brown accepted me. I learned about the research opportunities, as well as certain professors in the quantitative sciences who had taken an interest in data science. I felt the field would be a great way to keep learning and doing the math from excellent instructors, as well as to be able to see what the ever-so-hot field of machine learning was about. 

What are your plans following the program?

I'm actually working at a company called Data Plus Math on the data science team. The team is great and is very focused on developing rigorous models for the work that they do, as well as focus on the business needs to. In the future, I hope to be able to do something along the lines of developing mathematical/statistical methods with an application for data analysis. 

What are the most important skills you received or experiences you had during the program?

Aside from the coding experience which I had very little of before my time here, it would be the methods that I have learned to deal with data that doesn't come in a nice form. I think the most humbling experience, however, would be that statistics is its own rich field of math and that it's more than p-values and confidence intervals.

Is there a special experience or story you'd like to share from your time at the DSI?

I'm doing research with a professor at Brown in Topological Data Analysis, so that's fun! There's a lot to do; lots of paper reading and implementing certain ideas which I wouldn't have otherwise touched. Aside from research, I thoroughly enjoyed my experience learning from Stu Geman in DATA 1010. He truly knew a lot, and connected everything he taught, no matter how simple to interesting topics in science or mathematics that would make you want to ask him more questions. I remember going up to him one day after class, and asking him a simple question about a certain theorem he had just talked in Cross Validation (Stone). He went on to try to explain another result proven by another Stone about generators of stochastic processes. It had nothing to do with the course material, but it was an example of how he really made you think outside the box of the 'data science' methods.