If you don’t mind, start by telling us a little about yourself.

I’m from Goochland, Virginia, a small town 30 minutes outside of Richmond, Virginia. I actually grew up a Virginia Tech sports fan but didn’t really consider Virginia Tech until the last possible second. I had just decided I wanted to attend college at the end of my junior year of high school, and only ended up at Virginia Tech because of a schedule complication that landed me in AP Statistics during my senior year. Long story short: I began to love Statistics, and while I applied to other schools as a potential Political Science major, I decided to apply to Virginia Tech for Statistics. I can’t pinpoint what it was that made me choose Virginia Tech, but everything just “felt right” about the school when I visited.

I’ve loved my time here at Virginia Tech, and I’m currently the president of the Statistics Club, as well as working as an instructional assistant at the Math Emporium.

Do you have a major other than statistics?

I have a second major in Computational Modeling and Data Analytics (CMDA), and two minors: one in mathematics and one in computer science. 

What was it that brought to you to the statistics department?

Since I applied as a statistics major, I’ll explain what’s kept me in the statistics department. The faculty and department as a whole have been nothing short of amazing since I’ve been here. I’ve been able to establish real, meaningful relationships with some faculty members, as well as my peers. The department is growing, but still rather small, which makes it easy to feel like a member of the department and not just a row in the grade book. 

What has surprised you about the statistics program?

The flexibility of the major. While you’ll definitely learn the fundamentals and the advanced stuff as a statistics student, the program really encourages the students to apply their skills to a specific field. A lot of my peers want to pursue econometrics, others want to be actuaries, and I personally want to pursue genomics and precision medicine. The statistics program at Virginia Tech has been great about offering elective courses and access to faculty that make it a little easier for students to apply their statistical skills to something they’re passionate about.

Could you describe an experience you had in the program that stands out?

Something that really sticks with me is my first time meeting our department head, Dr. Fricker, and some of the graduate students in the department during my sophomore year. It was really the first time I’d had the chance to talk to Dr. Fricker and express my interests and experiences, and we’ve had a pretty nice relationship since then.

Is there an experience you have had in Statistics that has shaped the way you approach problem-solving?

Everything about being a statistics student has shaped the way I approach problem-solving. I’ve learned to be more skeptical, detail-oriented, and to really understand the context of problems before diving in and trying to implement solutions. 

Have you had the chance to intern anywhere?

Yes. The summer after my sophomore year, I did undergraduate research at the University of Washington - Seattle. I worked under Dr. James Bruce in the Department of Genome Sciences. That summer I wrote software that enabled the lab to study protein function and interaction more in-depth. Though I knew nothing about genetics and proteins going into the summer, this experience really spurred my interest in personalized medicine and has inspired me to apply to doctoral programs in genetics, computational biology, or biostatistics.

This past summer I interned at Nielsen. The experience here was very different from my time doing academic research, but I learned a great deal about data science in industry. The people at Nielsen were also very nice and easy to work with.

Was there a skill (or a prospective/approach to problem solving) you learned in statistics that you found particularly useful to the work you did in your internship?

Not so much at Nielsen. I spent my time there more or less developing data cleaning rules and packaging the code in a reusable package. I did have the chance to create a time-saving metric for validating my results using some sampling techniques I learned in Experimental Design, which was really cool.

Has your knowledge of statistics deepened your understanding of computational modeling and data analytics (CMDA)?

Yes, very much so. My knowledge of statistics has really deepened my understanding of the material in CMDA. CMDA provides a really powerful skillset to have in industry, but having a stats background has really helped me reason why and how things in CMDA work. As somebody who wants to conduct research, knowing why and how things work is something I really value. The theoretical statistics courses really gave me a boost when trying to reason out how the machine learning algorithms I learn in CMDA work.

What career path do you hope to follow?

As of right now, I want to pursue a career in genomics. More specifically, I’d like to work in precision medicine applying things like deep learning to genetic data in cancer research.

Barring any and all obstacles, what impact do you ultimately hope to have on the world?

My ultimate goal is to make the treatment of diseases like cancer and Alzheimer’s more effective. I want to be a key piece to the puzzle that makes currently incurable diseases, curable. 

Highlight of your Hokie career so far?

Oh, that’s a good one. Other than VT beating UVa in basketball at Cassell during my sophomore year, I’d have to say the highlight has been having dinner with Dean Sally Morton and Dr. Robertson Evia at the Inn. That was my first time meeting the Dean and Dr. Robertson Evia has been a great professor and friend since I enrolled at Virginia Tech, so that night was really special.

Lastly, what advice would you have for students considering a degree in statistics?

Apply your knowledge of statistics to what you love. We statistics majors are in a fortunate position where we’re in demand more than ever. Every company can benefit from statistical modeling and analysis.