Published May 03, 2022 by Rebecca Goldfine

John Hood ’22 Receives National Science Foundation Fellowship for Graduate Study

The NSF has awarded senior John Hood with a Graduate Research Fellowship to pursue a PhD in statistics at the University of Chicago.
John Hood
John Hood, a statistics and economics major, in Hubbard Hall.

The NSF Graduate Research Fellowship Program "recognizes and supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines," according to its website. The program provides fellows with three years of financial support, including an annual $34,000 stipend. The University of Chicago is also offering Hood a stipend.

In most years, a few Bowdoin alumni receive the NSF graduate fellowship, but it's uncommon for a Bowdoin student to receive the award while still an undergraduate.

Hood, who grew up in Charlotte, North Carolina, was encouraged to apply for the fellowship last summer when he participated in an undergraduate directed research program in math and statistics at North Carolina State University. He worked with a small team on a project to improve prediction models for extreme weather events.

University of Chicago's doctoral program in statistics appealed to Hood because of its emphasis on machine learning. "I'm interested in the application of new technology," he said, using it to address issues like climate change or financial forecasting. 

The university also has a strong record of helping its graduates find jobs in academia, technology, and finance—but Hood is not yet sure in which direction he'll head. "My interests are so general right now," he said, although he'd like to strike a balance between theoretical statistics and real-world applications. 

His senior-year honors project reflects his interest in applying math to real-world situations. With his advisor Dan Stone, associate professor of economics, Hood wrote a thesis titled "Analyzing managerial reactions to fluctuating player performance in Major League Baseball." 

John Hood's Top Three Classes

Advanced Analysis, with Associate Professor of Mathematics Thomas Pietraho

The class, which has applications to probability and mathematical finance, covers topics such as the Lebesgue measure and integral, measurable functions and random variables, convergence theorems, random walks and Brownian motion, and the Ito integral. 

Hood: "That was almost certainly the hardest math class I've taken at Bowdoin. It challenged me in ways I had never been challenged, particularly with my organizational skills, and also the material was extremely difficult. Ultimately, though, it had really cool applications to economics."

Microeconomics, with Professor of Economics Guillermo "Ta" Herrera

The intermediate class looks at the theory of resource allocation and distribution, with an emphasis on systems of markets and prices social mechanisms for making resource allocation decisions. Topics include the theory of individual choice and demand, the theory of the firm, market equilibrium under competition and monopoly, general equilibrium theory, and welfare economics.

Hood: "It was great. It went at a great pace and had a good balance between rigorous math and proving theorems. And it had that econ side of being very applied. The TA was one of my closest friends and we spent hours talking about the material in that class, which was really fun."

Advanced Topics in Probability and Statistics, with Associate Professor of Mathematics Jack O'Brien

The class examines specialized topics in probability and statistics, including regression analysis, nonparametric statistics, logistic regression, and other linear and nonlinear approaches to modeling data. It emphasizes the mathematical derivation of statistical procedures and the application of statistical theory to real-life problems.

Hood: "The first half of the class was learning abstract material and solving proofs, and the second half of the class was all coding online. It was interesting to me to take these rigorous topics and implement them on real-world data in such beautiful ways."