Affiliation:
Assistant Professor, Department of Statistics, Indiana University
Educational Background:
PhD, Department of Biostatistics, Johns Hopkins School of Public Health
MS, Industrial Engineering, Department of Industrial Engineering and Operations Research, Georgia Institute of Technology
BS, Industrial Engineering, Department of Industrial Engineering and Operations Research, Georgia Institute of Technology
About Amanda
I am an assistant professor in the department of statistics and a member of the Program in Neuroscience at Indiana University. My research focuses on developing statistical methods for neuroimaging data. My path here was not a direct one, and the destination was not initially clear, but each step was vital to forming my identity as a researcher and academic.
In college at Georgia Tech, I intended to major in math but quickly decided on industrial engineering for its focus on solving practical problems. I gained experience doing so through internships at Walt Disney World and Shaw Floors, which gave me an appreciation for the complexity of real-world data sets and the importance of “getting your hands dirty” to make a real impact. Several months into my first full-time job, however, I realized I wanted to continue learning more advanced analytical techniques and pursue work with a positive societal impact. These two factors led me to return to graduate school.
After earning an MS in industrial engineering at Georgia Tech, I realized I wanted to be part of a research community focused on analyzing health-related data and I needed a stronger foundation in statistics. This inspired me to pursue a PhD in biostatistics at the Johns Hopkins School of Public Health. It was there that I developed a passion for statistics as a tool to advance scientific discovery. As a professor today, I strive to emphasize the “why” of statistics before the “how” and to bring research problems into the classroom. Based on my own experience discovering statistics, I believe this is vital to attract and empower future generations of statisticians.
In considering research topics as a PhD student, I found myself attracted to brain imaging—unsurprising given my engineering background and interest in data-driven approaches. Since then, I have striven to be “plugged in” to the neuroimaging community, attending nearly as many imaging conferences as statistics ones, serving on the editorial board of NeuroImage (a top neuroimaging methods journal), and establishing deep collaborative partnerships with imaging scientists.
In retrospect, coming from a small town in Louisiana and having limited financial resources at my disposal, I realize my educational opportunities could have been constrained. However, several programs made pursuing an academic path possible. First, I attended the Louisiana School for Math, Science, and the Arts—a public residential high school where I was able to take advanced math courses. Second, Georgia Tech provided me with a full funding package, cobbled together from a number of need- and merit-based scholarships provided by small foundations and government programs. Finally, in graduate school, I received an NSF Graduate Research Fellowship, which paid for my first three years of graduate studies and provided a generous stipend.
Without each of these support mechanisms, I might not be where I am today. I am grateful for these footholds and believe it is vital that we continue to provide and expand educational opportunities for all students, regardless of economic status, geography, or family background.
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