I am beginning my third year as a PhD student at New York University, and under the guidance of SOT members Judith Zelikoff, PhD, and Terry Gordon, PhD, I am researching the occupational health outcomes of workers in commercial kitchens who are chronically exposed to cooking emissions. I am honored to have been selected for the SOT Diversity Initiatives Career Development Award.
Using these funds, I was able to attend a Skills for Health and Research Professionals (SHARP) training session offered by the Columbia University Mailman School of Public Health. This workshop impressively demonstrated modeling environmental mixtures with its intensive analytical sessions using data analysis methods that have become increasingly prominent in health studies. Over the course of two days, I learned techniques such as Bayesian kernel, tre-based methods, weighted quantile sum regression, and factor analysis taught by experts in environmental health, epidemiology, and statistics. This workshop utilized lectures to teach the underlying theory behind the statistical methods, as well as hands-on computer lab sessions to put these ideas into practice.
I am in no way a “math person”—in fact, I shudder at the mere thought of something more complex than C1V1 = C2V2. While my training was in chemistry, I had little desire to revisit the likes of physical chemistry, calculus, or statistical mechanics. I thought myself done with those courses and was eager to live a long, lucrative career as an environmental health scientist (right?). However, I know my decision to attend graduate school was, in part, guided by my aspiration to learn more about the world. I want to be a better health scientist and to use the best science to help inform the decisions for the best protection of environmental health. In a world where computational toxicology, AI, and statistical modeling are at the forefront of public health advancement, I know I need to grow in ways that require me to be uncomfortable.
Over the course of this incredible workshop, I was out of my comfort zone—but in the best, most exciting way possible. I had the privilege of learning from a barrage of environmental epidemiological experts, who each ensured I could take away the concepts and theory behind their methods. I also gained insight into using R code for selection and implementation of complex statistical modeling, and I met a wonderful cohort of people who, like me, were out of their comfort zones to learn what is new and exciting. I am grateful for this opportunity to be challenged and to learn more. The prospect of incorporating this new knowledge into my research is impelling; I aim to find ways to predict how complex mixture exposures might impact health outcomes in occupational cohorts. I hope that this might serve as a framework for predicting complex multipollutant exposures in the workplace.
The SOT Diversity Initiatives Career Development Award has allowed me to grow as a scientist, and I am forever grateful. I am eager to practice what I have learned and fail until I eventually succeed. Who knows, ask me in a year and maybe by then I will have become a “math person.”
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