2. Analyzing Menstrual Cycle Data and Math Transcending Boundaries with Emma Pierson

2. Analyzing Menstrual Cycle Data and Math Transcending Boundaries with Emma Pierson

Author: Katherine A. Keith, Naitian Zhou, & Lucy Li June 12, 2021 Duration: 44:10

We talk with Emma Pierson, PhD in Computer Science from Stanford and incoming assistant professor of Computer Science at Cornell Tech, about her paper "Daily, weekly, seasonal and menstrual cycles in women’s mood, behaviour and vital signs" published in Nature Human Behavior, 2021. This was joint work with fellow computer scientists (Tim Althoff and Jure Leskovec), head of data science at a partner company (Daniel Thomas), and professor of obstetrics and gynecology (Paula Hillard).

Emma shared with us strategies for normalizing research on women's health and the menstrual cycle and creating trust with industry partners. She emphasized that math is a universal language that can transcend the boundaries of individuals' personal experiences.

Paper link: https://www.nature.com/articles/s41562-020-01046-9


Behind every published paper or headline-grabbing finding using social data, there's a hidden story of collaboration, dead ends, and problem-solving. Diaries of Social Data Research pulls back the curtain on that process. Hosted by researchers Katherine A. Keith, Naitian Zhou, and Lucy Li, this series sits down with scholars working at the intersection of computational methods and social science to explore the real, human effort behind the datasets. Each conversation functions like an open research diary, detailing how interdisciplinary teams actually come together, navigate differing academic cultures, and tackle the practical hurdles of working with massive, often messy, information about human behavior. You'll hear about the stalled projects, the unexpected breakthroughs, and the meticulous work that turns a raw idea into a credible contribution. This isn't a podcast about polished results, but about the fascinating and often untold journey of modern research. For anyone curious about how we actually study society through data-the alliances built, the ethics debated, and the code debugged late into the night-this series offers a rare and authentic look inside the lab.
Author: Language: English Episodes: 20

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