This year's TEDMED was held Nov. 18-20 in Palm Springs, Calif. Stanford Medicine is a medical research institution partner of TEDMED, and a group of MD and PhD students who represented Stanford at the conference will be sharing their experiences here.
A tall, striking woman walked out onto the TEDMED stage. Energetically and confidently, Vivienne Ming, PhD, a theoretical neuroscientist and entrepreneur, went on to tell us about her passion for optimizing human potential utilizing big data.
There are many examples where Ming has used data for social good, such as matching refugees to their families with image-recognition software, but here at TEDMED she discussed personalized education. By looking for trends and patterns in classroom data, she has built an educational tool that predicts a child’s grade trajectory in the class and then chooses the highest impact personalized intervention for each individual student. Using data-science techniques to study society and education, Ming dazzles us with the possibilities of data in improving our capacity to reach our individual potentials.
Ming completely upended my view on data science in social issues
As a graduate student applying these same techniques to the field of genomics, I was intrigued. My understanding of data science applied to the social sciences, such as in the field of economics, was that the modeling remained fairly straightforward and simple: Understandable models were very important for the social sciences, and complex data-science models were not very interpretable. As a medical student, I found this frustrating – my interests lay both in cutting-edge computational advances as well as empathy for human suffering, and I couldn’t figure out how to apply my data-science skills beyond the world of science to answer questions of social and health inequalities.
Ming completely upended my view on data science in social issues. I was fortunate enough to have the chance to chat with her the next day, and I asked her about the computational work, as I was curious how complex her models really could be. She displayed a wonderful technical capacity and a deep understanding of how to choose the right algorithms for the social problems at hand. Granted, it's still not common to find such imaginative interdisciplinary work combining cutting-edge computer science work and social science work. But Ming showed me a world of possibility around bringing data skills into improving everything from hiring to education to gender equality. I came away impressed, inspired, and excited about the possibilities of utilizing my own skills in the world beyond genomics.
Working at the intersection of two fields can be extremely challenging to impossible. And it's particularly tricky to apply the latest data-science methods to societal questions. As such, to be able to intelligently and thoughtfully do the two together is an art – and Ming is a master of the important intersection where computation meets humanity.
Daniel Kim is a fifth-year MD/PhD student at Stanford. He studies biomedical informatics and genomics and is interested in all things data-related.
