We're excited to partner with ACM NY (www.meetup.com/ACM-NY) for a talk opening up the black box of the on-demand economy by Ming Yin, Postdoctoral Researcher at Microsoft Research.
Thank you to NYC Data Science Academy (www.nycdatascience.com) for hosting us!
Peeking into the On-Demand Economy:
Today, an increasing number of digital & mobile technologies have emerged to match customers, in almost real time, with a potentially global pool of self-employed labor, leading to the rise of the on-demand economy, which has brought about dramatic changes in our society. It creates new business models & new dynamics of labor allocation. It enables new models of computation, that is, human-in-the-loop computing. And it leads to new forms of knowledge creationpeople all over the world are contributing to scientific studies in dozens of fields, either by making scientific observations as amateur scientists or by participating in online experiments as subjects. Despite its already significant impacts, the on-demand economy has still been considered as a black-box approach to soliciting labor from a crowd of on-demand workers. Little is known about these workers & their aggregated behavior. In this talk, using the on-demand crowdsourcing platforms as an example, Ming present her attempts & findings on opening up this black box with a combination of experimental & computational approaches, with focuses on understanding who the on-demand workers are, how to model their unique working behavior, & how to improve their work experience.
Ming Yin, Postdoctoral Researcher at Microsoft Research:
Ming Yin is currently a postdoctoral researcher at Microsoft Research New York City. Starting in Fall 2018, she will join Purdue University as an Assistant Professor in the Department of Computer Science. Mings primary research interests lie in the interdisciplinary area of social computing & crowdsourcing. Her research has contributed to better understanding human behavior in social computing & crowdsourcing systems through large-scale online behavior experiments, as well as incorporating the empirical insights from the behavioral data into developing models, algorithms, & interfaces to facilitate the design towards better systems. More broadly, her research connects to the fields of applied artificial intelligence & machine learning, computational social science, human-computer interaction & behavioral economics. Mings work is published in top venues like WWW, CHI, AAAI & IJCAI. Ming is named as a Siebel Scholar (Class of 2017), & she has received Best Paper Honorable Mention at the ACM Conference on Human Factors in Computing Systems (CHI16). Ming obtained her bachelor's degree from Tsinghua University, Beijing, China, in 2011, & completed her PhD at Harvard University in 2017.
