Cornell University
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Ming Yin, Wednesday, February 22, 2017

Wednesday, February 22, 2017 - 4:00pm
Gates Hall 114

The Information Science Colloquium speaker for Wednesday, February 22, 2017, will be Ming Yin, a computer science PhD at Harvard and a member of the EconCS group. Her primary research interests lie in the interdisciplinary area of social computing and crowdsourcing. Her research has contributed to better understanding human behavior in social computing and crowdsourcing systems through large-scale online behavior experiments, as well as incorporating the empirical insights from the behavioral data into developing models, algorithms, and interfaces to facilitate the design towards better systems. Ming is named as a Siebel Scholar (Class of 2017), and she has received Best Paper Honorable Mention at the ACM Conference on Human Factors in Computing Systems (CHI’16). Before Harvard, Ming obtained a bachelor degree from Tsinghua University, Beijing, China.

Title: Peeking into the On-Demand Economy

Abstract: Today, an increasing number of digital platforms have emerged to match customers, almost in real time, with a potentially global pool of freelancers, leading to the rise of the on-demand economy. In addition to creating new dynamics of labor allocation, the on-demand economy has also led to new models of computation— it has enabled the human-in-the-loop computing— and new forms of knowledge creation—people 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 and their aggregated behavior. In this talk, using the crowdsourcing Internet marketplaces as an example, I present my attempts and findings on opening up this black box with a combination of experimental and computational approaches, with focuses on understanding who the on-demand workers are, how to model their unique working behavior, and how to improve their work experience.