Crowdsourcing has been increasingly popular for gaining programmatic access to human intelligence for solving tasks that computers cannot easily perform alone. To date, computers have been employed largely in the role of platforms for recruiting and reimbursing human workers; the burden of managing crowdsourcing tasks and ensuring quality has relied on manual designs and controls.
In this talk, Ece Kamar shows how machine learning and decision-theoretic reasoning can be used in harmony to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks efficiently. This methodology, which we refer to as CrowdSynth, includes predictive models that perform inference about workers and tasks, and efficient algorithms for making effective decisions. We demonstrate the way CrowdSynth methodology can help to maximize the efficiency of a large-scale crowdsourcing operation with experiments on a large-scale citizen-science project called Galaxy Zoo.
Who is Ece Kamar?
Ece Kamar who was born in Izmir in 1983, studied at Bornova Anatolian and Izmir Science High School. He graduated from Sabancı University Computer Science and Engineering Department in 2005. She received her Ph.D. from Harvard University Computer Science.
Kamar was awarded the Robert L. Wallace Prize Award in Harvard and the Microsoft Research Postgraduate Research Scholarship Award and focused on models and algorithms for effective human-computer team work at Harvard’s dissertation thesis.
Kamar is currently a senior researcher in the Adaptive Systems and Interaction group within Microsoft Research in Redmond. In addition, some of Kamar’s research, which has published more than 40 fictional magazines in its most respected artificial intelligence publications, was patented and used in Microsoft products.
