Crowd-sourcing systems provide means to harness human ability at a large-scale to solve a variety of problems effectively. Examples abound of classical surveys for collecting opinion of a group to the modern setting of social recommendations. In this talk, we shall discuss effective ways to design crowd-sourcing experiments as well as aggregate the information collected. In the context of Mechanical Turk framework, this leads to automated approach for getting a task done at the minimum possible cost. Time-permitting, different variations of the theme will be discussed. This is based on joint work with D. Karger (MIT) and S. Oh (UIUC).
Devavrat Shah is currently a Jamieson career development associate professor with the department of electrical engineering and computer science, MIT. He is a member of the Laboratory for Information and Decision Systems (LIDS) and Operations Research Center (ORC). His research focus is on theory of large complex networks which includes network algorithms, stochastic networks, network information theory and large scale statistical inference.
Devavrat Shah received his Bachelor of Technology in Computer Science and Engineering from Indian Institute of Technology, Bombay in 1999 with the Presidents of India Gold Medal – awarded to the best graduating student across all engineering disciplines. He received his PhD in Computer Science from Stanford University in 2004. His doctoral thesis titled “Randomization and Heavy Traffic Theory: New Approaches for Switch Scheduling Algorithms” was completed under supervision of Balaji Prabhakar. His thesis was adjudged winnder of George B. Dantzig best dissertation award from INFORMS in 2005. After spending a year between Stanford, Berkeley and MSRI, he started teaching at MIT in Fall 2005.