Optimizing Human Computation
Speaker: Aditya Parameswaran , Stanford UniversityContact:
Date: April 5 2012
Time: 4:00PM to 5:00PM
Host: Sam Madden, CSAIL
Francis Doughty, 253-4602, firstname.lastname@example.org
Many data processing tasks are better done by humans ("the crowd") than by computers. For instance, humans are much better at understanding and analyzing images, video, or text. Humans can also be good sources of information, e.g., providing ratings or favorite dishes for local restaurants. We consider the overall problem of optimizing how human computation is combined with computer algorithms and systems. In general, we seek to expend as few resources as possible (e.g, time waiting, money spent), while still providing high quality results.
In this talk I will describe two aspects of our crowdsourcing work:: (a) Crowd Algorithms: Viewing humans as data processors, our goal is to incorporate human computation into fundamental algorithms such as sorting, filtering, and searching, while meeting certain optimization objectives. (b) Declarative Crowdsourcing: Viewing humans as data providers, our goal is to build a database system that seamlessly combines traditional stored data with data collected from humans as part of query processing. I will present a few examples of the computational challenges in each case, and provide solutions.
Aditya Parameswaran is a fifth year PhD student in the Infolab at Stanford University, working with Hector Garcia-Molina. He is broadly interested in information management. Specifically, he has been working on optimizing human computation, information extraction and recommendation systems. He is a recipient of the Key Scientific Challenges award (2010) from Yahoo! Research, selection for the "Best Papers of VLDB 2010", as well as the Terry Groswith fellowship at Stanford University. He graduated at the top of his undergraduate class from the Indian Institute of Technology (IIT) Bombay (2007).
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