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Date Category Seminar Info
2013/03/26 General Place: FDA 232
Time: 10:00 - 11:00
Speaker: Edith Law
Affiliation: Harvard
Area: Machine Learning
Title: Towards Crowdsourcing Science

Human computation is the study of intelligent systems where humans are an integral part of the computational process. Well-known examples of human computation systems include crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) which coordinate workers to perform tasks for monetary rewards, games with a purpose (e.g., the ESP Game) which generate useful data through gameplay, and identity verification systems (e.g., reCAPTCHA) that accomplish remarkable feats (e.g., digitize millions of books) through users performing computation for access to online content. One limitation of these human computation systems is that they are restricted to tasks that can easily be decomposed and solved by any person with basic perceptual capabilities and common-sense knowledge. There are many other tasks, however, that are complex and require expertise to solve, but for which human computation would be greatly beneficial. For example, many tasks that arise in the course of scientific inquiries involve data that is unfamiliar to people without formal training (e.g., images of micro-organisms, EEG signals). How can we enable a crowd of people to perform tasks that are difficult, and possibly hard to decompose? In this talk, I will illustrate the key challenges of human computation in the scientific domain, draw connections to my previous work on games with a purpose and crowdware systems, as well as highlight new directions.

Biography of Speaker:

Edith Law is a CRCS postdoctoral fellow at the School of Engineering and Applied Sciences at Harvard University. She graduated from Carnegie Mellon University in 2012 with Ph.D. in Machine Learning, where she studied human computation systems that harness the joint efforts of machines and humans. She is a Microsoft Graduate Research Fellow, co-authored the book "Human Computation" in the Morgan & Claypool Synthesis Lectures on Artificial Intelligence and Machine Learning, co-organized the Human Computation Workshop (HCOMP) Series at KDD and AAAI from 2009 to 2012, and helped create the first AAAI Conference on Human Computation and Crowdsourcing. Her work on games with a purpose and large-scale collaborative planning has received best paper honorable mentions at CHI.

2013/03/15 General Place: MC 103
Time: 11:00 - 12:30
Speaker: Jeff Huang
Affiliation: PhD Candidate in Information Science at the University of Washington
Area: Data Mining
Title: Interaction Data in Search and Beyond: By People, For People

People generate an ever-growing amount of behavioral data when they interact with computer systems. Rather than treating these data purely as numbers or tokens, I will present projects that decode user behavior from the data and construct practical models. One project collects mouse cursor activity on a live search engine, and incorporates these data in two graphical models: one to understand visual attention to predict where people are looking without an eye-tracking device, and one that can be used to improve the relevance of search results. I will also provide examples of how interactions can drive research in games, mobile devices, reviews, and the web. Through this, we can better understand fundamental human behavior and help design systems that allow people to find information faster and easier.

Biography of Speaker:

Jeff Huang is a PhD Candidate in Information Science at the University of Washington. His research in data-driven information retrieval focuses on modeling users from interaction data. He has been awarded Best Paper at SIGIR 2010, two Honorable Mentions at CHI 2011, and the Facebook Fellowship. During his graduate studies, Jeff has conducted research at the University of Washington and five research groups at Microsoft Research and Google, and has received external funding from Google and Microryza. His work appears in venues such as SIGIR, CHI, AAAI, UIST, CIKM, WSDM, as well as in the Wall Street Journal, GeekWire, and the MIT Technology Review. Jeff earned his Masters and Bachelors degrees in Computer Science at the University of Illinois.