Extreme Democracy

Title: Extreme Democracy 
Speaker: Ariel Procaccia, Associate Professor in Computer Science, Carnegie Mellon University
Date: December 12, 2017
Time: 12:00-1:00pm
Room: Gates-Hillman Center 6501

Abstract:
I will present several forms of democratic decision making that go far beyond your run-of-the-mill election. Specifically, I will discuss (i) liquid democracy, which allows voters to transitively delegate their votes; (ii) participatory budgeting, in which residents of a city or country vote on how its budget should be divided; and (iii) virtual democracy, which automates ethical decisions by holding elections among models of real voters. I will focus on the computational challenges that these new paradigms give rise to. 

Bio: 
Ariel Procaccia is an Associate Professor in the Computer Science Department at Carnegie Mellon University. He usually works on problems at the interface of computer science and economics. His distinctions include the IJCAI Computers and Thought Award (2015), the Sloan Research Fellowship (2015), the NSF Faculty Early Career Development Award (2014), and the IFAAMAS Victor Lesser Distinguished Dissertation Award (2009); as well as half a dozen paper awards, including Best Paper (2016) and Best Student Paper (2014) at the ACM Conference on Economics and Computation (EC). He is co-editor of the Handbook of Computational Social Choice (Cambridge University Press, 2016). 


Towards a Universal Knowledge Accelerator

Title: Towards a Universal Knowledge Accelerator
Speaker: Aniket Kittur, Associate Professor in Human-Computer Interaction Institute, Carnegie Mellon University
Date: October 17, 2017
Time: 12:00-1:00
Room: Gates-Hillman Center 6501

Abstract:
The human mind remains an unparalleled engine of innovation, with its unique ability to make sense of complex information and find deep analogical connections driving progress in science and technology over the past millennia. The recent explosion of online information available in virtually every domain should present an opportunity for accelerating this engine; instead, it threatens to slow it as the information processing limits of individual minds are reached. 

In this talk I discuss our efforts towards building a universal knowledge accelerator: a system in which the sensemaking people engage in online is captured and made useful for others, leading to virtuous cycles of constantly improving information sources that in turn help people more effectively synthesize and innovate. Approximately 70 billion hours per year in the U.S. alone are spent on complex online sensemaking in domains ranging from scientific literature to health; capturing even a fraction of this could provide significant benefits. We discuss three integrated levels of research that are needed to realize this vision: at the individual level in understanding and capturing higher order cognition; at the computational level in developing new interaction systems and AI partners for human cognition; and at the social level in developing complex and creative crowdsourcing and social computing systems.


Bio:
Aniket Kittur is an Associate Professor and holds the Cooper-Siegel Chair in the Human-Computer Interaction Institute at Carnegie Mellon University. His research looks at how we can augment the human intellect using crowds and computation. He has authored and co-authored more than 70 peer-reviewed papers, 14 of which have received best paper awards or honorable mentions. Dr. Kittur is a Kavli fellow, has received an NSF CAREER award, the Allen Newell Award for Research Excellence, major research grants from NSF, NIH, Google, and Microsoft, and his work has been reported in venues including Nature News, The Economist, The Wall Street Journal, NPR, Slashdot, and the Chronicle of Higher Education. He received a BA in Psychology and Computer Science at Princeton, and a PhD in Cognitive Psychology from UCLA.


Learning from People

Title: Learning from People
Speaker: Nihar B. Shah, Assistant Professor in Machine Learning and Computer Science, Carnegie Mellon University
Date: October 10, 2017
Time: 12:00-1:00pm
Room: Gates-Hillman Complex 6501

Abstract:
Learning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk, I describe progress on these challenges in the context of two canonical settings, namely those of ranking and classification. In addressing the first challenge, I introduce a class of "permutation-based" models that are considerably richer than classical models, and present algorithms for estimation that are both statistically optimal and significantly more robust than prior state-of-the-art methods. I also discuss how these algorithms automatically adapt and are simultaneously also optimal over the classical models, thereby enjoying a surprising a win-win. As for the second challenge, I present a class of "multiplicative" incentive mechanisms, and show that they are the unique mechanisms that can guarantee honest responses. Extensive experiments on a popular crowdsourcing platform reveal that the theoretical guarantees of robustness and efficiency indeed translate to practice, yielding several-fold improvements over prior art.

Bio:
Nihar B. Shah is an Assistant Professor in the Machine Learning and Computer Science departments at CMU. He is a recipient of the the 2017 David J. Sakrison memorial prize from EECS Berkeley for a "truly outstanding and innovative PhD thesis", the Microsoft Research PhD Fellowship 2014-16, the Berkeley Fellowship 2011-13, the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012, and the SVC Aiya Medal 2010. His research interests include statistics, machine learning, and game theory, with a current focus on applications to learning from people.


Solving Photo Mysteries with Expert-Led Crowdsourcing

Title: Solving Photo Mysteries with Expert-Led Crowdsourcing
Speaker: Kurt Luther, Department of Computer Science, Virginia Tech
Time: 12:30 - 1:30pm
Room: Gates-Hillman Complex 6501

Abstract:
Despite the old adage that a picture is worth a thousand words, images often need context to be meaningful to their viewers. In this talk, I show how expert-led crowdsourcing, a novel approach that combines the relative strengths of experts and amateur crowds, can be used to solve photo mysteries. In one example, I conducted a qualitative study of image verification experts in journalism, national security, and human rights organizations to understand how they perform geolocation, the process of mapping the precise location where a photo or video was taken. This research informed the design of GroundTruth, a system where experts collaborate with crowds to geolocate unknown images. In another example, I partnered with a historical photography magazine to develop Civil War Photo Sleuth, a system that leverages crowdsourcing and computer vision techniques to help experts identify unknown soldier portraits from the 19th century. I also discuss broader challenges and opportunities in crowdsourced investigations, open-source intelligence, and collaborative sensemaking illustrated by these examples.

Bio:
Kurt Luther is an assistant professor of computer science at Virginia Tech, where he is also affiliated with the Center for Human-Computer Interaction, the Department of History, and the Hume Center for National Security and Technology. He directs the Crowd Intelligence Lab (http://crowd.cs.vt.edu), an interdisciplinary research group exploring how crowdsourcing systems can support creativity and discovery. He is principal investigator for over $1.5M in sponsored research, including an NSF CAREER Award. Previously, Dr. Luther was a postdoctoral fellow in the HCI Institute at Carnegie Mellon University. He received his Ph.D. in human-centered computing from Georgia Tech, where he was a Foley Scholar, and his B.S. in computer graphics technology from Purdue University. He has also worked at IBM Research, Microsoft Research, and YouTube/Google.


Supporting Collective Ideation at Scale

Title: Supporting Collective Ideation at Scale
Speaker: Pao Siangliulue, Department of Computer Science, Harvard University
Date: April 18, 2017
Time: 12:30 - 1:30pm

Abstract:
A growing number of online collective ideation platforms, such as OpenIDEO or Quirky, have demonstrated the potential of large-scale collaborative innovation in various domains. However, these platforms also introduce new challenges. People have to wade through a sea of possibly mundane and redundant ideas before encountering genuinely inspiring ones. Further, once all ideas are collected, the communities have to spend a lot of time and effort to synthesize the ideas into a few solutions. Alternatively, an intelligent system can select and present ideas for its users instead of leaving them to look for inspirations in a haphazard way.

In this talk, I will show how a system can decide which ideas to present to the users and when to do so. I will introduce a computational model of an idea space, two crowdsourcing methods to generate this model and the model's application for creativity-enhancing interventions. I will also present an empirical study on the effects of timing of example delivery on people's idea generation.


Bio:
Pao is a Ph.D. candidate in Computer Science focusing on Human-Computer Interaction (HCI) research at Harvard University. She works with Prof. Krzysztof Gajos in the Intelligent Interactive Systems Group. Her research explores how we can apply intelligent technologies and crowdsourcing to enable novel ways for people to come up with creative ideas together. Pao received her B.S. in Electrical Engineering and M.S. in Computer Science from Stanford University where she worked in Stanford HCI group.