KEY DATES

  • Conference
    Paper abstracts due: Jul. 31, 2012
    Papers due: Aug. 7, 2012
    Paper notifications: Oct. 26, 2012
    Camera ready deadline: Nov. 30, 2012
    • Conference: Feb. 6-8, 2013
  • Workshops
    Workshop proposals due: Jun. 29, 2012
    Workshop notifications: Aug. 3, 2012
    Paper notifications: Nov. 30, 2012
    • Workshops: Feb. 4-5, 2013
  • Tutorials
    Tutorial proposals due: Aug. 31, 2012
    Tutorial notifications: Oct. 12, 2012
    Tutorial camera ready: Nov. 2, 2012
    • Tutorial: Feb. 4-5, 2013
  • Data Challenge
    Launch Data Challenge: Aug. 31, 2012
    Registration to participate: Sep. 30, 2012
    End Data Challenge: Dec. 14, 2012
    • Data Challenge presentations: Feb. 5, 2013
  • Doctoral Consortium
    Submission: Sep. 14, 2012 Sep. 21, 2012

    Notifications: Nov. 20, 2012
    Camera ready deadline: Nov. 30, 2012
    • Doctoral Consortium: Feb. 5, 2013

Keynote speakers

 

Catherine Tucker

MIT Sloan School of Management

Catherine Tucker is the Mark Hyman Jr. Career Development Professor and Associate Professor (with tenure) of Marketing at MIT Sloan. Her research interests lie in how technology allows firms to use digital data to improve their operations and marketing and in the challenges this poses for regulations designed to promote innovation. She has particular expertise in online advertising, digital health, social media and electronic privacy. Generally, most of her research lies in the interface between Marketing, Economics and Law. She has received an NSF CAREER award for her work on digital privacy and a Garfield Award for her work on electronic medical records. Dr. Tucker is Associate Editor at Management Science and a Research Associate at the National Bureau of Economic Research.

Three Findings Concerning Protecting Consumer Privacy Online

The Internet now enables firms to collect detailed and potentially intrusive data about their customers both easily and cheaply. I discuss three empirical results related to customer privacy-protection that is enacted in response to this change:
1) Privacy protection that focuses on obtaining consent appear to restrict economic outcomes.
2) Privacy protection which gives direct control over customers' privacy appears to enhance economic outcomes.
3) Restricting the length of time that potentially private data is stored appears to have little economic impact.

 


 

Duncan Watts

Microsoft Research

Duncan Watts is a principal researcher at Microsoft Research and a founding member of the MSR-NYC lab. From 2000-2007, he was a professor of Sociology at Columbia University, and then, prior to joining Microsoft, a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group. He is a former external professor of the Santa Fe Institute and is currently a visiting fellow at Columbia University and at Nuffield College, Oxford. His research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review. He is also the author of three books; “Small Worlds: The Dynamics of Networks Between Order and Randomness (Princeton, 1999); “Six Degrees: The Science of A Connected Age” (Norton, 2003), and most recently “Everything is Obvious (Once You Know The Answer)” (Crown Business, 2011). He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.

 

The Virtual Lab

The Internet and the Web, have transformed society, spawning new industries, altering social and cultural practices, and challenging long-accepted notions of individual privacy, intellectual property, and national security. In this talk, I argue that social science is also being transformed. In particular, I describe how crowd sourcing sites like Amazon's Mechanical Turk are increasingly being used by researchers to create "virtual labs" in which they can conduct behavioral experiments on a scale and speed that would have been hard to imagine just a decade ago. To illustrate the point, I describe some recent experiments that showcase the advantages of virtual over traditional physical labs, as well as some of the limitations. I then discuss how this relatively new experimental capability may unfold in the near future, along with some implications for social and behavioral science.

 


 

Qiang Yang

Huawei and HKUST

Prof. Qiang Yang is the head of Huawei Noah’s Ark Lab in Hong Kong. He has been a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST) since 2007. Prior to joining HKUST, he had been a faculty member at the University of Waterloo and Simon Fraser University in Canada. He is an IEEE Fellow, IAPR Fellow and ACM Distinguished Scientist. His research interests are data mining and artificial intelligence. Qiang received his PhD from the University of Maryland, College Park in 1989. His research teams won the 2004 and 2005 ACM KDDCUP competitions on data mining. He is the vice chair of ACM SIGART, the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST), and organizer for many international conferences and workshops, including the PC Co-chair for ACM KDD 2010, the General Chair for ACM KDD 2012 in Beijing and PC Chair for IJCAI 2015 Conference in Argentina.

 

Big Data, Lifelong Machine Learning and Transfer Learning

A major challenge in today's world is the Big Data problem, which manifests itself in Web and Mobile domains as rapidly changing and heterogeneous data streams. A data-mining system must be able to cope with the influx of changing data in a continual manner. This calls for Lifelong Machine Learning, which in contrast to the traditional one-shot learning, should be able to identify the learning tasks at hand and adapt to the learning problems in a sustainable manner. A foundation for lifelong machine learning is transfer learning, whereby knowledge gained in a related but different domain may be transferred to benefit learning for a current task. To make effective transfer learning, it is important to maintain a continual and sustainable channel in the life time of a user in which the data are annotated. In this talk, I outline the lifelong machine learning situations, give several examples of transfer learning and applications for lifelong machine learning, and discuss cases of successful extraction of data annotations to meet the Big Data challenge.