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Stanford Data Science / Infoseminar: Applying theory to practice (and practice to theory)

Topic: 
Applying theory to practice (and practice to theory)
Abstract / Description: 

The speaker will talk about applying theory to practice, with a focus on two IBM case studies. In the first case study, the practitioner initiated the interaction. This interaction led to the following problem. Assume that there is a set of "voters" and a set of "candidates", where each voter assigns a numerical score to each candidate. There is a scoring function (such as the mean or the median), and a consensus ranking is obtained by applying the scoring function to each candidate's scores. The problem is to find the top k candidates, while minimizing the number of database accesses. The speaker will present an algorithm that is optimal in an extremely strong sense: not just in the worst case or the average case, but in every case! Even though the algorithm is only 10 lines long (!), the paper containing the algorithm won the 2014 Gödel Prize, the top prize for a paper in theoretical computer science.

The interaction in the second case study was initiated by theoreticians, who wanted to lay the foundations for "data exchange", in which data is converted from one format to another. Although this problem may sound mundane, the issues that arise are fascinating, and this work made data exchange a new subfield, with special sessions in every major database conference.

This talk will be completely self-contained, and the speaker will derive morals from the case studies. The talk is aimed at both theoreticians and practitioners, to show them the mutual benefits of working together.


Stanford Data Science / Infoseminar is a weekly event held at Stanford that brings together people interested in big data, analytics, databases, and other interesting computer science topics.

Date and Time: 
Friday, January 23, 2015 - 4:15pm to 5:15pm
Venue: 
Huang Engineering Center, Nvidia Auditorium
Tags: 

Stanford Data Science / Infoseminar: Reverse-Engineering Censorship in China

Topic: 
Reverse-Engineering Censorship in China
Abstract / Description: 

Chinese government censorship of social media constitutes the largest selective suppression of human communication in recorded history. In three ways, we show, paradoxically, that this large system also leaves large footprints that reveal a great deal about itself and the intentions of the government. First is an observational study where we download all social media posts before the Chinese government can read and censor those they deem objectionable, and then detect from a network of computers all over the world which are censored. Second, we conduct a large scale randomized experiment by creating accounts on numerous social media sites spread throughout the country, submitting different randomly assigned types of social media texts, and then detecting which types are censored. And finally, we supplement the current approach of conducting uncertain (and potentially unsafe) confidential interviews with insiders via participant observation by setting up our own social media site in China, contracting with Chinese firms to install the same censoring technologies as existing sites, and -- with direct access to their software, documentation, and even customer service help desk support -- reverse engineering how it all works. Our results demonstrate, contrary to prior understandings, that criticism of the state, its leaders, and their policies are routinely published whereas posts with collective action potential are much more likely to be censored (regardless of whether they are for or against the state). We are also able to clarify the internal mechanisms of the Chinese censorship apparatus, and show how changes in censorship behavior reveal government intent by presaging their action on the ground. This talk is based on two papers, joint with with Jennifer Pan and Margaret Roberts, available at http://j.mp/ChinaExp and http://j.mp/ChinaObs.


Stanford Data Science / Infoseminar is a weekly event held at Stanford that brings together people interested in big data, analytics, databases, and other interesting computer science topics.

Date and Time: 
Friday, January 16, 2015 - 4:15pm to 5:15pm
Venue: 
Huang Engineering Center, Nvidia Auditorium
Tags: 

Stanford Data Science / Infoseminar: From bytes to bites: How data science might help feed the world

Topic: 
From bytes to bites: How data science might help feed the world
Abstract / Description: 

There is a lot of hype now about using big data in agriculture. This talk will present some background on the big current questions in the topics of agriculture and food security, briefly outline a vision for how data science can contribute to improving both agriculture and food security, detail some current work in our center towards this vision, and highlight some of the remaining technical obstacles.


Stanford Data Science / Infoseminar is a weekly event held at Stanford that brings together people interested in big data, analytics, databases, and other interesting computer science topics.

Date and Time: 
Friday, January 9, 2015 - 4:15pm to 5:15pm
Venue: 
Huang Engineering Center, Nvidia Auditorium
Tags: 

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