A panel discussion of Election 2016 featuring:
Founder and Editor in Chief, FiveThirtyEight
Director of the Tow Center for Digital Journalism, Columbia University
Wallace S. Sayre Professor of Political Science, Columbia University
Ester Fuchs (Moderator)
Professor of International and Public Affairs and Political Science, Columbia University
Data, Polling, the Media and Democracy
Tuesday, December 6, 2016
5:30–7:00 p.m. (Doors open at 5:00 p.m.)
Rotunda, Low Memorial Library
Columbia University in the City of New York
Opening Remarks by David Madigan, Executive Vice President for Arts and Sciences, Professor of Statistics, Columbia University
Convened by President Bollinger and Provost Coatsworth, the Data & Society Taskforce is comprised of Deans and faculty from across the Columbia community with a special interest in data science and its impact on education and research at Columbia. The Taskforce is led by Mary Boyce, Dean of Engineering and Applied Science, and David Madigan, EVP for Arts & Sciences.
Sponsored by the Data Science Institute, Columbia University
The US Presidential election is probably the most significant political event in 2016. The spectacular campaign period has featured many controversial news stories. Many analysts have given their views on how the public image of Clinton and Trump affect the candidates’ success; what kinds of stories stick with a candidate, who earns on a certain story, and what do the candidates’ constantly shifting public images mean for the outcome of the election?
Describing the landscape of political positions and measuring the effects of speeches and events is very difficult to do. This session will feature research from United Minds, a Weber Shandwick company, supported by text analytics technology firm Gavagai.
Preparing Tomorrows Leaders for a Data-Rich World
Cyber-physical systems are engineered systems that require tight conjoining of and coordination between the computational (discrete) and the physical (continuous). Cyber-physical systems are rapidly penetrating every aspect of our lives, with potential impact on sectors critical to national security and competitiveness, including aerospace, automotive, chemical production, civil infrastructure, energy, finance, healthcare, manufacturing, materials, and transportation. As these systems fulfill the promise of the Internet of Things, smart cities, household robots, and personalized medicine, we need to ensure they are trustworthy: reliable, secure, and privacy-preserving. This talk will look at cyber-physical systems from the lens of trustworthy computing. Throughout my talk, I will raise research challenges for how to make cyber-physical systems trustworthy, with a special emphasis on privacy.
Colloquium Series: Jeannette Wing
Corporate Vice President, Microsoft Research
Colloquium Series: Dr. Robert Schapire
Principal Researcher, Microsoft Research (NYC)
Alumni Networking Event and Panel
Presented by Columbia Business School Private Equity Program
Hosted by Alston & Bird LLP, 90 Park Avenue
SVP, Data Science
A real-time bidding platform responds to incoming ad-opportunities ("bid requests") by deciding whether or not to submit a bid and how much to bid. If the submitted bid wins, the user is shown an ad. Advertisers hope that ad-exposure leads to an increased likelihood of a desired action, such as a click or conversion (purchase, etc). So an important quantity that advertisers want to measure is the causal effect of advertising, namely, what is the response probability of an exposed user, compared with the counterfactual (un-observable) response-rate of the user if they were not exposed to the ad. In an ideal randomized test, the user is randomly assigned to test or control AFTER the submitted bid is won, and test users are served the ad in the normal way, while control users are not. While this is ideal from a statistical perspective, in practice this approach has the drawback that money spent by advertisers is wasted when a user is assigned to control. At Media Math we have developed a methodology for causal effect measurement where users are assigned to test or control BEFORE bid submission. One challenge here is that not all test-group users are exposed to an ad; only a winning bid results in ad exposure, and the winning population can have a significant bias. This talk will describe our approach to handle this and other challenges to ad impact measurement in this setting, and how we use MCMC Gibbs sampling to arrive at confidence intervals for ad-impact.
Networks are increasingly important in many aspects of our world: physical networks like transportation networks, utility networks and the Internet, online information networks like the WWW, online social networks like Facebook and Twitter, epidem