The Data Science Institute's Centers for Data, Media & Society (formerly New Media) + Health Analytics will be holding a poster session, Wednesday, May 3rd from 2PM to 4PM in 407 Mudd featuring poster presentations and demonstrations related t
A panel of 4 hedge funds that are using Big Data and Machine Learning to invest, moderated by BAML PB Consulting.
SVP, Data Science
Title: Estimating Causal Effect of Ads in a Real-Time Bidding Platform
Abstract: 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.
Dr. Michael Winter
Senior Fellow, Advanced Technology
Pratt & Whitney, United Technologies Corporation
Systems Engineering: Imperatives, Definitions, Technology & Talent
The lecture will present the motivation, mechanics, and methodologies of model-based systems engineering as applied to product platforms and infrastructures that are often safety or operationally critical. Cyber-physical system-of-systems that combine both physics and controls form the basis of modern society. Application of systems engineering principles in an analytic context with focus on requirements, architecture, model-based development, and design flows will be presented as applied in an industrial context.
The Data Science Institute (DSI) at Columbia University and Bloomberg LP are pleased to announce a workshop on "Machine Learning in Finance". The workshop will be held at Columbia University under the auspices of the Financial and Business Analytics Center, one of the constituent centers in the DSI, and the Center for Financial Engineering. More Information and Tickets Here.
The Distinguished Colloquium Series in Interdisciplinary and Applied Mathematics, along with Columbia's Data Science Institute, proudly present a lecture by:
Jennifer Tour Chayes
Distinguished Scientist and Managing Director of Microsoft Research New England, Cambridge, MA.
Title: "Once upon a graph: How to get from now to then in massive networks"
HealthHacks is a two-day hackathon, combining creative hardware and software solutions to solve real-world healthcare problems through innovation, creativity, and interdisciplinary teamwork.