Institute Industry Innovation

Cross-device User Clustering at Adobe (CANCELLED)

Thursday, November 29, 2018 - 11:00am to 12:30pm
Schapiro Hall (CEPSR) Davis Auditorium
Columbia University
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars


Charles Menguy, Senior Computer Scientist

As people now engage with digital properties using a myriad of devices such as laptops, smart phones, tablets, connected TVs and gaming consoles, the traditional cookie-based or device-level views of online user interaction are too narrow. Even when using a single device, a person may be assigned multiple IDs due to cookie churn or the use of different browsers. Marketers are looking through a fragmented lens and are spending their marketing dollars without understanding more than a fractional part of consumer interactions.

People centric approach to optimize Data Science, Commercial impact and Leadership

Wednesday, November 14, 2018 - 10:30am to 12:00pm
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars

Amel Lageat, Senior Director, Consumer Business

Abstract: In a world of Infobesity, analysts, engineers, professionals, executive leaders, and people now have access to more data and analytics opportunities that we can ever make sense of. However, a genuine people centric approach can provide the sharpest guidance in designing relevant strategies and solutions: it makes data, models, and analytics more meaningful and purposeful, and also leads to marketing and commercial impact in global organizations.

Going Further: Changing the Airline Industry Beyond the Aircraft

Thursday, October 4, 2018 - 4:00pm to 5:30pm
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars

Dr. Paul Ardis, Research Mission Leader at GE Global Research

Analytics are opening up new possibilities in the aviation sector as we think beyond the airplane during flight. Putting together GE’s expertise in AI for manufacturing and service with airline passenger analytics and intelligent supply chains, we are working towards a future where efficiency and agility is realized to maximize efficiency and minimize disruption.

Semi-automated exploration and extraction of data in scientific tables

Wednesday, September 26, 2018 - 5:00pm to 6:30pm
Columbia University
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars

Ron Daniel, Jessica Cox, Corey Harper

Most of the experimental results reported in scientific articles, and recorded in databases or in supplements to the article, are provided in tables. Unfortunately, the amazing recent progress in natural language understanding is of little help if we want to automatically understand those tables. Tables are, after all, not your grandmother’s natural language. Despite this, we believe significant progress can be made towards the goal of combining tables of related information into larger sets that can be analyzed, visualized, understood, and used as the basis for decisions. Elsevier Labs is prototyping tools to help guide people in the exploration of tables from many articles and the extraction and merging of the data they contain. This talk will show examples of what has been accomplished by manually merging such data. With those as examples of the desired outcomes, we will describe our experiments to duplicate such examples, the work flow in which they operate, and our most recent results.

Solving Complex Problems with Design Thinking and Data Science

Thursday, November 30, 2017 - 11:00am to 12:00pm
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars


Presented by Lucas Saloumi (Data Scientist), Rick Winslow (Head of Commercial Digital Innovation) and Zeina Zeitouni (Product Manager)

Capital One is a pioneer in applying both artificial intelligence and design thinking to make banking smarter, more intuitive and personal. Presenters from the Commercial Banking Digital Innovation team will show how designers and data scientists can work together on cross-functional teams to solve complex problems and build compelling new products. They will share examples of how machine learning classification and outlier detection has been applied in their business. And they will discuss the challenges faced when deploying intelligent systems at scale, all while keeping customers and business partners engaged in the process.

How the New Availability of Urban and Industrial Data are Impacting Our World from Public Safety to Jet Engines

Thursday, May 11, 2017 - 4:00pm to 5:30pm
Schapiro Hall (CEPSR) 750 (Costa Engineering Commons)
Columbia University
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars

(Talk 2 of 2)


Peter Marx, Vice President, Advanced Projects, GE Digital, Adjunct Professor, USC

Estimating Causal Effect of Ads in a Real-Time Bidding Platform

Wednesday, April 26, 2017 - 5:00pm to 6:00pm
Davis Auditorium | The Schapiro Center | Columbia University
530 West 120th Street
New York, NY 10027
United States

Columbia Data Science Innovation Seminars

Prasad Chalasani
SVP, Data Science
MediaMath

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.

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