Adobe Awards Research Grants to Fund Innovative Data Science Projects

October 16, 2014

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In June of this year, Adobe launched, for the first time, the Adobe Digital Marketing Research Awards for North America university faculty. Professors focused on digital marketing from top universities in the U.S. and Canada submitted proposals. Eight proposals were selected and recipients received US50,000 each to fund data science projects in the areas of media optimization, analytics, social, and more.

As one of the largest software-as-a-service platform providers in the world, Adobe has quickly become a powerhouse in digital marketing with petabytes of rich data assets and Fortune 500 customers to prove it. The company is doing exciting, industry-leading work in data science for customers such as predicting consumer preferences, determining marketing mix attribution, and maximizing social media engagement. Through these awards, Adobe wants to collaborate with the academic data science community to fuel innovative research in digital marketing.

“We’re seeing talented people make incredible advances in data science at the university level, and Adobe wants to help further their research,” says Anil Kamath, Adobe fellow and vice president of technology. “We believe that collaboration between industry and academia can push the frontiers of how data science is used in digital marketing, and we’re excited to see what these awards can help researchers accomplish.”

From the proposals received, Adobe selected eight winning research projects.

Recipient: Ramesh Johari, Stanford University

Project: Causal Interface Meets Machine Learning: Using Predictive Models to Evaluate Interventions

“It’s a great benefit that Adobe is running this research grant program because it provides a chance to demonstrate proof-of-concept before scaling up a research project,” Johari says. “It is ideal for high-risk, high-reward ideas.”

Recipient: Peter Bartlett, University of California, Berkeley

Project: Sequential Decision Methods for Optimizing Long-Term Value

“This award is facilitating collaborations with Adobe researchers and helping me and my students and postdocs to learn about applications of our research in digital marketing,” Bartlett says. “The awards will facilitate better connections between researchers in Adobe and the rest of the research community, giving an opportunity to steer that community’s research agenda in a direction that addresses problems that arise in digital marketing.”

Recipient: John Owens, University of California, Davis and Stephen Boyd, Stanford University

Project: Scaling Convex Optimization with GPUs

“In an era of declining governmental support of research coupled with incredibly interesting problems in industry, I couldn’t be happier that Adobe is reaching out to the research community with these awards and the opportunity to work with Adobe’s amazing engineers.”

Recipient: Lise Getoor, University of California, Santa Cruz

Project: Cross-Device Graph-based Entity Resolution

“Collaborating and working with industry is an important component of my research and having this opportunity to work with Adobe is extremely exciting,” Getoor says. “These awards are a great way to help build ties between industry and academia.”

Recipient: Alexander Smola and Ryan Tibshirani, Carnegie Mellon University

Project: Graph Trend Filtering for Recommendation

“Our project intends to test out a new method we’ve developed for learning how to better model data over graphs,” Tibshirani says. “Think of a graph as representing a custom way of connecting objects, such as people, and the connections could represent friendships. The technique we developed allows us to represent different strengths of relationships at different places in the graph. This could prove to be particularly useful for building a recommendation system, where we hope to recommend favorable items to users, based on the items that their friends have seen and rated highly.”

Recipient: Kinshuk Jerath, Miklos Sarvary, and Ryan Dew, Columbia University

Project: Large Scale Experiments on the Efficacy of Digital Advertising

“Our team is trying to run large-scale online experiments on advertising effectiveness, with advertisement exposures in the order of millions,” Jerath says. “This is very expensive to do, and without the award, we would basically have not been able to make any headway in this project. We feel very fortunate that we have received this award.”

Recipient: Aravind Srinivasan, University of Maryland, College Park

Project: Matching Advertisements and Content to Customers

“This award will help me understand the industry perspective well, and enable me to work with Adobe scientists who have a practical and foundational understanding of the relevant problems,” Srinivasan says.

Recipient: Craig Boutilier, University of Toronto, Ontario

Project: Sequential Attribution Modeling and Optimization in Digital Marketing

Find more information on the Adobe Digital Marketing Research Awards Program here.