University Marketing Research


Promoting the use of data science in marketing.

Every year, Adobe funds a university faculty research program to promote the understanding and use of data science in the area of marketing. Our goal is to encourage both theoretical and empirical development of solutions to problems in marketing.
Adobe will provide funding support of up to $50,000 to a North American academic institution, college, or university for each selected research proposal. Awards will be in the form of an unrestricted gift to the academic institution under the names of the researches who submitted the proposal.
Topics for research include, but are not limited to, the following areas:
Analytics Cloud
Transforming insights from the trillions of customer touch points that we track on behalf of brands into advanced analytical algorithms to make high-value predictions about consumer experiences.
Marketing Cloud
Analyzing data to better deliver personalized digital experiences to customers across websites, applications, emails and notifications.
Advertising Cloud
Using data to help brands design and deliver effective advertising campaigns across search, display, social, video and mobile channels to reach and acquire customers efficiently.



Grant Submission Guidelines & FAQ

Who is eligible to receive the grant?
Full-time faculty members from North American universities
How long should the proposal be?
Preferably two pages
Submission requirements:
Title, proposer name and contact information, research goals, description of project(s), use of funds
Award Range:
Up to $50,000 (US)
August 18, 2017 and February 16, 2018

Send proposals and questions to


August 2017 Award Winners

Drexel University's Elea McDonnel Feit & University of Pennsylvania's Ron Bergman
Advanced Approaches to Planning and Analysis of A/B Tests
MIT's Stefanie Jegelka
Scalable, Adaptive Media Summarization
MIT Sloan's Georgia Perakis
Advertising Portfolio Optimization Under Uncertainty
NYU's Xi Chen & University of Minnesota's Zizhuo Wang
Real-Time Learning and Optimization with Applications to Personalized Product/Ad Recommendation
Purdue University's Wreetabrata Kar & Mohammad Rahman
Harnessing Knowledge Search and Content Condumption Patterns in Consumer Targeting and Product Portfolio Optimization
UC Berkeley's John DeNero
Personalizing Neural Models Using Residual Mixtures of Experts
UC Davis' John Owens
Scalability and Mutability for Large Streaming Graph Problems on the GPU
UC Riverside's Vagelis Papalaxakis
Point-of-interest Recommendation and User Profiling via Scalable Tensor Decompositions
University of Alabama Tuscaloosa's Jacob Chakareski
Efficient 360° Video Streaming for Next Generation Virtual and Augmented Reality Applications
University of Maryland's Ben Shneiderman & Catherine Plaisant
Improving the Transparency of Sequential Recommenders
University of Maryland's Furong Huang
Understanding User Features with Text, Social and Behavior Data through Deep Tensor Neural Network Learning
University of Minnesota's Edward McFowland III
Spatial-Temporal Anomalous Pattern Detection (and A/B Testing)
University of Southern California's Tianshu Sun
IBASE: Adaptive Causal Inference by Integrating Big Data and Small Experiments
University of Southern California's Yan Liu
Deep Fragment Learning - Deep Variational Adversarial Canonical Correlation Analysis (DeVACA) of Fragmented Data for Intelligent Digital Marketing
University of Washington's Natalie Mizik
Increasing Consumer Engagement with Firm-Generated Social Media Content: The Role of Images and Words
Visit Adobe Research for more information.