Data Science Research Awards

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 the 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 researchers who submitted the proposal

Each proposal will address an area of interest relative to Adobe Experience Cloud. The comprehensive set of cloud services gives businesses access to an integrated set of solutions to build campaigns, manage advertising, and gain deep intelligence about the business. It’s everything a company needs to drive brand growth and archestrate an amazing customer experience.

See an extended list of suggested research topics here.

2022 Submission deadlines: February 11th and August 12th

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)                                                                                                                                                                                                  *Adobe does not pay for overhead on unrestricted gifts. This unrestricted gift should not be used for indirect costs, administrative costs, and overhead charges.

Send proposals and questions to

August 2021 Award Winners

Aditya Grover

University of California, Los Angeles

Robust and Scalable Multimodal Reasoning With Unpaired Data

Jasjeet Sekhon and Colleen Chan

Yale University

Statistical Surrogacy for Heterogenous Treatment Effect Estimation and Treatment Decision Rules

Nan Jiang

University of Illinois at Urbana-Champaign

Towards Reliable Validation and Evaluation in Offline Reinforcement Learning

Ugur Kursuncu

Georgia State University

Multimodal Knowledge-infused Learning for Context-aware Consumer-Content Mapping

February 2021 Award Winners

Prof. Anshumali Shrivastava

Rice University

Theory and Practice of Efficient Graph-Based Near-Neighbor at Scale

Elena Zheleva

University of Illinois at Chicago

Heterogeneous treatment effect estimation with guarantees

Ramesh Raskar

Massachusetts Institute of Technology

Learning Behavioral Policies over Networks of Interacting Agents

Raymond Fu, Ph.D.

Northeastern University, Boston

Time-Aware Casual Embedding for Personalized Digital Marketing

August 2020 Award Winners

Lingzhou Xue and Qian Chen

Pennsylvania State University and University of Nebraska Lincoln

Scalable Graphical Event Models for Marketing Attribution

Wreetabrata Kar and Mohammad Rahman

Purdue University

An Advertiser’s Dilemma with Marketing Interventions: At Scale Heterogenous Treatment Effects to Rescue

Ahangyang “Atlas” Wang

The University of Texas

Towards Automated Design of Efficient Deep Multi-Modal Recommendation Models

Adel Javanmard

University of Southern California

Learn your customer novel statistical methods for segmenting online users and their behaviors