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 dma-research-awards@adobe.com

Ashwin Pananjady & Vidya Muthukumar Georgia Institute of Technology Building models for customer intelligence from evolving behavior

Murat Kocaoglu Purdue University Causal Discovery for Root Cause Analysis

Rishabh Iyer University of Texas at Dallas Subset Selection for Compute-Efficient Language Model Pretraining

Yong Jae Lee University of Wisconsin-Madison 3D Controllable, Aesthetically Pleasing Visual Content Creation

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

Elena Zheleva Universidad de los Andes Heterogeneous treatment effect estimation with guarantees

Prof. Anshumali Shrivastava Olin College of Engineering Theory and Practice of Efficient Graph-Based Near-Neighbor at Scale

Ramesh Raskar Brigham Young University Learning Behavioral Policies over Networks of Interacting Agents

Raymond Fu, Ph.D. Massachusetts Institute of Technology Time-Aware Casual Embedding for Personalized Digital Marketing

Adel Javanmard University of Southern California Learn your customer novel statistical methods for segmenting online users and their behaviors

Ahangyang “Atlas” Wang The University of Texas Towards Automated Design of Efficient Deep Multi-Modal Recommendation Models

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

Ambuj Tewaria University of Michigan Causality in Decision Making: Bandits and Beyond

Guang Cheng Purdue University Towards A/B Testing Automation: a Multi-armed Bandit Framework

Philip Thomas University of Massachusetts Amherst Preventing the Exploitation of Cognitive Biases

Rose Yu Northeastern University Context-Aware Customer Journey Optimization in Spatiotemporal Environment

Aaditya Ramdas Carnegie Mellon University Online experimentation with quantiles

Leman Akoglu Carnegie Mellon University Entity resolution and user de-duplication of recurring fraudsters and free-loaders

Srijan Kumar Georgia Tech PICANTE: Deep Representation Learning for Periodic User Clustering

Xiang Ren University of Southern California Empowering Interpretable Recommendation with User Knowledge Graphs

Yu-Xiang Wang University of California, Santa Barbara From Off-Policy Evaluation to Reinforcement Learning with Low-Switching Cost