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.
2023 Submission deadlines: February 10th and August 11th
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.
Himabindu Lakkaraju and Chirag Agarwal
Harvard UniversityCounterfactual LLM Policies for Personalized Content Recommendation
Jiasi Chen and Samet Oymak
University of MichiganCheaper and Query-aware Inference for Language Models: Optimal LM Cascades by Modeling Future Rewards
Jiebo Luo
University of RochesterPersonalized Recommendation via Prompting Generative Models: Multimodal User/Content Input Augmentation
Xiaojing Dong and Steven Wu
Santa Clara University and Carnegie Mellon UniversityA New Approach to Build Individual Level Model with Walled Gardens Data
Aditya Grover
University of California, Los AngelesLearning Prompts for Deep Generative Models via Multimodal Constraints and Reinforcement Learning
Eugene Wu
Columbia UniversityInteractive Explanatory Analyses for Big Data Business KPIs
Ugur Kursuncu
Georgia State UniversityLearning A Domain-specific Knowledge Graph for Persuasive Marketing Communications
Xiaolong Wang
University of California, San DiegoOpen-Vocabulary Perception by Unifying Text-Driven Synthesis and Grounding
Adel Javanmard and Gourab Mukherjee
University of Southern CaliforniaStatistical Joint Modeling for Integrated Marketing Flows: Personalized Promotions Optimized Over Journey and Lifespan
Danqi Chen
Princeton UniversityMemory-Augmented Approaches for Long Document Understanding and Summarization
Dr. Sheng Li
University of VirginiaMeasuring Campaign Effectiveness by Disentangling Observational Marketing Data
Peng Zhang and Guanyang Wang
Rutgers UniversityImproving the Design of Randomized Controlled Trials via Discrepancy Theory
Ashwin Pananjady & Vidya Muthukumar
Georgia Institute of TechnologyBuilding models for customer intelligence from evolving behavior
Murat Kocaoglu
Purdue UniversityCausal Discovery for Root Cause Analysis
Rishabh Iyer
University of Texas at DallasSubset Selection for Compute-Efficient Language Model Pretraining
Yong Jae Lee
University of Wisconsin-Madison3D Controllable, Aesthetically Pleasing Visual Content Creation
Aditya Grover
University of California, Los AngelesRobust and Scalable Multimodal Reasoning With Unpaired Data
Jasjeet Sekhon and Colleen Chan
Yale UniversityStatistical Surrogacy for Heterogenous Treatment Effect Estimation and Treatment Decision Rules
Nan Jiang
University of Illinois at Urbana-ChampaignTowards Reliable Validation and Evaluation in Offline Reinforcement Learning
Ugur Kursuncu
Georgia State UniversityMultimodal Knowledge-infused Learning for Context-aware Consumer-Content Mapping
Elena Zheleva
Universidad de los AndesHeterogeneous treatment effect estimation with guarantees
Prof. Anshumali Shrivastava
Olin College of EngineeringTheory and Practice of Efficient Graph-Based Near-Neighbor at Scale
Ramesh Raskar
Brigham Young UniversityLearning Behavioral Policies over Networks of Interacting Agents
Raymond Fu, Ph.D.
Massachusetts Institute of TechnologyTime-Aware Casual Embedding for Personalized Digital Marketing
Adel Javanmard
University of Southern CaliforniaLearn your customer novel statistical methods for segmenting online users and their behaviors
Ahangyang “Atlas” Wang
The University of TexasTowards Automated Design of Efficient Deep Multi-Modal Recommendation Models
Lingzhou Xue and Qian Chen
Pennsylvania State University and University of Nebraska LincolnScalable Graphical Event Models for Marketing Attribution
Wreetabrata Kar and Mohammad Rahman
Purdue UniversityAn Advertiser’s Dilemma with Marketing Interventions: At Scale Heterogenous Treatment Effects to Rescue
Ambuj Tewaria
University of MichiganCausality in Decision Making: Bandits and Beyond
