Adobe Research Grants

Adobe Research Grants is sponsored by Adobe. Supports academic research in computer science and design, focusing on AI-driven creative tools and human-computer interaction.

Adobe Research » Data Science Research Awards 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 .

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? Title, proposer name and contact information, research goals, description of project(s), use of funds *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 August 2023 Award Winners Himabindu Lakkaraju and Chirag Agarwal Counterfactual LLM Policies for Personalized Content Recommendation Jiasi Chen and Samet Oymak Cheaper and Query-aware Inference for Language Models: Optimal LM Cascades by Modeling Future Rewards Personalized Recommendation via Prompting Generative Models: Multimodal User/Content Input Augmentation Xiaojing Dong and Steven Wu Santa Clara University and Carnegie Mellon University A New Approach to Build Individual Level Model with Walled Gardens Data February 2023 Award Winners University of California, Los Angeles Learning Prompts for Deep Generative Models via Multimodal Constraints and Reinforcement Learning Interactive Explanatory Analyses for Big Data Business KPIs Learning A Domain-specific Knowledge Graph for Persuasive Marketing Communications University of California, San Diego Open-Vocabulary Perception by Unifying Text-Driven Synthesis and Grounding August 2022 Award Winners Adel Javanmard and Gourab Mukherjee University of Southern California Statistical Joint Modeling for Integrated Marketing Flows: Personalized Promotions Optimized Over Journey and Lifespan Memory-Augmented Approaches for Long Document Understanding and Summarization Measuring Campaign Effectiveness by Disentangling Observational Marketing Data Peng Zhang and Guanyang Wang Improving the Design of Randomized Controlled Trials via Discrepancy Theory February 2022 Award Winners Ashwin Pananjady & Vidya Muthukumar Georgia Institute of Technology Building models for customer intelligence from evolving behavior Causal Discovery for Root Cause Analysis University of Texas at Dallas Subset Selection for Compute-Efficient Language Model Pretraining University of Wisconsin-Madison 3D Controllable, Aesthetically Pleasing Visual Content Creation August 2021 Award Winners University of California, Los Angeles Robust and Scalable Multimodal Reasoning With Unpaired Data Jasjeet Sekhon and Colleen Chan Statistical Surrogacy for Heterogenous Treatment Effect Estimation and Treatment Decision Rules University of Illinois at Urbana-Champaign Towards Reliable Validation and Evaluation in Offline Reinforcement Learning Multimodal Knowledge-infused Learning for Context-aware Consumer-Content Mapping February 2021 Award Winners Heterogeneous treatment effect estimation with guarantees Prof. Anshumali Shrivastava Olin College of Engineering Theory and Practice of Efficient Graph-Based Near-Neighbor at Scale Learning Behavioral Policies over Networks of Interacting Agents Massachusetts Institute of Technology Time-Aware Casual Embedding for Personalized Digital Marketing August 2020 Award Winners University of Southern California Learn your customer novel statistical methods for segmenting online users and their behaviors 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 An Advertiser’s Dilemma with Marketing Interventions: At Scale Heterogenous Treatment Effects to Rescue February 2020 Award Winners Causality in Decision Making: Bandits and Beyond Towards A/B Testing Automation: a Multi-armed Bandit Framework University of Massachusetts Amherst Preventing the Exploitation of Cognitive Biases Context-Aware Customer Journey Optimization in Spatiotemporal Environment August 2019 Award Winners Carnegie Mellon University Online experimentation with quantiles Carnegie Mellon University Entity resolution and user de-duplication of recurring fraudsters and free-loaders PICANTE: Deep Representation Learning for Periodic User Clustering University of Southern California Empowering Interpretable Recommendation with User Knowledge Graphs University of California, Santa Barbara From Off-Policy Evaluation to Reinforcement Learning with Low-Switching Cost