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Top row, left to right, Chitta Baral, Muhao Chen, Jia Deng; second row, left to right, Elena Glassman, Carlos Guestrin, Tatsunori Hashimoto; and bottom row, left to right, Jonathan Kummerfeld, Ali Mesbah, Maarten Sap
The nine recipients of the 2023 spring ARA are, top row, left to right, Chitta Baral, Muhao Chen, and Jia Deng; second row, left to right, Elena Glassman, Carlos Guestrin, and Tatsunori Hashimoto; and bottom row, left to right, Jonathan Kummerfeld, Ali Mesbah, and Maarten Sap.

Amazon Research Awards recipients announced

Awardees have access to Amazon public datasets, along with AWS AI/ML services and tools to perform cutting-edge research in generative AI.

By Amazon Research Awards team
September 07, 2023
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Since its inception in 2015, Amazon Research Awards has issued calls for proposals in topics ranging from fairness in AI to robotics to natural-language processing. However, the spring 2023 call for proposal for Amazon Web Services AI: Generative AI offered a new focus area for this program, reflecting Amazon’s ongoing efforts to collaborate with researchers in this important area of research.

When announcing the CFP, Arash Nourian, AWS general manager, Machine Learning Engines, noted, "Generative AI has a great potential to revolutionize the way we interact with the world around us. However, it presents a number of challenges that should be addressed in order to realize its full potential, such as responsible use of these systems. At AWS, we think it is important to support the research community in addressing these challenges that could have a direct technological and societal impact."

The response was impressive, resulting in the highest number of submissions for a single ARA CFP topic since the program’s inception. Today, ARA is publicly announcing nine award recipients who represent eight universities in three countries. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society.

In addition, given the strong response and importance of the research, ARA plans to continue investing in generative AI in future CFPs.

“Amazon is thrilled to collaborate with academia to explore the frontiers of generative AI and advance its capabilities, usefulness, usability, and safe and responsible behavior towards broad societal adoption and transformative impact,” said Sudipta Sengupta, vice president and distinguished scientist in AWS Database & AI Leadership. “Our program aims to mitigate compute infrastructure cost and scale barriers for academia to participate in generative-AI research with a spectrum of timescales and outcomes.”

Recipients have access to more than 300 Amazon public datasets and can utilize AWS AI/ML services and tools through AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.

Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.

ARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.

The table below lists, in alphabetical order, the spring 2023 cycle call-for-proposal recipients.

RecipientUniversityResearch title
Chitta BaralArizona State UniversityEnsuring logical robustness in generative natural-language systems
Muhao ChenUniversity of California, DavisRobust (controlled) natural-language generation with structure‐aware equivariance learning
Jia DengPrinceton UniversityLanguage-guided procedural generation of 3D scenes
Elena GlassmanHarvard UniversityMaking sense of language model outputs for end-user tasks
Carlos GuestrinStanford UniversityAlpaca farm: an open framework for language-model-safety research and development
Tatsunori HashimotoStanford UniversityAlpaca farm: an open framework for language-model-safety research and development
Jonathan KummerfeldUniversity Of SydneyMaking sense of language model outputs for end-user tasks
Ali MesbahUniversity Of British ColumbiaReducing hallucinations: contextual strategies for code generation in large language models
Maarten SapCarnegie Mellon UniversityRLKF: mitigating factual hallucinations and social biases with knowledge-based reinforcement learning
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