34 Amazon Research Awards Build on Trainium recipients announced

Amazon has announced 34 recipients of the Build on Trainium awards, drawn from researchers at 30 universities. The awards fall under the Fall 2025 Build on Trainium: Responsible AI call for proposals.

Build on Trainium is a $110 million credit program focused on AI research and university education intended to support development on AWS Trainium. It provides compute credits to academic teams working in areas including new model architectures, ML libraries, optimizations, and large-scale distributed systems. Proposals were reviewed for the quality of their scientific content and their potential to impact the research community and society.

The Responsible AI cycle invited proposals on five priority topics: AI safety and alignment, multi-lingual language models, representation engineering, sustainability and small language models, and deep learning models for synthetic data generation, all leveraging AWS Trainium infrastructure. Recipients gain access to more than 700 Amazon public datasets, AWS Promotional Credits for AI/ML services and tools, an assigned Amazon research contact for consultation, and Trainium resources such as tutorials and hands-on sessions.

"Build on Trainium gives the next wave of AI researchers powerful, scalable access to Amazon's purpose-built AI chips, so the only limit is their imagination, not their compute budget," said Yida Wang, AWS AI Principal Applied Scientist. Wang said University of Illinois Urbana-Champaign researchers are studying topology-aware parallelization strategies for large-scale mixture-of-experts models with as many as one trillion parameters on up to 1,024 Trainium chips, while University of Washington researchers are developing an inference-optimization framework to raise token efficiency, with the goal of portable, high-performance LLM inference on Trainium.

Recipients include Wei Bao (The University of Sydney), Viveck Cadambe (Georgia Institute of Technology), Yujun Cai (The University of Queensland), Haipeng Chen (College of William and Mary), Tianlong Chen (University of North Carolina at Chapel Hill), Saadia Gabriel (University of California Los Angeles), Haewon Jeong (University of California Santa Barbara), Haojian Jin (University of California San Diego), Marios Kogias (Imperial College London), Sachin Kumar (The Ohio State University), Emanuele La Malfa (Institute for Decentralized AI), Xiaoxiao Li (The University of British Columbia), Yingcong Li (New Jersey Institute of Technology), Zhijian Liu (University of California San Diego), Songtao Lu (The Chinese University of Hong Kong), Yao Lu (UCL), Sasa Misailovic (University of Illinois at Urbana-Champaign), Tinoosh Mohsenin (Johns Hopkins University), ThanhVu Nguyen (George Mason University), Frank Rudzicz (Dalhousie University), Anuj Sharma (Iowa State University), Shen Shen (Massachusetts Institute of Technology), Ryan Shi (University of Pittsburgh), Naichen Shi (Northwestern University), Jaideep Srivastava (University of Minnesota Twin Cities), Cheng Tan (Northeastern University), Yue Wang (University of Central Florida), Yang Wang (University of Illinois at Urbana-Champaign), Ermin Wei (Northwestern University), Jun Wu (Michigan State University), Xiaokui Xiao (National University of Singapore), Min Xu (Carnegie Mellon University), Ziyu Yao (George Mason University), and Junzhe Zhang (Syracuse University).