{"slug": "34-amazon-research-awards-build-on-trainium-recipients-announced", "title": "34 Amazon Research Awards Build on Trainium recipients announced", "summary": "Amazon Web Services (AWS) announced 34 new recipients of its Build on Trainium research awards, a $110 million credit program supporting AI research on its Trainium chips. The Fall 2025 cycle focused on Responsible AI, covering topics such as AI safety, multilingual models, and synthetic data generation. Recipients, including teams at the University of Illinois Urbana-Champaign and the University of Washington, gain access to over 700 public datasets, AWS Promotional Credits, and dedicated support from Amazon research contacts.", "body_md": "[ Build on Trainium](https://aws.amazon.com/ai/machine-learning/trainium/research/) is a $110 million credit program focused on AI research and university education aimed to support the next generation of innovation and development on\n\n[. The program provides compute credits to novel AI research on Trainium, investing in leading academic teams to build innovations in critical areas including new model architectures, ML libraries, optimizations, large-scale distributed systems, and more.](https://aws.amazon.com/machine-learning/trainium/)\n\n__AWS Trainium__This announcement includes awards funded under the [ Fall 2025 Build on Trainium: Responsible AI](https://www.amazon.science/research-awards/build-on-trainium-responsible-ai-call-for-proposals-fall-2025) call for proposals. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. This cycle’s focus on Responsible AI invited proposals addressing 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. The recipients have access to more than 700\n\n[and can utilize AWS AI/ML services and tools through their AWS Promotional Credits, are assigned an Amazon research contact who offers consultation and advice, and benefit from AWS Trainium resources, such as tutorials and hands-on sessions.](https://aws.amazon.com/opendata/?wwps-cards.sort-by=item.additionalFields.sortDate&wwps-cards.sort-order=desc)\n\n__Amazon public datasets__Build on Trainium represents AWS's commitment to democratizing AI research through collaborative partnership with academia.\n\n\"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. \"By leveraging the support from Build on Trainium, 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. At the University of Washington, researchers are developing an inference-optimization framework that raises token efficiency for everyone building on Trainium, with the goal to deliver portable, high-performance LLM inference on Trainium.\"\n\n|\n|\n|\n|---|---|---|\nThe University of Sydney |\nFACTOR: Federated Adversarial Co-Training with Textual Gradient for LLM Security and Robustness |\n|\nGeorgia Institute of Technology |\nLeveraging Public-Private Mixtures For Differentially Private Synthetic Data Generation |\n|\nThe University of Queensland |\nResponsible AI on Trainium: Scalable Detection and Mitigation of Evasive Multimodal Scam Content |\n|\nCollege of William and Mary |\nDELA: Editable Diffusion Language Models |\n|\nUniversity of North Carolina at Chapel Hill |\nAlgorithm-System Co-Design for Efficient Sparse and Quantized LLMs |\n|\nUniversity of California Los Angeles |\nMANSA: Democratizing Voice AI with Efficient Multimodal Foundation Models |\n|\nUniversity of California Santa Barbara |\nLeveraging Public-Private Mixtures For Differentially Private Synthetic Data Generation |\n|\nUniversity of California San Diego |\nGoverning Social Bias in AI Image Generation through Value Manifests |\n|\nImperial College London |\nTowards Deterministic Model Inference |\n|\nThe Ohio State University |\nNatively Multimodal and Multilingual Speech-Text Large Language Models |\n|\nInstitute for Decentralized AI (ADAI) |\nSafe, Social Pre-training of LLM Agents |\n|\nThe University of British Columbia |\nMemorization-Aware Preference Optimization for Machine Unlearning |\n|\nNew Jersey Institute of Technology |\nEfficient and Adaptable Language Models via Sub-Model Search |\n|\nUniversity of California San Diego |\nAlgorithm-System Co-Design for Efficient Sparse and Quantized LLMs |\n|\nThe Chinese University of Hong Kong |\nM3-Align: Scalable Multilevel & Multiobjective Alignment for Multilingual Language Models |\n|\nUCL - University College London |\nBreaking the Multilingual Data Wall: Scaling Synthetic Data for Low-Resource Language Model Pretraining |\n|\nUniversity of Illinois at Urbana-Champaign |\nCratos: Certified Robustness for Quantization and Pruning-Aware Training and Tuning of Vision Language Models |\n|\nTinoosh Mohsenin |\nJohns Hopkins University |\nTRIM-LLM: From Quadratic to Linear Attention and Structured Pruning for Carbon and Cost-Efficient LLM Deployment on Trainium |\nGeorge Mason University |\nLeveraging AWS Trainium for Verifiable AI and ML-Assisted Mathematical Reasoning |\n|\nFrank Rudzicz |\nDalhousie University |\nRepresentation Immunization on Trainium: Scalable Noising & Weight-Locking |\nAnuj Sharma |\nIowa State University |\nBuild on Trainium: Physics-Grounded Synthetic Crash Generation for Vulnerable Road Users with Representation Engineering on Video Diffusion and VLMs |\nShen Shen |\nMassachusetts Institute of Technology |\nAgent Tool-Use Safety Benchmarking with MCP-Specific LoRA Mitigations |\nRyan Shi |\nUniversity of Pittsburgh |\nBenchmarking and Improving Multilingual LLMs on Real Indic Language Healthcare Dialogues |\nNaichen Shi |\nNorthwestern University |\nLLM Hallucination Detection and Mitigation |\nJaideep Srivastava |\nUniversity of Minnesota Twin Cities |\nKnowledge-Infused Time-Series Pretraining with Safety-by-Knowledge-Checking for Trustworthy Clinical AI |\nCheng Tan |\nNortheastern University |\nTowards Reliable and Trustworthy LLM Services with ϵ-correctness |\nYue Wang |\nUniversity of Central Florida |\nGame-Theoretic Frameworks for Responsible AI on Pluralistic Alignment |\nYang Wang |\nUniversity of Illinois at Urbana-Champaign |\nSafeguarding Youths in Multimodal Generative AI: Toward a Trainium-Powered Framework for Safety and Alignment |\nErmin Wei |\nNorthwestern University |\nHigher Order Based Fast LLM Training Method |\nJun Wu |\nMichigan State University |\nBigger Models, Bigger Risks? Investigating the Safety Landscape of LLM Scaling |\nXiaokui Xiao |\nNational University of Singapore |\nTrainium-Accelerated, LLM-Guided Differentially Private Synthesis of Hierarchical Relational Data |\nMin Xu |\nCarnegie Mellon University |\nLanguage-Grounded Interpretability for ViT and 3D Models |\nZiyu Yao |\nGeorge Mason University |\nRepresentation Engineering of LLMs for Secure Code Generation |\nJunzhe Zhang |\nSyracuse University |\nDeconfounding Image Editing for Robust Causal Prediction |", "url": "https://wpnews.pro/news/34-amazon-research-awards-build-on-trainium-recipients-announced", "canonical_source": "https://www.amazon.science/research-awards/latest-news/34-amazon-research-awards-build-on-trainium-recipients-announced", "published_at": "2026-08-05 15:00:00+00:00", "updated_at": "2026-08-05 15:36:21.545464+00:00", "lang": "en", "topics": ["ai-research", "ai-infrastructure", "ai-chips", "ai-safety", "ai-policy"], "entities": ["Amazon Web Services (AWS)", "AWS Trainium", "Yida Wang", "University of Illinois Urbana-Champaign", "University of Washington", "Georgia Institute of Technology", "University of California San Diego", "Imperial College London"], "alternates": {"html": "https://wpnews.pro/news/34-amazon-research-awards-build-on-trainium-recipients-announced", "markdown": "https://wpnews.pro/news/34-amazon-research-awards-build-on-trainium-recipients-announced.md", "text": "https://wpnews.pro/news/34-amazon-research-awards-build-on-trainium-recipients-announced.txt", "jsonld": "https://wpnews.pro/news/34-amazon-research-awards-build-on-trainium-recipients-announced.jsonld"}}