Runtime optimizations and a thorough evaluation process driven by agentic AI help ensure that models written in PyTorch will execute efficiently and reliably on Trainium systems.
Expanding the mix of synthetic data used to train a tabular foundation model enables it to adapt to new tables without new models and lead the single-model field on the TabArena benchmark.
Using the Neuron Kernel Interface, a Reactor–AWS collaboration tackled the dynamic shapes, memory access patterns, and cache management that make real-time autoregressive diffusion hard—building techniques that generalize across models.
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.
Customer-obsessed science
Research areas
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November 11, 20265 min readStatic analysis of deployed networks helps reduce the risk that sensitive data leaks across partitions, supporting the security and reliability of global-scale networking. -
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October 20, 20263 min read
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The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.
Submissions open October 1 and close on November 11, 2026
The collaboration will support research in robotics, industrial and systems engineering, and aerospace technology while fostering innovation in foundational and emerging technologies.
The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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ICON 20212021Building machine learning models for low resource languages is extremely challenging due to the lack of available training data (either un-annotated or annotated). To support such scenarios, zero-shot cross lingual transfer is used where the machine learning model is trained on a resource rich language and is directly tested on the resource poor language. In this paper, we present a technique which improves
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Physical Review X2021The estimation of multiple parameters in quantum metrology is important for a vast array of applications in quantum information processing. However, the unattainability of fundamental precision bounds for incompatible observables greatly diminishes the applicability of estimation theory in many practical implementations. The Holevo Cramér-Rao bound (HCRB) provides the most fundamental, simultaneously attainable
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NeurIPS 2021 Workshop on I (Still) Can't Believe It's Not Better2021Pre-trained language models (PLMs) such as BERT and GPT learn general text representations and encode extensive world knowledge; thus, they can efficiently and accurately adapt to various downstream tasks. In this work, we propose to leverage these powerful PLMs as recommender systems and use prompts to reformulate the session-based recommendation task to a multi-token cloze task. We evaluate the proposed
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KDD 2021 Workshop on Data-Efficient Machine Learning2021Query rewriting (QR) is an increasingly important technique for reducing user friction in a conversational AI system. User friction is caused by various reasons, including errors in automatic speech recognition (ASR), natural language understanding (NLU), entity resolution (ER) component, or users’ slip of the tongue. In this work, we propose a search-based self-learning QR framework: User Feedback Search
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CLEO 20212021Decoherence and noise from optical absorption in superconducting circuits hinder development of microwave to optical quantum transducers. Addressing these issues, we fabricate niobium-based resonators and qubits, and study them under laser illumination at milliKelvin temperatures.
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
