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.
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.
How the Verus "program verifier", which automatically checks code against a mathematical specification of its functionality, helps increase security assurance in software projects.
Ten years after we founded the Automated Reasoning Group, mathematical logic has moved from academic research into production services that secure millions of customer workloads — demonstrating that systems can be provably correct, not just probably correct.
Customer-obsessed science
Research areas
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October 23, 20266 min read“Controllable text to speech” makes speech more expressive, character voices more distinct and consistent. -
October 20, 20263 min read -
October 12, 202614 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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CAV 20212021Over the past ten years, the adoption of cloud services has grown rapidly, leading to the introduction of automated deployment tools to address the scale and complexity of the infrastructure companies and users deploy. Without the aid of automation, ensuring the security of an ever-increasing number of deployments becomes more and more challenging. To the best of our knowledge, no formal automated technique
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The Journal of Financial Data Science2021We present a machine learning pipeline for fairness-aware machine learning (FAML) in finance that encompasses metrics for fairness (and accuracy). Whereas accuracy metrics are well understood and the principal ones used frequently, there is no consensus as to which of several available measures for fairness should be used in a generic manner in the financial services industry. We explore these measures
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The Web Conference 2021 Workshop on Multilingual Search2021Learning cross-lingual word representations is an effective approach for developing multilingual models. In this work, we lay the groundwork and present preliminary results on learning cross-lingual representations appropriate for deployment to edge devices. Specifically, we learn cross-lingual representations using multilingual language models and use these to seed different parts of a Neural Natural Language
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PAKDD 20212021Learning from source code usually requires a large amount of labeled data. Despite the possible scarcity of labeled data, the trained model is highly task-specific and lacks transferability to different tasks. In this work, we present effective pre-training strategies on top of a novel graph-based code representation, to produce universal representations for code. Specifically, our graph-based representation
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Interspeech 20212021Spoken language understanding (SLU) smart assistants such as Amazon Alexa host hundreds of thousands of voice applications (skills) to delight end-users and fulfill their utterance requests. Sometimes utterances fail to be claimed by smart assistants due to system problems such as model incapability or routing errors. The failure may lead to customer frustration, dialog termination and eventually cause
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.
