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 1, 202610 min readAugmenting a network graph with agentic AI produces a “digital twin” that can help isolate network failures. -
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August 21, 20269 min read
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July 30, 20268 min read
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July 29, 20266 min read
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The collaboration will support research in robotics, industrial and systems engineering, and aerospace technology while fostering innovation in foundational and emerging technologies.
Submissions open October 1 and close on November 4, 2026
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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ECML-PKDD 20212021Amazon Last Mile strives to learn an accurate delivery point for each address by using the noisy GPS locations reported from past deliveries. Centroids and other center-finding methods do not serve well, because the noise is consistently biased. The problem calls for supervised machine learning, but how? We addressed it with a novel adaptation of learning to rank from the information retrieval domain. This
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KDD 2021 WIT Workshop on Deriving Insights from User-Generated Text2021Many eCommerce catalogs rely on structured product data to provide a good experience for customers. For large scale services, product information is provided by millions of different manufacturer and vendor schemas. Due to inherent heterogeneity of this data, unifying it to a consistent catalog schema remains a challenge. Schema matching is the problem of finding such correspondences between concepts in
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ICML 20212021In large-scale time series forecasting, one often encounters the situation where the temporal patterns of time series, while drifting over time, differ from one another in the same dataset. In this paper, we provably show under such heterogeneity, training a forecasting model with commonly used stochastic optimizers (e.g. SGD) potentially suffers large variance on gradient estimation, and thus incurs long-time
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ESEC/FSE 20212021Integrating static analyses into continuous integration (CI) or continuous delivery (CD) has become the best practice for assuring code quality and security. Static Application Security Testing (SAST) tools fit well into CI/CD, because CI/CD allows time for deep static analyses on large code bases and prevents vulnerabilities in the early stages of the development lifecycle. In CI/CD, the SAST tools usually
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EACL 20212021A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated
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.
