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.
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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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EMNLP 2019 Workshop on TextGraphs2019Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graph based semi-supervised learning models as well as their inductive variants for NLU. We evaluate
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CCS 2019 12th ACM Workshop on Artificial Intelligence and Security2019Many forms of interaction between computer systems and users are recorded in the form of event records, such as login events, API call records, bank transaction records, etc. These records are often comprised of high-dimensional categorical variables, such as user name, zip code, autonomous system number, etc. In this work, we consider anomaly detection for such data sets, where each record consists of
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EMNLP 20192019The need for high-quality, large-scale, goal-oriented dialogue datasets continues to grow as virtual assistants become increasingly widespread. However, existing publicly available datasets useful for this area are limited either in their size, linguistic diversity, domain coverage, or annotation granularity. We introduce the MultiDoGO dataset to overcome these limitations. With a total of over 65,000 dialogues
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EMNLP 2019 Workshop on Machine Reading for Question Answering2019Although advances in neural architectures for NLP problems as well as unsupervised pretraining have led to substantial improvements on question answering and natural language inference, understanding of and reasoning over long texts still poses a substantial challenge. Here, we consider the task of question answering from full narratives (e.g., books or movie scripts), or their summaries, tackling the NarrativeQA
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IDCM 20192019How can we early warn against an impending student drop out or an adverse health condition in near real-time? How can we leverage recent interventions such as tutoring or medicines to early warn more accurately? More challengingly, how do we learn to early warn from data that is peppered with such interventions? Early warnings are pivotal for avoiding long-term problems in healthcare, education, mechanical
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.
