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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KDD 2021 Workshop on Data-Efficient Machine Learning2021Virtual assistants enable users to interact with a large number of services in natural language. Third-party developers building new applications for virtual assistants often have limited annotation resources and find it challenging to procure large amounts of suitable training data, opting instead for limited collections of sample utterance templates, annotated with their semantics. We can enrich such
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Interspeech 2021 Workshop on Speech Synthesis (SSW11)2021We describe a heterophone homograph (simply ’homograph’ henceforth) disambiguation system based on per-case classifiers, trained on a small amount of labelled data. These classifiers use contextual word embeddings as input features and achieve state-of-the-art accuracy of 0.991 on the English homographs on a publicly available dataset, without any additional rule system being necessary. We show that as
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Interspeech 2021 Workshop on Speech Synthesis (SSW11)2021We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to synthesise speech with an improved coarse and fine-grained prosody. We present a generic multi-scale spectrogram prediction mechanism where the system first predicts coarser scale mel-spectrograms that capture the suprasegmental information in speech, and later uses these coarser scale mel-spectrograms to predict finer scale mel-spectrograms
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NAACL 2021 TrustNLP Workshop on Trustworthy Natural Language Processing2021Many existing approaches for interpreting text classification models focus on providing importance scores for parts of the input text, such as words, but without a way to test or improve the interpretation method itself. This has the effect of compounding the problem of understanding or building trust in the model, with the interpretation method itself adding to the opacity of the model. Further, importance
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ICLR 2021 Workshop on Robust and Reliable Machine Learning in the Real World2021Goal oriented dialogue systems in real-word environments often encounter noisy data. In this work, we investigate how robust these systems are to noisy data. Specifically, our analysis considers intent classification (IC) and slot labeling (SL) models that form the basis of most dialogue systems. We collect a test-suite for six common phenomena found in live human-to-bot conversations (abbreviations, casing
Collaborations
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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.
