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 12, 202614 min read“Priming” reuses pretrained Transformer weights to build models with more efficient architectures at less than 1% of the original training cost and with throughputs up to twice as high. -
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August 21, 20269 min read
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July 30, 20268 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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FM 20232023Computational notebooks are widely used for machine learning (ML). However, notebooks raise new correctness concerns beyond those found in traditional programming environments. ML library APIs are easy to misuse, and the notebook execution model raises entirely new problems concerning reproducibility. It is common to use static analyses to detect bugs and enforce best practices in software applications.
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International Conference on Automatic Face and Gesture Recognition 20232023Synthesizing high-fidelity talking head videos of an arbitrary identity, lip-synced to a target speech segment, is a challenging problem. Recent GAN-based methods succeed by training a model on a large amount of videos, allowing the generator to learn a variety of audio-lip representations. However, they are unable to handle head pose changes. On the other hand, Neural Radiance Fields (NeRFs) model the
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VLDB 20232023Existing general purpose frameworks for gigantic model training, i.e., dense models with billions of parameters, cannot scale efficiently on cloud environment with various networking conditions due to large communication overheads. In this paper, we propose MiCS, which Minimizes the Communication Scale to bring down communication overhead. Specifically, by decreasing the number of participants in a communication
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WACV 2023 3rd Workshop on Real-World Surveillance: Applications and Challenges (RWS)2023Biodiversity loss and ecosystem degradation are global challenges demanding creative and scalable solutions. Recent increases in data collection coupled with machine learning have the potential to expand landscape monitoring capabilities. We present a computer vision solution to the problem of identifying invasive species. The Australian Tree Fern (Cyathea cooperi) is a fast growing species that is displacing
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WACV 2023 Workshop on Video/Audio Quality in Computer Vision2023In this paper, we propose a framework for learning feature representations for Image Quality Assessment (IQA) using contrastive learning. To account for the absence of large-scale IQA dataset, we pretrain an image encoder to cluster images based on the image quality using synthetically distorted versions of pristine unlabeled images. Images of similar quality are grouped closer in embedding space, while
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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.
