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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IROS 20212021Autonomous mobile robots need maps for effective, safe navigation, and SLAM in general is still an unsolved problem. Nonetheless, certain combinations of environmental characteristics and sensors admit tractable solutions. In particular, detection and tracking of linear features such as line segments (2D) or planar facets (3D) has been proven robust in many man-made environments. However, these types of
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IEEE Access2021This paper presents a novel model family that we call SUPERVEGAN, for the problem of video enhancement for low bitrate streams by simultaneous video super resolution and removal of compression artifacts from low bitrates (e.g. 250Kbps). Our strategy is fully end-to-end, but we upsample and tackle the problem in two main stages. The first stage deals with removal of streaming compression artifacts and performs
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KDD 2021 International Workshop on Industrial Recommendation Systems2021Intelligent personal assistants (IPA) enable voice applications that facilitate people’s daily tasks. However, due to the complexity and ambiguity of voice requests, some requests may not be handled properly by the standard natural language understanding (NLU) component. In such cases, a simple reply like “Sorry, I don’t know” hurts the user’s experience and limits the functionality of IPA. In this paper
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KDD 2021 Workshop on Pretraining: Algorithms, Architectures, and Applications2021Few-shot learning techniques rely on generalizations of a base model trained on a large training set in order to transfer learn to more specialized tasks. Such techniques extend unsupervised learning models by allowing for fine-tuning a general model to a domain of interest using a relatively low number of training samples. This is especially important for applications where data may be non-homogeneous
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EMBC 20212021In this work, we propose to use a deep learning framework for decoding the electroencephalogram (EEG) signals of human brain activities. More specifically, we learn an end-to-end model that recognizes natural images or motor imagery by the EEG data that is collected from the corresponding human neural activities. In order to capture the temporal information encoded in the long EEG sequences, we first employ
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.
