A new dataset with over 238,000 images challenges and advances the state of the art in visual defect detection for complex retail applications.
To transform scientific domains, foundation models will require physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques that overcome data scarcity while maintaining scientific rigor.
The language AI agents might speak, sharing context without compromising privacy, modeling agentic negotiations, and understanding users’ commonsense policies are some of the open scientific questions that researchers in agentic AI will need to grapple with.
With a novel parallel-computing architecture, a CAD-to-USD pipeline, and the use of OpenUSD as ground truth, a new simulator can explore hundreds of sensor configurations in the time it takes to test just a few physical setups.
From reimagining storage to serverless computing, Aurora continues to push the boundaries of what's possible in database technology.
Customer-obsessed science
Research areas
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September 2, 2025Audible's ML algorithms connect users directly to relevant titles, reducing the number of purchase steps for millions of daily users. -
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Featured news
Amazon Research Awards have opened their Fall call for proposals, which closes November 5, 2025.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
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Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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IEEE CDC 20252025Online advertising is typically implemented via real-time bidding, and advertising campaigns are then defined as extremely high-dimensional optimization problems. To solve these problems in light of large scale and significant uncertainties, the optimization problems are modularized in a manner that makes feedback control a critical component of the solution. The control problem, however, is challenging
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IEEE 2025 Workshop on Automatic Speech Recognition and Understanding2025Speech Recognition has seen a dramatic shift towards adopting Large Language Models (LLMs). This shift is partly driven by good scalability properties demonstrated by LLMs, ability to leverage large amounts of labelled, unlabelled speech and text data, streaming capabilities with autoregressive framework and multi-tasking with instruction following characteristics of LLMs. However, simple next-token prediction
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VLDB 20252025We propose OmniMatch, a novel joinability discovery technique, specifically tailored for the needs of data products: cohesive curated collections of tabular datasets. OmniMatch combines multiple column-pair similarity measures leveraging self-supervised Graph Neural Networks (GNNs). OmniMatch's GNN captures column relatedness by leveraging graph neighborhood information, significantly improving the recall
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2025 IEEE-RAS Humanoids2025Enabling robots to grasp objects specified through natural language is essential for effective human–robot interaction, yet it remains a significant challenge. Existing approaches often struggle with open–form language expressions and typically assume unambiguous target objects without duplicates. Moreover, they frequently rely on costly, dense pixel–wise annotations for both object grounding and grasp
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RecSys 2025 Workshop on Music Recommender Systems2025Personalization is a key requirement for most customer experiences in a music streaming service, such as landing page, station song sequencing, or search. A common approach is to develop dedicated personalization ML models, one for each experience, that directly integrate with all the personalization signals alongside experience-specific signals. However, this is not scalable as it is costly for each product
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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.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
