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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
July 9, 202610 min read -
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Featured news
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
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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LAK 20232023A/B testing at scale provides opportunities for learning analytics researchers to learn from large sample sizes. Deploying and running live intervention experiments with such large samples, however, raises infrastructural challenges. This paper discusses some of those challenges, and reports on two possible implementations that address those challenges in a workforce learning context at a large technology
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ICSE 20232023Manual code reviews and static code analyzers are the traditional mechanisms to verify if source code complies with coding policies. However, they are hard to scale. We formulate code compliance assessment as a machine learning (ML) problem, to take as input a natural language policy and code, and generate a prediction on the code’s compliance, non-compliance, or irrelevance. Our intention for ML-based
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ICSE 20232023Static application security testing (SAST) tools have found broad adoption in modern software development workflows. These tools employ a variety of static analysis rules to generate recommendations on how to improve the code of an application. Every recommendation consumes the time of the engineer that is investigating it, so it is important to measure how useful these rules are in the long term. But what
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AAAI 20232023Ensuring the overall end-user experience is a challenging task in arbitrary style transfer (AST) due to the subjective nature of style transfer quality. A good practice is to provide users many instead of one AST result. However, existing approaches require to run multiple AST models or inference a diversified AST (DAST) solution multiple times, and thus they are either slow in speed or limited in diversity
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AAAI 20232023Pretrained code language models have enabled great progress towards program synthesis. However, common approaches only consider in-file local context and thus miss information and constraints imposed by other parts of the codebase and its external dependencies. Existing code completion benchmarks also lack such context. To resolve these restrictions we curate a new dataset of permissively licensed Python
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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.
