Customer-obsessed science
Research areas
-
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 -
-
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.
-
IROS 20262026Long-horizon motion forecasting for multiple autonomous robots is challenging due to nonlinear agent interactions, compounding prediction errors, and continuous-time evolution of dynamics. Learned dynamics of such a system can be useful in various applications such as travel time prediction, prediction-guided planning and generative simulation of warehouse robots. In this work, we aim to develop an efficient
-
ICML 2026 Workshop on Foundation Models for Structured Data2026Tabular foundation models like TabPFN and TabICL achieve state-of-the-art performance through in-context learning, yet their architectures remain fundamentally opaque. We introduce KernelICL, a framework to enhance tabular foundation models with quantifiable sample-based inspectability. Building on the insight that in-context learning is akin to kernel regression, we make this mechanism explicit by replacing
-
2026Large language model (LLM) agents increasingly operate in streaming case-based reasoning (CBR) settings, where continuous improvement from past experience is crucial. Existing methods achieve this by storing past cases and retrieving similar ones as few-shot examples. This strategy fails near decision boundaries, where highly similar cases have conflicting outcomes and the discriminative factors are not
-
ICML 2026 Workshop on Deep Learning for Code (DL4C)2026Recent agentic approaches to LLM-based kernel generation have achieved strong results on CUDA, yet emerging AI accelerators such as AWS Trainium and Inferentia remain unaddressed. Writing kernels for these chips via the Neuron Kernel Interface (NKI) is particularly challenging due to a multi-engine architecture, tile-based programming with a fixed 128-element partition dimension, and explicit memory management
-
2026Large language models can memorize information that must be removed–ranging from copyright-sensitive content (e.g., book chapters) to personally identifiable information (e.g., income)–to ensure responsible and compliant behavior. Unlearning has emerged as an efficient alternative to full retraining, aiming to remove specific knowledge. However, users may still expect model to leverage the removed information
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
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.
