Top eight university teams move on to head-to-head finals focused on AI security for code generation.
In 2017, IEEE Internet Computing identified a single paper from its publication history that had best withstood the test of time: a 2003 paper called “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”
A new multimodal foundation model that unifies speech and text processing in a single architecture, delivering frontier voice intelligence and industry-leading price performance.
F4D Studios
How Amazon’s scientists developed a first-of-its-kind multi-echelon system for inventory buying and placement.
We present Amazon Nova Premier, our most capable multimodal foundation model and teacher for model distillation.
Customer-obsessed science
Research areas
-
June 25, 2025With large datasets, directly generating data ID codes from query embeddings is much more efficient than performing pairwise comparisons between queries and candidate responses.
Featured news
Top eight university teams move on to head-to-head finals focused on AI security for code generation.
David Chang/Getty Images/iStockphoto
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.
-
KDD 2025 Workshop on AI for Supply Chain2025This paper proposes using two metrics to quantify the forecastability of time series prior to model development: the spectral predictability score and the largest Lyapunov exponent. Unlike traditional model evaluation metrics, these measures assess the inherent forecastability characteristics of the data before any forecast attempts. The spectral predictability score evaluates the strength and regularity
-
KDD 2025 Workshop on AI for Supply Chain2025Multi-horizon and multi-lead time forecasting is a well-established area in machine learning, particularly in demand forecasting, which plays a critical role in a product’s lifecycle. Accurate forecasts support key operational functions, including inventory management, financial planning, promotion planning, and supply chain optimization. Traditional demand forecasting methods typically rely on learning
-
KDD 2025 Workshop on AI for Supply Chain2025Large Language Models (LLMs) have recently enabled natural language interfaces that translate user queries into executable SQL, offering a powerful solution for non-technical stakeholders to access structured data. However, one of the limitation that LLMs do not natively express uncertainty makes it difficult to assess the reliability of their generated queries. This paper presents a case study that evaluates
-
KDD 2025 Workshop on AI for Supply Chain2025Modern time series forecasting increasingly relies on complex ensemble models generated by AutoML systems like AutoGluon, delivering superior accuracy but with significant costs to transparency and interpretability. This paper introduces a comprehensive, dual-approach framework that addresses both the explainability and forecastability challenges in complex time series ensembles. First, we develop a surrogate-based
-
KDD 2025 Workshop on AI for Supply Chain2025We propose RSight, a new deep neural network model for product demand forecasting across multiple geographic regions. Our model employs a novel region-enhanced encoder to learn cross regional information. Using a dataset consisting of weekly sales for 15 million products from a large e-commerce company at the US Zip2 level, our method achieves substantial accuracy improvement over existing state-of-the-art
Academia
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.
