Customer-obsessed science
Research areas
-
July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
-
July 9, 202610 min read
-
Featured news
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.
-
2025As the scale of training large language models (LLMs) increases, one emergent failure is silent data corruption (SDC), where hardware produces incorrect computations without explicit failure signals. In this work, we are the first to investigate the impact of real-world SDCs on LLM training by comparing model training between healthy production nodes and unhealthy nodes exhibiting SDCs. With the help from
-
2025Condition-based monitoring (CBM) is essential for maintaining high machine uptime in industrial settings. While existing CBM solutions effectively use time-series data (e.g., thermal, vibration, amperage, etc.), these can be enhanced with LLMs to integrate domain knowledge and generate interpretable summaries. However, LLMs often incur higher latency and cost than traditional methods. We thus propose LEAD
-
NeurIPS 2025 Workshop on Mathematical Reasoning and AI2025We present an approach for training language models to interactively prove theorems using the Lean proof assistant. Our approach enables models to propose partial proofs, receive verification feedback, and iteratively refine their proofs. We develop a synthetic data generation pipeline that converts static proof datasets into multi-turn interactive sequences, complete with incremental verification feedback
-
NeurIPS 2025 AI4NextG2025This paper presents a pilot study toward a foundation model for wireless sensing using FMCW radar. We propose a transformer-based architecture trained on data from mmWave sensor with self-supervised objectives designed to capture temporal–spatial signal characteristics without labels. Our framework introduces strategies for handling sparse channel representations and provides a unified normalization across
-
2025Unlearning has been proposed to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based methods, which can be categorized into gradient ascent-based (GA-based) and suppression-based methods. However, they often degrade model utility (the ability to respond to normal prompts). In this work, we aim to develop a general framework
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.
