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
Hosted by Danielle Perszyk, cognitive scientist at Amazon, 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.
-
ACM 2026 HotCarbon Workshop on Sustainable Computer Systems2026Allocating the one-time carbon cost of training a large AI model across the inference requests it serves is an open methodological problem with no standardized solution. The choices made in boundary definition, functional unit selection, and lifetime forecasting can alter reported per-request emissions by an order of magnitude, undermining any comparison across models. We decompose this problem into three
-
ACM SIGKDD MiLeTs 20262026Multivariate time series contain two kinds of cross-variable relationships: persistent ones that reflect underlying structure (geographic proximity, shared infrastructure, physical coupling) and dynamic ones that arise from transient conditions in each observation window. Current transformer architectures conflate the two—channel-independent models ignore cross-variable relationships entirely, while cross-variable
-
AutoML Conference 20262026Bayesian hyperparameter optimization typically requires fitting a surrogate model to each new task, incurring per-task training cost that grows with the number of observations and limits deployment flexibility. We show that TabPFN v2 (Hollmann et al., 2025), a pretrained tabular foundation model never trained on Bayesian optimization data, can serve as a drop-in zero-shot BO surrogate, eliminating the per-task
-
ICCCN 20262026The Internet consists of interconnected, independently managed Autonomous Systems (AS) that rely on the Border Gateway Protocol (BGP) for inter-domain routing. BGP anomalies—such as route leaks and hijacks—can divert traffic through unauthorized or inefficient paths, jeopardizing network reliability and security. Although existing rule-based and machine learning methods can detect these anomalies using
-
2026A/B testing remains the standard for rolling out new features in the technology industry. Each experiment, however, consumes real traffic, engineering effort, and weeks of wall-clock time. Can AI agents—conditioned on behavioral profiles and contextual descriptions of the intervention—simulate outcomes accurately enough to vet candidate treatments before committing live traffic? We formalize this question
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.
