Customer-obsessed science
Research areas
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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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IEEE 2023 Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)2023Classical speech coding uses low-complexity postfilters with zero lookahead to enhance the quality of coded speech, but their effectiveness is limited by their simplicity. Deep Neural Networks (DNNs) can be much more effective, but require high complexity and model size, or added delay. We propose a DNN model that generates classical filter kernels on a per-frame basis with a model of just 300 K parameters
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KDD 2023 Workshop on Mining and Learning with Graphs2023Learning compact representation from customer shopping behaviors is at the core of web-scale E-commerce recommender systems. At Amazon, we put great efforts into learning embedding of customer engagements in order to fuel multiple downstream tasks for better recommendation services. In this work, we define the notion of shopping trajectory that consists of customer interactions at the categorical level
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CVPR 20232023We introduce Á-la-carte Prompt Tuning (APT), a transformer-based scheme to tune prompts on distinct data so that they can be arbitrarily composed at inference time. The individual prompts can be trained in isolation, possibly on different devices, at different times, and on different distributions or domains. Furthermore each prompt only contains information about the subset of data it was exposed to during
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Winter Simulation Conference 20232023With modern warehouses becoming more automated, there is a growing opportunity to test and validate material handling concepts throughout the project life cycle. Emulation and digital twin pose a capability for material handling system validation from the ideation stage through post-implementation. An emulation model is a virtual replica of a physical system, and digital twin is a transformation of an emulation
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AAAI 2023 Workshop on Artificial Intelligence and Climate2023Rapid and continuous increase in greenhouse gas (GHG) emissions is warming our planet at unprecedented rates. Consumer products and services, including all aspects of the corresponding supply chain, contribute to more than 75% of these emissions. Attribution of GHG emissions to each product will drive awareness and change from individual consumers to large corporations that produce and own these products
Collaborations
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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.
