Amazon Scholar Aravind Srinivasan coauthored a 2014 paper about forecasting civil unrest in Latin America, which won a test-of-time award at KDD 2025.
The pandemic turbo-charged retail growth — teams of scientists at Amazon forged a path forward to handle the scale.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
Every NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football into a sport where every movement is measured, modeled, and instantly analyzed.
"Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.
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January 13, 20267 min readLeveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets. -
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December 29, 20256 min read
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
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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NeurIPS 2025 Workshop on Continual and Compatible Foundation Model Updates2025Command-lines are a common attack surface in cybersecurity. Yet they often contain sensitive user information, creating a dual challenge: systems must detect suspicious commands accurately while protecting user privacy. Existing approaches typically tackle one challenge without the other. To address this gap, we present PASTRAL, a practical framework for privacy-preserving detection of suspicious command-lines
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AES Show 20252025Surround sound systems commonly distribute loudspeakers along standardized layouts for multichannel audio reproduction. However in less controlled environments, practical layouts vary in loudspeaker quantity, placement, and listening locations / areas. Deviations from standard layouts introduce sound-field errors that degrade acoustic timbre, imaging, and clarity of audio content reproduction. This work
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IEEE Big Data 20252025Enterprise relational databases increasingly contain vast amounts of non-semantic data—IP addresses, product identifiers, encoded keys, and timestamps—that challenge traditional semantic analysis. This paper introduces a novel Character-Level Autoencoder (CAE) approach that automatically identifies and groups semantically identical columns in nonsemantic relational datasets by detecting column similarities
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IJCNLP-AACL 20252025In recent years, dense retrieval has been the focus of information retrieval (IR) research. While effective, dense retrieval produces uninterpretable dense vectors, and suffers from the drawback of large index size. Learned sparse retrieval (LSR) has emerged as promising alternative, achieving competitive retrieval performance while also being able to leverage the classical inverted index data structure
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2025Previous AutoML systems have made progress in automating machine learning workflows, but still require significant manual setup and expert knowledge. This paper presents a novel multi-agent system that integrates Large Language Models (LLMs) with external knowledge bases of existing machine learning tools to automate the complete end-to-end solution. To address the limitations of pure LLM solutions, including
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A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
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