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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, 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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2026Despite remarkable progress in text-guided image editing, generative models frequently fail to preserve visual object consistency, defined as the preservation of a subject’s key attributes throughout the editing process. We address this limitation through three contributions. First, we introduce ABO-Edit, a dataset specifically designed to study object consistency, comprising over 12,000 triplets of source
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KDD 2026 Workshop on Machine Learning in Finance2026Financial transaction time series are strongly shaped by recurring external events such as holidays, promotional campaigns, and settlement cycles. In practice, however, anomaly detection systems often evaluate these series without explicitly modeling event context, leading to excessive false positives during predictable event-driven fluctuations. We propose a context-aware framework based on impact-driven
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ASE 20262026Complex itemized bills from services such as cloud computing, healthcare, or telecommunications can be difficult to understand because the pricing terms that determine what a customer pays are straightforward, but the eligibility rules that determine whether a given line item, a single charge for a product and quantity, qualifies for a given price are scattered across user guides, service terms, and fine
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2026Visual grounding aims to associate free-form textual queries with specific regions in an image. While recent Multimodal Large Language Models (MLLMs) have demonstrated promising capabilities in this domain, they primarily excel at object-level grounding and often struggle with part-level grounding—an essential requirement for fine-grained tasks such as robotic manipulation. In this work, we introduce a
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KDD 2026 Workshop on Two-sided Marketplace Optimization2026Optimizing online pricing strategy for Amazon Device dependent products is a uniquely challenging topic in dynamic pricing of two-sided marketplaces when balancing the supply side economics and demand responsiveness. To address this challenge, we propose a scalable framework that integrates a hierarchical segmentation model with a sequential learning layer for high-velocity event (HVE) adjustment, to simulate
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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.
