Customer-obsessed science
Research areas
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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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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.
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IEEE UEMCON 20262026The proliferation of Software-as-a-Service (SaaS) applications has created significant data integration complexity. Connecting N SaaS sources (e.g., Salesforce) to M analytical targets (e.g., data lakes, data warehouses) traditionally requires N×M bespoke integrations. This paper presents the SaaS Data Replication Format (SDRF), a JSON-based Change Data Capture (CDC) envelope that decouples SaaS source
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RecSys 20262026Multi-agent collaborative filtering systems coordinate autonomous LLM-powered user and item agents through natural-language interaction to refine preferences and generate recommendations. These systems inherit vulnerabilities from both their data-driven nature and multi-agent interactions, which manifest in distinct ways. Understanding how connectivity modulates vulnerability in these systems could facilitate
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ACM SIGSPATIAL 20262026Geospatial analysis traditionally requires specialized GIS expertise, complex software interfaces, and significant manual effort to orchestrate data from heterogeneous sources. We present Fangorn, an agentic platform that enables analysts to perform sophisticated geospatial intelligence operations through natural language conversation. Fangorn combines a modular tool ecosystem based on the Model Context
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ACM SIGSPATIAL 2026, International Journal of Geographical Information Science2026We present a nonparametric method for multi-modal trajectory prediction that requires no GPU, fits in seconds on CPU, and matches or exceeds a 57M-parameter transformer. The method builds a transition table of historical state-to-next-position pairs and retrieves neighbors using a product kernel over spatial proximity, bearing, speed, and temporal context. Two inference modes operate over this shared representation
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As the capabilities of Generative AI (GenAI) models advance, teams must assess security & safety risks before deployment to prevent potential harm. Teams developing GenAI models face the dual challenge of improving both performance and security & safety through specialized testing protocols. The rapid pace of AI development means teams often lack proper security & safety evaluation tools or prioritize performance
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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.
