Customer-obsessed science
Research areas
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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. -
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July 9, 202610 min read
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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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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RecSys 20262026We report a practical lesson from building a GPU-free explainable-recommendation serving stack: explanations are pre-generated offline into a per-item candidate pool, and a small CPU-resident model selects one at request time. Every candidate in the pool of size K carries an offline BERTScore-F1 label against a reference explanation, so we compare a pairwise learning-to-rank model (LightGBM LambdaRank)
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RecSys 20262026One of the most challenging problems entertainment live-streaming services face in recommendation systems is that user behaviors are sparse and delayed, and interaction data exhibits bias for different user segments. Unlike e-commerce applications where user actions follow linear sequences, live-streaming viewers engage in multiple concurrent behaviors of watching, chatting, following, and spending, each
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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.
