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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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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CIKM 20262026Static pruning is widely used to accelerate sparse neural retrieval, yet existing studies each validate their conclusions within a single custom pipeline, leaving it unclear which findings transfer to modern engines with different index organizations and dynamic pruning mechanisms. We present the first cross-engine pruning portability study, evaluating static pruning strategies across three engines—a controlled
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ACM CODI-CRAC 20262026In this work, we propose a method for dialog simulation to gather high-quality open-domain, multi-turn question answering conversations . The simulation is grounded on Stack Exchange posts and motivated by computational discourse theory. We first convert forum posts into structured directed graphs; then, different traversals through the graph represent possible conversational trajectories. Our proposed
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IMAGE 20262026Raw seismic data consists of complex signals captured by specialized instruments that geophysicists must process through carefully sequenced filters to extract meaningful geological information. When designing these processing workflows, experts frequently encounter technical challenges ranging from incorrect configurations to problematic information flow patterns. Traditionally, these errors remain undetected
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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.
