Customer-obsessed science
Research areas
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August 21, 20269 min readExtendable framework enables testing agents on the full set of capabilities required to successfully complete a procedure, not isolated proxy tasks. -
July 30, 20268 min read -
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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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2025Unlearning has been proposed to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based methods, which can be categorized into gradient ascent-based (GA-based) and suppression-based methods. However, they often degrade model utility (the ability to respond to normal prompts). In this work, we aim to develop a general framework
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ICCV 2025 Workshop on Computer Vision in Advertising and Marketing2025Financial institutions continue to process millions of handwritten forms despite digital transformation efforts, creating a significant operational bottleneck. This research addresses the persistent challenge of automating handwritten data extraction from financial documents by introducing a four-stage processing pipeline that significantly out-performs existing solutions. Our approach sequentially combines
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Machine Learning for Healthcare 20252025Large language models demonstrate impressive performance on standardized healthcare benchmarks, yet their deployment readiness for real-world environments remains poorly understood. Current medical benchmarks present idealized scenarios that misrepresent the complexity of actual clinical data. We systematically evaluate LLM robustness by introducing clinician-validated perturbations to MedQA that mirror
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Winter Simulation Conference 20252025The integration of Computer-Aided Design (CAD) models into discrete event simulation software is a critical requirement for many simulation projects, particularly those involving the movement of people or vehicles where spatial accuracy directly impacts study outcomes. While importing CAD files and configuring simulation elements is essential for system accuracy, this process is typically time-consuming
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Transactions on Machine Learning Research2025While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs). Even highly accurate models can produce erratic revisions between FCDs, undermining trust and disrupting downstream decision-making. To improve the volatility of forecast revisions, state-of-the-art models like MQCNN, MQT, and SPADE
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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.
