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Artificial intelligence

Applied AI

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Displaying 1 - 25 of 53

Photopolymer Additive Manufacturing 2025 Workshop Report: Building a Unified Vision from Research to Regulation

June 10, 2026
Author(s)
Callie Higgins, Jason Killgore, Mike Idacavage, Vince Anewenter, Mickey Fortune, Gary Cohen, Perri Katzman, Jessica Hemond, Spencer Loveless, Michael Gould
The third biannual Photopolymer Additive Manufacturing Alliance Workshop was held on September 15-16, 2025, at the University of Colorado Boulder to continue its mission of advancing photopolymer additive manufacturing (PAM). Building on the 2023 PAMA

GROQ-seq Datasets Across Transcription Factors (LacI, RamR, VanR), T7 RNA Polymerase and TEV Protease

April 19, 2026
Author(s)
Aviv Spinner, Shwetha Sreenivasan, James McLellan, Svetlana Ikonomova, Dana Cortade, Simon d'Oelsnitz, Kristen Sheldon, Olga Vasilyeva, Nina Alperovich, Anjali Chadha, Lily Nematollahi, Andi Dhroso, Zach Sisson, Corey Hudson, Erika DeBenedictis, Peter Kelly, Amanda Reider Apel, David Ross, Catherine Baranowski
Predicting any protein's function from its sequence alone would be a significant breakthrough in molecular biology. Although machine learning approaches have sought to tackle this, their limited generalizability reflects the absence of sufficiently large

Causal Machine Learning: An Empirical Approach to Supply Chain Management

January 9, 2026
Author(s)
Alfredo Roa-Henriquez, Juan Fung, Ruhaimatu Abudu, Jennifer Helgeson, Douglas Thomas
Over the past two decades, artificial intelligence (AI) has revolutionized industries, with machine learning (ML) at its core. While ML has enhanced supply chain management (SCM) in efficiency and resilience, it often relies on correlations, risking

Database of Diffusion MRI Brain Scans at 64 mT and 3 T

January 8, 2026
Author(s)
Andrew Dienstfrey, Zydrunas Gimbutas, Joe Chalfoun, Adele Peskin, Kalina Jordanova, Kathryn Keenan, Stephen Ogier
Low-field magnetic resonance imaging offers the promise to significantly increase access to in vivo soft tissue imaging. The technology is both portable and low-cost in relation to the high-field scanners in clinical use today. However, these gains are

AI-Powered ParaView for NIST: Enabling Accessible Scientific Visualization

November 3, 2025
Author(s)
Simon Su, William Sherman, Judith Terrill
This position paper outlines the research agenda for National Institute of Standards and Technology (NIST)'s AI-Powered ParaView effort and how the research directly addresses and supports multiple action items within the America's AI Action Plan. How this

From Traditional Topic Models to LLM Topic Models: Can Large Language Models Replace Traditional Topic Models?

August 1, 2025
Author(s)
Zongxia Li, Lorena Calvo Bartolome, Alexander Hoyle, Daniel Stephens, Paiheng Xu, Alden Dima, Jordan Boyd-Graber, Juan Fung
A common use of NLP is to facilitate the understanding of large document collections, with models based on Large Language Models (LLMs) replacing probabilistic topic models. Yet the effectiveness of LLM-based approaches in real-world applications remains

Quasi-Deterministic Channel Propagation Model for Human Sensing: Gesture Recognition Use Case

July 9, 2025
Author(s)
Jack Chuang, Raied Caromi, Jelena Senic, Samuel Berweger, Neeraj Varshney, Jian Wang, Anuraag Bodi, Camillo Gentile, Nada Golmie
We describe a quasi-determinstic channel propagation model for human gesture recognition reduced from real-time measurements with our context aware channel sounder, considering four human subjects and 20 distinct body motions, for a total of 120,000

A Plan for Global Engagement on AI Standards

April 29, 2025
Author(s)
Jesse Dunietz, Mark Latonero, Kathleen Roberts
This plan has been developed by the Department of Commerce in coordination with the Department of State and agencies across the U.S. Government. It reflects more than 65 comments received in response to a December 2023 Request for Information

Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud

August 2, 2024
Author(s)
Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Hang Mi, Bo Ai, Ruisi He
Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in

A Plan for Global Engagement on AI Standards

July 26, 2024
Author(s)
Jesse Dunietz, Elham Tabassi, Mark Latonero, Kamie Roberts
Recognizing the importance of technical standards in shaping development and use of Artificial Intelligence (AI), the President's October 2023 Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (EO 14110)

Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

July 26, 2024
Author(s)
Chloe Autio, Reva Schwartz, Jesse Dunietz, Shomik Jain, Martin Stanley, Elham Tabassi, Patrick Hall, Kamie Roberts
This document is a cross-sectoral profile of and companion resource for the AI Risk Management Framework (AI RMF 1.0) for Generative AI, pursuant to President Biden's Executive Order (EO) 14110 on Safe, Secure, and Trustworthy Artificial Intelligence. The

Forecasting Operation of a Chiller Plant Facility Using Data Driven Models

July 23, 2024
Author(s)
Behzad Salimian Rizi, Afshin Faramarzi, Amanda Pertzborn, Mohammad Heidarinejad
In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller

Human-in-the-loop Technical Document Annotation: Developing and Validating a System to Provide Machine-Assistance for Domain-Specific Text Analysis

May 14, 2024
Author(s)
Juan Fung, Zongxia Li, Daniel Stephens, Andrew Mao, Pranav Goel, Emily Walpole, Alden A. Dima, Jordan Boyd-Graber
In this report, we address the following question: to what extent can machine learning assist a human with traditional text analysis, such as content analysis or grounded theory in the social sciences? In practice, such tasks require humans to review and

Measurement-driven neural-network training for integrated magnetic tunnel junction arrays

May 14, 2024
Author(s)
William Borders, Advait Madhavan, Matthew Daniels, Vasileia Georgiou, Martin Lueker-Boden, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland, Brian Hoskins
The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann
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