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Developing a Series of AI Challenges for the United States Department of the Air Force

2022-07-14 · Vijay Gadepally, Gregory Angelides, Andrei Barbu, Andrew Bowne, Laura J. Brattain, Tamara Broderick, Armando Cabrera, Glenn Carl, Ronisha Carter, Miriam Cha, Emilie Cowen, Jesse Cummings, Bill Freeman, James Glass, Sam Goldberg, Mark Hamilton, Thomas Heldt, Kuan Wei Huang, Phillip Isola, Boris Katz, Jamie Koerner, Yen-Chen Lin, David Mayo, Kyle McAlpin, Taylor Perron, Jean Piou, Hrishikesh M. Rao, Hayley Reynolds, Kaira Samuel, Siddharth Samsi, Morgan Schmidt, Leslie Shing, Olga Simek, Brandon Swenson, Vivienne Sze, Jonathan Taylor, Paul Tylkin, Mark Veillette, Matthew L Weiss, Allan Wollaber, Sophia Yuditskaya, Jeremy Kepner

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances.

📄 PDF Abstract BibTeX arXiv:2207.07033

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Advancing AI Challenges for the United States Department of the Air Force

2025-10-31 · Christian Prothmann, Vijay Gadepally, Jeremy Kepner, Koley Borchard 외 arxiv

The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial inte…

Optimal Dispatch in Emergency Service System via Reinforcement Learning

2020-10-15 · Cheng Hua, Tauhid Zaman

In the United States, medical responses by fire departments over the last four decades increased by 367%. This had made it critical to decision makers in emergency response departments that existing resources are efficie…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making

2026-05-13 · Orhun Vural, Abdulaziz Ahmed, Ferhat Zengul, James Booth 외 arxiv

Overcrowding in emergency departments (ED) remains a persistent operational challenge worldwide, causing delays in care delivery and downstream congestion. ED boarding time, defined as the duration admitted patients rema…

Time Series Forecasting Decision Making

United States Road Accident Prediction using Random Forest Predictor

2025-04-28 · Dominic Parosh Yamarthi, Haripriya Raman, Shamsad Parvin

Road accidents significantly threaten public safety and require in-depth analysis for effective prevention and mitigation strategies. This paper focuses on predicting accidents through the examination of a comprehensive …

AutoML Prediction Time Series Analysis

A Framework for Evaluating the Impact of Food Security Scenarios

2023-01-23 · Rachid Belmeskine, Abed Benaichouche

This study proposes an approach for predicting the impacts of scenarios on food security and demonstrates its application in a case study. The approach involves two main steps: (1) scenario definition, in which the end u…

Decision Making Time Series Time Series Analysis