Publications and Resources

Leveraging modeling and experimentation for information and communications technology solutioning, assessment and optimization.

This 2024 NHCP research report provides a foundational understanding of ML and its applications and helping to leverage ML for a safer, more efficient, and sustainable transportation future.

The complementary characteristics of LiDARs and cameras motivate their combined use in object detection, especially for autonomous vehicles. We propose a novel automated annotation pipeline.

The Noblis Data Lakehouse has a scalable data infrastructure that provides replication, consistency and reliability; an efficient and scalable engine for data processing; and support for faster analytic queries and the ML development lifecycle.

This document is designed to help researchers in the US navigate and comply with requirements and best practices for human subjects research, with specific emphasis on biometric and forensic science research.

Members of the Noblis team completed a research project on designing and testing the effectiveness of polymerase chain reaction primers for Monkeypox using algorithms – continuing efforts to help prevent and prepare for potential infectious disease outbreaks.

Scientists from Noblis and the Defense Biological Product Assurance Office (DBPAO) published a paper in the online journal Frontiers in Public Health.

The Noblis autonomy concept orchestrates the motions and actions of unfamiliar, connected, and autonomous machines. With this concept, systems of autonomous machines are safer, more productive, and more equitable.

Noblis’ high-performance computing (HPC) services foster client adoption of artificial intelligence (AI) and analytic solutions across national security, law enforcement and federal civilian markets.

Artificial Intelligence (AI) and Machine Learning (ML) are powerful methods for data processing and analysis but are complex to understand. XAI can be used to clarify the deep learning methods within AI and assures the algorithms are looking at the right features during their decision-making process.

This document discusses some of the issues that can arise through this lifecycle, with examples of when the collection or use of such datasets have gone very wrong — and recommends best practices that should help avoid such pitfalls.

A collection of published Noblis forensics research studies evaluating facial recognition.