AI & Data 

Strategic Vision Team Update

Heather Evans

NIST Program Coordination Office 
The team 

Kirk Dohne (EL) Mike Garris (ITL) Will Guthrie (ITL) Bob Hanisch (MML)

Craig Schlenoff (EL) Jim Warren (MML) Adam Wunderlich (CTL) Brian Zimmerman (PML) 

Heather Evans (PCO) 
Why now? 

“The current wave of progress and 

enthusiasm for AI began around 2010, 

driven by three factors that built upon 

each other: the availability of big data

from sources including e-commerce, 

businesses, social media, science, and 

government; which provided raw 

material for dramatically improved 

machine learning approaches and 

algorithms; which in turn relied on the 

capabilities of more powerful 

computers.”

White House Report,

Preparing for the Future of AI
AI&D Overview & Rationale

• AI and Data are essential to the future of 

research and scientific discovery, 

industrial competitiveness, and social 

progress

• NIST has expertise and resources in place 

that serve as a strong foundation, but are 

subcritical

• Two roles: external and internal
Vision and Strategies 

NIST’s investigation and deployment of data and AI technologies builds confidence and trust that 

drives new measurement research outcomes and an expanded commercial marketplace. 

Three key strategies: 

1. Expertise 

2. Data and Analysis

3. Infrastructure (Networking, Data 

Storage, and Computational)
Expertise

NIST must grow its expertise in data engineering (e.g., data and network infrastructure, 

database engineering), data science (e.g., analytics, informatics, management), 

AI/machine learning (ML) methods, and matching of the appropriate analysis methods 

to different research domains.

Specifically: 

• Increase our in-house expertise with additional specialists 

• Partner with academic, government, and industry leaders to rapidly gain knowledge in these 

areas, for example postdoctoral researchers and exchange programs 

• Increase cross-OU collaboration and centralized resources to rapidly build scale and success 

stories
Data and Analysis

A culture change within NIST is essential to make data usable across space and time, 

from acquisition to storage, access, and use of data (observed or simulated). NIST staff 

should be able to routinely draw on AI/ML tools to gain insight into their research.

Specifically: 

• Train staff and provide them with the tools to practice high quality data management, for 

example electronic laboratory notebooks, metadata capture, databases, etc. 

• Make tools available for both specific types of analysis (e.g. clustering or classification) and 

the general, undirected study of data (e.g. “modern” exploratory data analysis). 

• Develop validation tools and datasets for assessing AI systems and to further research in 

high impact areas, such as advanced communications, health care, and manufacturing
Networking, Data Storage, and Computational Infrastructure 

NIST needs a robust infrastructure to support faster data transfer, centralized storage, 

and shared computational resources for the application of modern tools for data 

analysis and AI algorithms. 

• Network infrastructure does not support the amount of data 

that needs to be transferred and stored

• Data processing is computationally intensive and not widely 

accessible to all NIST programs

• Future AI will have computation demands more than an order 

of magnitude greater than any current in-house capacity
What does success look like? 

• A truly interdisciplinary cross-laboratory data sharing culture at NIST

• It is easy to collect, store, and analyze complex datasets

• NIST researchers routinely draw on AI/ML tools to gain insight into 

their research data and code implementations

• NIST’s programs are providing validation tools and datasets for the 

most in-demand ML and AI application areas

• NIST is seen by industry and academia as a leader providing widely 

accepted tools/technologies for data and AI 
Key Questions

• How can NIST ensure access to talent in this competitive field? 

• Which partners are essential for NIST’s success in realizing this vision? 

• Which research areas are the highest impact for application of ML/AI? 

• What best practices in culture change should NIST implement to 

cultivate data management? 

• How do we best integrate advanced data analysis (ML, AI) techniques in 

our measurement science?