Skip to main content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.


The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

The opinions, findings, and conclusions or recommendations expressed are those of the Center author(s) and do not necessarily reflect the views of the National Science Foundation.

Center Overview

With the vision of creating artificial intelligence and leveraging collective wisdom from academia, industry, and governments, the Center for Big Learning (CBL) consortium focuses on large-scale deep learning, intelligent platforms, and deep-learning-enabled Big Data applications in multiple disciplines.

The mission of CBL is to explore and pioneer research frontiers in emerging large-scale deep learning for a broad spectrum of Big Data applications; design novel intelligent platforms to enable big learning research and applications; transfer research discoveries to meet urgent needs in industry with CBL's diverse members; and nurture the next-generation talents in a setting that mixes academic and industry with real-world relevance and significance via the industry-university consortium.


  • University of Oregon
  • University of Florida
  • University of Missouri
View Center Website

Center Personnel

Joel Harley
Acting Center Director
+1 352 392 2692

Zhu Li
+1 816 235 2346

Thien Nguyen
+1 541 346 1398

Research Focus

CBL conducts research in the area of big learning, including:

  • Applications (general, business, cybersecurity, health and biology, natural language processing, vision, internet of things, and robotics).
  • Models and algorithms.
  • Systems and platforms.


Member Organizations

IUCRC affiliated member organizations are displayed as submitted by the Center. Non-federal organizations are not selected, approved, or otherwise endorsed by the National Science Foundation.
  • Adobe Research
  • ETRI
  • Inno Peak Tech
  • L3Harris Corporation
  • Qualcomm