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Center for Big Learning (CBL)

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.

Universities

Tampere University
University of Florida
University of Oregon
University of Missouri

View Center Website

Center Personnel

Joel Harley
Center Director
+1 352 392 2692
joel.harley@ufl.edu

Zhu Li
Co-Director
+1 816 235 2346
lizhu@umkc.edu

Thien Nguyen
Co-Director
+1 541 346 1398
thien@cs.uoregon.edu

Moncef Gabbouj
International Site Director
+358 40 0736613
moncef.gabbouj@tuni.fi

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.

Awards

1747751

Phase I IUCRC University of Missouri-Kansas City: Center for Big Learning (CBL)

This project establishes the NSF Industry/University Collaborative Research Center (I/UCRC) for Big Learning (CBL) to accelerate innovation and impact of Deep Learning in various embedded applications. The vision is to create intelligence towards intelligence-driven society. Through catalyzing the fusion of diverse expertise from the consortium of faculty members, students, industry partners, and federal agencies, CBL seeks to create state-of-the-art deep learning methodologies and technologies and enable intelligent applications, transforming broad domains, such as business, healthcare, Internet-of-Things, and cybersecurity. This timely initiative creates a unique platform for empowering our next-generation talents with cutting-edge technologies of societal relevance and significance.

The University of Missouri at Kansas City (UMKC) site focuses on the deep learning in embedded systems for mobile and IoT applications. It is based on a framework called DeepLite for deep learning model compression and acceleration that can fit cutting edge deep learning capabilities in embedded systems with very limited computing, storage, communication and power capabilities. DeepLite allows embedded deep learning model training and compression for power, storage, computation complexity tradeoffs with learning performances for targeted embedded applications like immersive content capture, depth and action sensing, visual surveillance, next gen image and video compression and communication.

CBL is expected to make wide ranging and long lasting impact to machine learning algorithm, system and application research, accelerating deep learning technology innovation and adoption in the real world, enable transformative new capabilities and new applications in all aspect of society, from education, medicine, media, to security and defense. CBL seamlessly integrates innovation, engineering education, technology business incubation, and community engagement. It facilitates closer interactions and cross pollination of ideas between academia and industry, broaden the research horizon for faculties and students, while help shrink the time to impact and time to market of new technology.

The center repository will be hosted at http://nsfcbl.org. The data, code, documents will be well organized and maintained on the CBL servers for the duration of the center for more than five years and beyond. The internal code repository will be managed by GitLab. After the software packages are well documented and tested, they will be released and managed by popular public code hosting services, such as GitHub and Bitbucket. Read More >

1747783

Phase I IUCRC University of Florida: Center for Big Learning

This project establishes the NSF Industry/University Collaborative Research Center for Big Learning (CBL). The vision is to create intelligence towards intelligence-driven society. Through catalyzing the fusion of diverse expertise from the consortium of faculty members, students, industry partners, and federal agencies, CBL seeks to create state-of-the-art deep learning methodologies and technologies and enable intelligent applications, transforming broad domains, such as business, healthcare, Internet-of-Things, and cybersecurity. This timely initiative creates a unique platform for empowering our next-generation talents with cutting-edge technologies of societal relevance and significance.

This project establishes the NSF Industry/University Collaborative Research Center for Big Learning (CBL) at University of Florida (UF). With substantial breakthroughs in multiple modalities of challenges, such as computer vision, speech recognition, and natural language understanding, the renaissance of machine intelligence is dawning. The CBL vision is to create intelligence towards intelligence-driven society. The mission is to pioneer novel deep learning algorithms, systems, and applications through unified and coordinated efforts in the CBL consortium. The UF Site will focus on intelligent platforms and applications and closely collaborate with other sites on deep learning algorithms, systems, and applications.

The CBL will have broad transformative impacts in technologies, education, and society. CBL aims to create pioneering research and applications to address a broad spectrum of real-world challenges, making significant contributions and impacts to the deep learning community. The discoveries from CBL will make significant contributions to promote products and services of industry in general and CBL industry partners in particular. As the magnet of deep learning research and applications, CBL offers an ideal platform to nurture next-generation talents through world-class mentors from both academia and industry, disseminates the cutting-edge technologies, and facilitates industry/university collaboration and technology transfer.

The center repository will be hosted at http://nsfcbl.org. The data, code, documents will be well organized and maintained on the CBL servers for the duration of the center for more than five years and beyond. The internal code repository will be managed by GitLab. After the software packages are well documented and tested, they will be released and managed by popular public code hosting services, such as GitHub and Bitbucket. Read More >

1747798

Phase I IUCRC University of Oregon: Center for Big Learning

Recent advances in machine learning are ushering in a new era in intelligent computing. The NSF Industry/University Collaborative Research Center for Big Learning (CBL) at the University of Oregon (UO) aims to design novel algorithms and develop efficient systems for deep learning applications in the era of big data. CBL will catalyze diverse expertise of faculty members, students, industry partners, and federal agencies to create state-of-the-art deep learning methodologies, technologies, and applications across broad domains of business, healthcare, and Internet-of-Things. CBL will provide training for new scientists and graduate students, as well as a rich environment for cross-disciplinary engagement.

The mission of CBL is to perform pioneering research and development in powerful deep learning algorithms, efficient intelligent systems, and novel applications through unified and coordinated efforts in the CBL consortium. The CBL research team at the UO includes experts in data science, artificial intelligence, machine learning, high-performance computing, health informatics, and bioinformatics. They will explore research projects related to medical record analysis, health behavior prediction, natural language processing, performance optimization, financial forecasting, and computer vision by deploying various deep learning models.

CBL is expected to have broad transformative impacts in computational technologies, education, and society. Through research and applications to address a broad spectrum of real-world challenges, CBL will make significant contributions and impacts to the deep learning community. The discoveries from CBL will amplify opportunities for new products and services of industry in general, while delivering key techniques to CBL industry partners in particular. As the nexus of deep learning research and applications, CBL offers an ideal platform to nurture next-generation talents through world-class mentors from both academia and industry, disseminate the cutting-edge technologies, and facilitate industry/university collaborative research.

The center's repository is hosted at http://nsfcbl.org. The data, code, and documents will be organized and maintained on the CBL server for the duration of the center plus five years. The internal code repository will be hosted at GitLab. After the software packages are well documented and tested, they will be released and hosted at popular public servers, such as GitHub and Bitbucket. Read More >

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 U.S. National Science Foundation.

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