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proposal-development

Mentors and Regional Facilitators
Name Region Skills Interests
Ami Gaspar Campus Champions, Northeast
Alana Romanella Campus Champions
Kevin Brandt Campus Champions, Great Plains
Christopher Maher Campus Champions
Balamurugan Desinghu ACCESS CSSN, Campus Champions, CAREERS, Northeast
Diana Toups Dugas RMACC, SWEETER, Campus Champions
Elie Alhajjar ACCESS CSSN
Eden Furtak-Cole Campus Champions
Gaurav Khanna Campus Champions, CAREERS, Northeast
Henry Neeman Campus Champions
Ibrahim Sheikh CAREERS
James Deaton Campus Champions, Great Plains, ACCESS CSSN
Kirk Anne Campus Champions
Mohammadreza H… Campus Champions
Paul Rulis Campus Champions
Mike Renfro Campus Champions
Xiaoqin Huang ACCESS CSSN
Thomas Cheatham Campus Champions, RMACC
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Engagements

High Performance Computing vs Quantum Computing for Neural Networks supporting Artificial Intelligence
Pace University

A personalized learning system that adapts to learners' interests, needs, prior knowledge, and available resources is possible with artificial intelligence (AI) that utilizes natural language processing in neural networks. These deep learning neural networks can run on high performance computers (HPC) or on quantum computers (QC). Both HPC and QC are emergent technologies. Understanding both systems well enough to select which is more effective for a deep learning AI program, and show that understanding through example, is the ultimate goal of this project. The entry to learning technologies such as HPC and QC is narrow at present because it relies on classical education methods and mentoring. The gap between the knowledge workers needed, which is in high demand, and those with the expertise to teach, which is being achieved at a much slower rate, is widening. Here, an AI cognitive agent, trained via deep learning neural networks, can help in emergent technology subjects by assisting the instructor-learner pair with adaptive wisdom. We are building the foundations for this AI cognitive agent in this project.

The role of the student facilitator will involve optimizing a deep learning neural network, comparing and contrasting with the newest technologies, such as a quantum computer (and/or a quantum computer simulator) and a high performance computer and showing the efficiency of the different computing approaches. The student facilitator will perform these tasks at the rate described in the proposal. Milestone work will be displayed and shared publicly via posting to the Jupyter Notebooks on Google Colab and linked to regular Github uploads.

Status: Complete

People with Expertise

Henry Neeman

University of Oklahoma

Programs

Campus Champions

Roles

research computing facilitator

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Expertise

c
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Diana Toups Dugas

New Mexico State University

Programs

RMACC, SWEETER, Campus Champions

Roles

mentor, researcher/educator, research computing facilitator

Expertise

Ajay Khanna

University of California Merced

Programs

Campus Champions

Roles

student champion

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Expertise

People with Interest

Buck Taylor

University of Portland

Programs

ACCESS CSSN

Roles

Consultant

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Interests

Vedang Chauhan

Western New England University

Programs

Northeast

Roles

mentor, researcher/educator

image of Vedang Chauhan

Interests

David White

Programs

ACCESS CSSN

Roles

cssn, Consultant

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Interests