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deep-learning

Mentors and Regional Facilitators
Name Region Skills Interests
Alana Romanella Campus Champions
Michael Blackmon Campus Champions
Kevin Brandt Campus Champions, Great Plains
Balamurugan Desinghu ACCESS CSSN, Campus Champions, CAREERS, Northeast
Daniel Sierra-Sosa Campus Champions
Fernando Garzon ACCESS CSSN
Ibrahim Sheikh CAREERS
Jeffrey Weekley Campus Champions
Od Odbadrakh
Lonnie Crosby Campus Champions
shuai liu ACCESS CSSN
Michael Puerrer Campus Champions, Northeast
Maryam Taeb
Nect Admin Great Plains, Northeast, RMACC
Jeffrey J. Nuc… CAREERS
Renos Zabounidis Campus Champions
Grant Scott Great Plains
Xiaoqin Huang ACCESS CSSN
Shaohao Chen Northeast
Simon Delattre
William Lai ACCESS CSSN
Yongwook Song Kentucky
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Engagements

AI for Business
University of California San Diego

The research focus is to apply the pre-training techniques of Large Language Models to the encoding process of the Code Search Project, to improve the existing model and develop a new code searching model. The assistant shall explore a transformer or equivalent model (such as GPT-3.5) with fine-tuning, which can help achieve state-of-the-art performance for NLP tasks. The research also aims to test and evaluate various state-of-the-art models to find the most promising ones.

Status: In Progress
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

Parth Shah

New Jersey Institute of Technology

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student-facilitator

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Ahamed Arafaath Muthalif Mubarak Ali

New Jersey Institute of Technology

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student-facilitator

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Yuan Qu

Rutgers University

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student-facilitator

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People with Interest

Parameshwaran Pasupathy

Rutgers University - New Brunswick

Programs

ACCESS CSSN, CAREERS

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student-facilitator

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Interests

Youngsu Kim

California State University-San Bernardino

Programs

ACCESS CSSN

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cssn, CIP

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Tejasvi Munge

New Jersey Institute of Technology

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student-facilitator

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