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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

Parameshwaran Pasupathy

Rutgers University - New Brunswick

Programs

ACCESS CSSN, CAREERS

Roles

student-facilitator

Placeholder headshot

Expertise

Grant Scott

University of Missouri

Programs

Great Plains

Roles

mentor, steering committee

Expertise

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Rohit K S S Vuppala

Oklahoma State University

Programs

Campus Champions

Roles

student champion

Placeholder headshot

Expertise

People with Interest

Adedeji Adekunle

Rutgers University, Camden

Programs

CAREERS

Roles

student-facilitator

Interests

Balamurugan Desinghu

Rutgers, the State University of New Jersey

Programs

ACCESS CSSN, Campus Champions, CAREERS, Northeast

Roles

mentor, researcher/educator, research computing facilitator, cssn, Consultant

Bala Desinghu Photo

Interests

Julia Huang

Rensselaer Polytechnic Institute

Programs

CAREERS

Roles

student-facilitator

Interests