Name | Region | Skills | Interests |
---|---|---|---|
Adam Carlson | Campus Champions | ||
Carrie Brown | CAREERS, ACCESS CSSN | ||
Cody Stevens | Campus Champions | ||
Balamurugan Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Derek Strong | Campus Champions | ||
Edwin Posada | Campus Champions | ||
Fernando Garzon | ACCESS CSSN | ||
Georgia Stuart | TRECIS | ||
Craig Gross | Campus Champions | ||
Ibrahim Sheikh | CAREERS | ||
Yu-Chieh Chi | Campus Champions | ||
Jacob Fosso Tande | Campus Champions | ||
Jordan Hayes | Campus Champions | ||
Jason Wells | ACCESS CSSN, Campus Champions | ||
Kali McLennan | Campus Champions, Great Plains | ||
shuai liu | ACCESS CSSN | ||
Michael Puerrer | Campus Champions, Northeast | ||
Maryam Taeb | |||
Paul Rulis | Campus Champions | ||
Mike Renfro | Campus Champions | ||
Rob Harbert | Northeast | ||
Amy Roberts | Campus Champions | ||
Ruben Lara | Campus Champions | ||
Sean Anderson | Campus Champions | ||
Xiaoqin Huang | ACCESS CSSN | ||
Suhong Li | CAREERS, ACCESS CSSN | ||
Soham Pal | Campus Champions, ACCESS CSSN | ||
Sumit Saluja | Campus Champions | ||
Swabir Silayi | Campus Champions | ||
Tyler Burkett | Kentucky | ||
William Lai | ACCESS CSSN |
Name | Roles | Skills | Interests |
---|---|---|---|
Kali McLennan |
mentor rcf |
Project Title Sort descending | Project Institution | Project Owner | Tags | Status |
---|---|---|---|---|
USD CI Improvements to Advanced Chemistry and Neuroscience Research | University of South Dakota | Kevin Brandt | administering-hpc, computational-chemistry, file-transfer, gaussian, github, globus, research-facilitation, research-grants, training, workflow | Finishing Up |
Title | Date |
---|---|
HPC and Data Science Summer Institute | 8/05/24 |
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.
City University of New York School of Professional Studies
CAREERS
student-facilitator
University of California, San Diego
ACCESS CSSN
mentor, research software engineer
Yale University
ACCESS CSSN, Campus Champions, CAREERS
mentor, research computing facilitator, steering committee, regional admin, Match SC