Name | Region | Skills | Interests |
---|---|---|---|
Christopher Bl… | Campus Champions | ||
Balamurugan Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Daniel Sierra-Sosa | Campus Champions | ||
Edwin Posada | Campus Champions | ||
Gaurav Khanna | Campus Champions, CAREERS, Northeast | ||
Jacob Fosso Tande | Campus Champions | ||
Jonathan Lyon | At-Large, Campus Champions, Kentucky, ACCESS CSSN | ||
Thomas Langford | Campus Champions, CAREERS | ||
Lonnie Crosby | Campus Champions | ||
Lisa Perez | SWEETER | ||
Justin Oelgoetz | Campus Champions | ||
Mark Perri | Campus Champions | ||
Paul Rulis | Campus Champions | ||
Sean Anderson | Campus Champions | ||
Xiaoqin Huang | ACCESS CSSN | ||
Shaohao Chen | Northeast | ||
Soham Pal | Campus Champions, ACCESS CSSN | ||
Swabir Silayi | Campus Champions | ||
Torey Battelle | Campus Champions |
Name | Roles | Skills | Interests |
---|---|---|---|
Christopher Gilbert |
student facilitator |
||
Chris Herdman | researcher/educator |
||
Balamurugan Desinghu |
mentor researcher/educator rcf |
||
Daniel Evans | researcher/educator |
||
Gaurav Khanna |
mentor regional facilitator researcher/educator rcf steering committee |
||
Justin McKennon | researcher/educator |
||
Jonathan Lyon |
mentor researcher/educator |
||
Shaohao Chen |
mentor |
Project Title Sort descending | Project Institution | Project Owner | Tags | Status |
---|---|---|---|---|
C++/17/14 Migration of Path Integral Quantum Monte Carlo Software | University of Vermont | Adrian Del Maestro | software-installation, compiling, debugging, dependencies, performance-tuning, quantum-mechanics, programming | Complete |
Unsupervised learning of topologically ordered phases of matter | Middlebury College | Northeast Cyberteam | gpu, machine-learning, quantum-mechanics | Complete |
Using distributed computing to equilibrate Monte Carlo simulations | Middlebury College | Chris Herdman | distributed-computing, mpi, quantum-mechanics | Complete |
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.
University of Rhode Island
Campus Champions, CAREERS, Northeast
mentor, regional facilitator, researcher/educator, research computing facilitator, Affinity Group Leader, steering committee