Name | Region | Skills | Interests |
---|---|---|---|
Andrew Sherman | ACCESS CSSN, Campus Champions, CAREERS | ||
Balamurugan Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Daniel Howard | RMACC, Campus Champions, ACCESS CSSN | ||
Gaurav Khanna | Campus Champions, CAREERS, Northeast | ||
Craig Gross | Campus Champions | ||
Paul Rulis | Campus Champions | ||
Renos Zabounidis | Campus Champions | ||
Ron Rahaman | Campus Champions | ||
Grant Scott | Great Plains | ||
Shaohao Chen | Northeast | ||
Soham Pal | Campus Champions, ACCESS CSSN | ||
Swabir Silayi | Campus Champions |
Name | Roles | Skills | Interests |
---|---|---|---|
Balamurugan Desinghu |
mentor researcher/educator rcf |
||
Gaurav Khanna |
mentor regional facilitator researcher/educator rcf steering committee |
||
Shaohao Chen |
mentor |
Project Title | Project Institution | Project Owner | Tags | Status Sort descending |
---|---|---|---|---|
Light Propagation in a Temporal Focusing Microscope using Matlab | Middlebury College | Northeast Cyberteam | matlab, parallelization, vectorization | Complete |
Title | Category | Tags | Skill Level |
---|---|---|---|
Introductory Tutorial to Numpy and Pandas for Data Analysis | Docs | ai, big-data, data-analysis, vectorization | Beginner |
Numba: Compiler for Python | Docs | vectorization, optimization, performance-tuning, parallelization | Intermediate, Advanced |
Scikit-Learn: Easy Machine Learning and Modeling | Tool | documentation, ai, plotting, visualization, big-data, data-analysis, deep-learning, image-processing, machine-learning, monte-carlo, neural-networks, vectorization | Beginner, Intermediate |
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.
Georgia Institute of Technology
Campus Champions
research software engineer
Rutgers, the State University of New Jersey
ACCESS CSSN, Campus Champions, CAREERS, Northeast
mentor, researcher/educator, research computing facilitator, cssn, Consultant
University of Rhode Island
Campus Champions, CAREERS, Northeast
mentor, regional facilitator, researcher/educator, research computing facilitator, Affinity Group Leader, steering committee
Rutgers, the State University of New Jersey
ACCESS CSSN, Campus Champions, CAREERS, Northeast
mentor, researcher/educator, research computing facilitator, cssn, Consultant
University of Missouri-Kansas City
Campus Champions
researcher/educator, research computing facilitator