Name | Region | Skills | Interests |
---|---|---|---|
Adam Carlson | Campus Champions | ||
Bhushan Chitre | Kentucky | ||
Michael Blackmon | Campus Champions | ||
Brian Haymore | RMACC, Campus Champions | ||
Christina Divoll | Campus Champions, ACCESS CSSN | ||
Chuck Pavloski | Campus Champions, CAREERS | ||
Chuck Pavloski | Campus Champions, CAREERS | ||
Charles Forsyth | Campus Champions | ||
Chad Julius | Campus Champions, Great Plains | ||
Stephen Cousins | Campus Champions | ||
Craig Earley | RMACC | ||
Edwin Posada | Campus Champions | ||
Gaurav Khanna | Campus Champions, CAREERS, Northeast | ||
Jeff Falgout | Campus Champions, RMACC | ||
Juan Vanegas | Northeast | ||
Jason Wells | ACCESS CSSN, Campus Champions | ||
Kali McLennan | Campus Champions, Great Plains | ||
Marina Kraeva | Campus Champions | ||
Paul Gluhosky | Campus Champions | ||
Ric Anderson | Campus Champions | ||
Ruben Lara | Campus Champions | ||
Sean Anderson | Campus Champions | ||
Shawn Doughty | Campus Champions, Northeast | ||
Spencer Pruitt | Northeast | ||
Sumit Saluja | Campus Champions | ||
Shawn Sivy | Campus Champions, CAREERS | ||
Scott Valcourt | Northeast, Campus Champions | ||
Trey Breckenridge | Campus Champions |
Name | Roles | Skills | Interests |
---|---|---|---|
Gaurav Khanna |
mentor regional facilitator researcher/educator rcf steering committee |
||
Juan Vanegas |
mentor |
||
Shawn Doughty |
mentor rcf |
||
Spencer Pruitt |
mentor |
||
Scott Valcourt |
mentor researcher/educator rcf steering committee |
Project Title | Project Institution | Project Owner | Tags | Status |
---|---|---|---|---|
Student-Developed HPC Cluster for Active Learning | University of New Hampshire | Scott Valcourt | big-data, hardware, hpc-cluster-build, hpc-operations | Complete |
Incorporating Hytools into the current image processing pipeline to produce better vegetation maps that will account for radiometric signals and will parallelize workflow | University of Maine at Fort Kent | Larry Whitsel | big-data, gis, hpc-operations, image-processing, python, r | Complete |
Benchmarking Locally-Developed HPC Resources | University of New Hampshire | Scott Valcourt | backup, big-data, data-management, file-systems, hpc-cluster-build, hpc-operations, permissions, provisioning, schedulers, slurm, unix-environment | Complete |
Logo | Name | Description | Tags | Join |
---|---|---|---|---|
Open OnDemand | An intuitive, innovative, and interactive interface to remote computing resources.Open OnDemand helps computational researchers and students efficiently utilize remote computing resources by making… | Login to join | ||
hpc.social | High Performance Computing and related fields, including Big Data, Research Computing, and related hardware and software optimized for these fields, including research software engineering and… | Login to join |
Title | Category | Tags | Skill Level |
---|---|---|---|
HPCwire | Website | documentation, pytorch, data-science, bioinformatics, hpc-operations, training, programming, programming-best-practices, python | Beginner, Intermediate, Advanced |
Open OnDemand | Website | ondemand, administering-hpc, cluster-management, cluster-support, hpc-operations, batch-jobs, kubernetes | Beginner, Intermediate, Advanced |
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.
Columbia University Medical Center
Campus Champions
research computing facilitator
Penn State
Campus Champions, CAREERS
mentor, research computing facilitator
Harvard University
ACCESS CSSN, Campus Champions
research computing facilitator, cssn
Austin Peay State University
Campus Champions
mentor, researcher/educator, research computing facilitator