Name | Region | Skills | Interests |
---|---|---|---|
Devin Bayly | ACCESS CSSN, Campus Champions, CCMNet | ||
Kevin Brandt | Campus Champions, Great Plains, CCMNet | ||
Cyd Burrows-Sc… | Campus Champions, CCMNet | ||
Dennis Milechin | Northeast | ||
Ibrahim Sheikh | CAREERS | ||
Kenneth Bundy | CAREERS | ||
Lauren Hill-Beaton | CAREERS | ||
Mohsen Ahmadkhani | CCMNet, ACCESS CSSN | ||
Rob Harbert | Northeast | ||
Grant Scott | Great Plains | ||
Sue Oldenburg | Campus Champions |
Name | Roles | Skills | Interests |
---|---|---|---|
Dennis Milechin |
mentor rcf |
||
George Avirappattu | researcher/educator |
||
Rob Harbert |
mentor |
Project Title | Project Institution | Project Owner | Tags | Status |
---|---|---|---|---|
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 |
Deep Learning High-Resolution Land Cover Mapping for Vermont | University of Vermont | Jarlath O'Neil-Dunne | arcgis, big-data, distributed-computing, gis, image-processing, machine-learning, python | Complete |
Utility poles Geo-Localization and Risk Estimation using Deep Learning | University of Vermont | safwan wshah | ai, arcgis, big-data, conda, cuda, deep-learning, gis, gpu, machine-learning, pip, python, tensorflow, unix-environment | Complete |
Title | Category | Tags | Skill Level |
---|---|---|---|
CI Computing Module For All | Learning | ai, computer-vision, neural-networks, visualization, big-data, gis, parallelization, data-management, data-science, bioinformatics, open-ondemand, hpc-getting-started, cpu-architecture, distributed-computing, job-submission, jupyterhub, python, r, cybersecurity, containers | Beginner |
GDAL Multi-threading | Learning | parallelization, gis | Intermediate |
GIS: Geocoding Services | Docs | gis | Beginner, Intermediate |
GeoACT (GEOspatial Agent-based model for Covid Transmission) is a designed to simulate a range of intervention scenarios to help schools evaluate their COVID-19 plans to prevent super-spreader events and outbreaks. It consists of several modules, which compute infection risks in classrooms and on school buses, given specific classroom layouts, student population, and school activities. The first version of the model was deployed on the Expanse (and earlier, COMET) resource at SDSC and accessed via the Apache Airavata portal (geoact.org). The second version is a rewrite of the model which makes it easier to adjust to new strains, vaccines and boosters, and include detailed user-defined school schedules, school floor plans, and local community transmission rates. This version is nearing completion. We’ll use Expanse to run additional scenarios using the enhanced model and the newly added meta-analysis module. The current goal is to make the model more general so that it can be used for other health emergencies. GeoACT has been in the news, e.g. UC San Diego Data Science Undergrads Help Keep K-12 Students COVID-Safe, and SDSC Supercomputers Helped Enable Safer School Reopenings (HPCWire 2022 Editors' Choice Award)
South Dakota State University
Campus Champions, Great Plains, CCMNet
regional facilitator, representative, research computing facilitator, Affinity Group Leader, CCMNet PM, CCMNet
University of Arizona
ACCESS CSSN, Campus Champions, CCMNet
research computing facilitator, Affinity Group Leader, CCMNet
CUNY School of Professional Studies
CAREERS
student-facilitator, mentor, regional facilitator, researcher/educator, research computing facilitator