Name | Region | Skills | Interests |
---|---|---|---|
Aaron Jezghani | Campus Champions | ||
Michael Blackmon | Campus Champions | ||
Cody Stevens | Campus Champions | ||
Cesar Sul | ACCESS CSSN, Campus Champions | ||
Balamurugan Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Fernando Garzon | ACCESS CSSN | ||
Georgia Stuart | TRECIS | ||
Jason Smith | CAREERS | ||
Jacob Fosso Tande | Campus Champions | ||
Jeffrey Weekley | Campus Champions | ||
Od Odbadrakh | |||
Thomas Langford | Campus Champions, CAREERS | ||
Lonnie Crosby | Campus Champions | ||
Michael Puerrer | Campus Champions, Northeast | ||
Patrick Burns | RMACC | ||
Paul Rulis | Campus Champions | ||
Renos Zabounidis | Campus Champions | ||
Ron Rahaman | Campus Champions |
Name | Roles | Skills | Interests |
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Carlos Paniagua | researcher/educator |
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Balamurugan Desinghu |
mentor researcher/educator rcf |
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Hyungil Kye |
student facilitator |
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Jason Smith |
mentor |
||
Thomas Langford |
mentor rcf |
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Dylan Le |
student facilitator |
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Nithila Arunkumar |
student facilitator |
||
Parameshwaran … |
student facilitator |
||
Shagesh Sridharan |
student facilitator |
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Stanley Nwoji | researcher/educator |
Project Title | Project Institution | Project Owner | Tags | Status |
---|---|---|---|---|
Computational Simulation of One-Dimensional Porous Polymers | The University at Albany, SUNY | Jeremy Feldblyum | computational-chemistry, materials-science, monte-carlo | In Progress |
Model Mie scattering and light propagation through a high scattering medium using Monte Carlo simulation | Southern Connecticut State University | Binlin Wu | matlab, monte-carlo, programming, scheduling | Complete |
My ongoing project is focused on using species trait value (as data matrices) and its corresponding phylogenetic relationship (as a distance matrix) to reconstruct the evolutionary history of the smoke-induced seed germination trait. The results of this project are expected to increase the predictability of which untested species could benefit from smoke treatment, which could promote germination success of native species in ecological restoration. This computational resources allocated for this project pull from the high-memory partition of our Ivy cluster of HPCC (Centos 8, Slurm 20.11, 1.5 TB memory/node, 20 core /node, 4 node). However, given that I have over 1300 species to analyze, using the maximum amount of resources to speed up the data analysis is a challenge for two reasons: (1) the ancestral state reconstruction (the evolutionary history of plant traits) needs to use the Markov Chain Monte Carlo (MCMC) in Bayesian statistics, which runs more than 10 million steps and, according to experienced evolutionary biologists, could take a traditional single core simulation up 6 months to run; and (2) my data contain over 1300 native species, with about 500 polymorphic points (phylogenetic uncertainty), which would need a large scale of random simulation to give statistical strength. For instance, if I use 100 simulations for each 500 uncertainty points, I would have 50,000 simulated trees. Based on my previous experience with simulations, I could design codes to parallel analyze 50,000 simulated trees but even with this parallelization the long run MCMC will still require 50000 cores to run for up to 6 months. Given this computational and evolutionary research challenge, my current work is focused on discovering a suitable parallelization methods for the MCMC steps. I hope to have some computational experts to discuss my project.
University of Vermont
Northeast
student-facilitator, researcher/educator
Georgia Institute of Technology
Campus Champions
research software engineer