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Mentors and Regional Facilitators
Name Region Skills Interests
Tony Elam Kentucky
Alana Romanella Campus Champions
Brian Gregor ACCESS CSSN, Northeast, Campus Champions
Bala Desinghu ACCESS CSSN, Campus Champions, CAREERS, Northeast
Deborah Penchoff Campus Champions
Dylan Perkins ACCESS CSSN, RMACC
David Ryglicki
Fernando Garzon ACCESS CSSN
Feseha Abebe-Akele CCMNet
Feng George Yu Campus Champions
Jacob Fosso Tande ACCESS CSSN, Campus Champions, CCMNet
Jordan Hayes Campus Champions
Jacob Pessin Northeast
Katia Bulekova ACCESS CSSN, Campus Champions, CAREERS, CCMNet, Northeast
Thomas Langford Campus Champions, CAREERS
shuai liu ACCESS CSSN
Mohsen Ahmadkhani CCMNet, ACCESS CSSN
Mahmoud Parvizi Campus Champions
Maryam Taeb
Neil McGlohon CAREERS
Jeffrey J. Nuc… CAREERS, CCMNet
Rebecca Belshe Campus Champions, CCMNet
Rob Harbert Northeast
Grant Scott Great Plains
Simon Delattre
Suhong Li CAREERS, ACCESS CSSN
Sathish Srinivasan ACCESS CSSN
Scott Valcourt Northeast, Campus Champions
Yun Shen CAREERS, Northeast, ACCESS CSSN, CCMNet
Yongwook Song Kentucky

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Topics from Ask.CI

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Engagements

Bayesian nonparametric ensemble air quality model predictions at high spatio-temporal daily nationwide  1 km grid cell
Columbia University

I aim to run a Bayesian Nonparametric Ensemble (BNE) machine learning model implemented in MATLAB. Previously, I successfully tested the model on Columbia's HPC GPU cluster using SLURM. I have since enabled MATLAB parallel computing and enhanced my script with additional lines of code for optimized execution. 

I want to leverage ACCESS Accelerate allocations to run this model at scale.

The BNE framework is an innovative ensemble modeling approach designed for high-resolution air pollution exposure prediction and spatiotemporal uncertainty characterization. This work requires significant computational resources due to the complexity and scale of the task. Specifically, the model predicts daily air pollutant concentrations (PM2.5​ and NO2 at a 1 km grid resolution across the United States, spanning the years 2010–2018. Each daily prediction dataset is approximately 6 GB in size, resulting in substantial storage and processing demands.

To ensure efficient training, validation, and execution of the ensemble models at a national scale, I need access to GPU clusters with the following resources:

  • Permanent storage: ≥100 TB
  • Temporary storage: ≥50 TB
  • RAM: ≥725 GB

In addition to MATLAB, I also require Python and R installed on the system. I use Python notebooks to analyze output data and run R packages through a conda environment in Jupyter Notebook. These tools are essential for post-processing and visualization of model predictions, as well as for running complementary statistical analyses.

To finalize the GPU system configuration based on my requirements and initial runs, I would appreciate guidance from an expert. Since I already have approval for the ACCESS Accelerate allocation, this support will help ensure a smooth setup and efficient utilization of the allocated resources.

Status: Complete

People with Expertise

Abigail Waters

Massachusetts Green High Performance Computing Center

Programs

Campus Champions, Northeast

Roles

student-facilitator, student champion

User

Expertise

Sathish Srinivasan

Programs

ACCESS CSSN

Roles

mentor, researcher/educator, research computing facilitator, research software engineer, cssn

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Expertise

Jordan Hayes

University of California - Riverside

Programs

Campus Champions

Roles

research computing facilitator

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Expertise

People with Interest

Matthew Chung

University of California Riverside

Programs

Campus Champions

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Interests

Anton Semenchenko

Programs

CCMNet

Roles

CCMNet

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Interests

Lucas Varella

Worcester Polytechnic Institute

Programs

Northeast

Roles

student-facilitator, regional facilitator, Masquerade

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Interests