artificial-intelligence
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NAIRR Pilot Office Hours | 5/13/25 |
NAIRR Pilot Office Hours | 5/27/25 |
NAIRR Pilot Office Hours | 6/10/25 |
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Title | Category | Tags | Skill Level |
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Enhancing LLMs with RAG: A Beginner’s Guide | Learning | ai, llm, NAIRR-pilot, generative-ai, nlp, deep-learning, machine-learning, neural-networks, reporting, artificial-intelligence, computer-science, data-science, jupyterhub, python | Beginner |
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Engagements

Bayesian nonparametric ensemble air quality model predictions at high spatio-temporal daily nationwide 1 km grid cell
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.
People with Expertise
Expertise
Jonathan Komperda
University of Illinois at Chicago
Programs
Campus Champions
Roles
research computing facilitator

Expertise
Manas Vishal
University of Massachusetts, Dartmouth
Programs
CAREERS, Northeast, CCMNet
Roles
student-facilitator, CCMNet
Expertise
People with Interest
Interests
Jeevesh Choudhury
Arizona State University
Programs
CCMNet
Roles
student-facilitator, research software engineer, CCMNet