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machine-learning

Mentors and Regional Facilitators
Name Region Skills Interests
Andrew Fullard Campus Champions
Aniruddha Maiti ACCESS CSSN, Campus Champions
Alana Romanella Campus Champions
Brian Gregor ACCESS CSSN, Northeast, Campus Champions
Ben Lynch Campus Champions
Brandon Biggs OnDemand
Kevin Brandt Campus Champions, Great Plains, CCMNet
Benjamin Meyers Campus Champions
Craig Gross Campus Champions, CCMNet
Chris Carothers CAREERS
Christopher Bl… Campus Champions
Bala Desinghu ACCESS CSSN, Campus Champions, CAREERS, Northeast
Daniel Morales Campus Champions
Deborah Penchoff Campus Champions
Daniel Sierra-Sosa Campus Champions
Fan Chen ACCESS CSSN
Fernando Garzon ACCESS CSSN
Feseha Abebe-Akele CCMNet
Iman Rahbari Campus Champions, ACCESS CSSN
Jason Smith CAREERS
Jonathan Komperda Campus Champions
Jeffrey Weekley Campus Champions
Jason Wells ACCESS CSSN, Campus Champions
Od Odbadrakh ACCESS CSSN
Lakitha Wijeratne TRECIS
Luis Cueva Parra Campus Champions, CCMNet
shuai liu ACCESS CSSN
Mohsen Ahmadkhani CCMNet, ACCESS CSSN
Mami Hayashida Kentucky
Mattie Niznik Campus Champions
Mahmoud Parvizi Campus Champions
Michael Puerrer Campus Champions, Northeast
Maryam Taeb
Neil McGlohon CAREERS
Jeffrey J. Nuc… CAREERS, CCMNet
Paul Rulis Campus Champions
Renos Zabounidis Campus Champions
Russell Hofmann ACCESS CSSN, CCMNet
Grant Scott Great Plains
Shane Sanders Campus Champions
Xiaoqin Huang ACCESS CSSN
Shaohao Chen Northeast
Liwen Shih Campus Champions, ACCESS CSSN
Swabir Silayi ACCESS CSSN, CCMNet, Campus Champions
Simon Delattre
Suhong Li CAREERS, ACCESS CSSN
Soham Pal ACCESS CSSN, Campus Champions, CCMNet
William Sanders Campus Champions
Sathish Srinivasan ACCESS CSSN
Thomas Pranzatelli
Vedang Chauhan Northeast
Xiaoge Wang Campus Champions
William Lai ACCESS CSSN
Yongwook Song Kentucky
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Affinity Groups

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AI Institutes Cyberinfrastructure Gathering place for AI researchers to find curated information about using ACCESS resources for AI applications and research. Login to join
Anvil Purdue University is the home of Anvil, a powerful new supercomputer that provides advanced computing capabilities to support a wide range of computational and data-intensive research spanning from… Login to join
Jetstream-2 Jetstream2 is a transformative update to the NSF’s science and engineering cloud infrastructure and provides 8 petaFLOPS of supercomputing power to simplify data analysis, boost discovery, and… Login to join

Topics from Ask.CI

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Knowledge Base Resources

Engagements

AI for Business
San Diego State University

The research focus is to apply the pre-training techniques of Large Language Models to the encoding process of the Code Search Project, to improve the existing model and develop a new code searching model. The assistant shall explore a transformer or equivalent model (such as GPT-3.5) with fine-tuning, which can help achieve state-of-the-art performance for NLP tasks. The research also aims to test and evaluate various state-of-the-art models to find the most promising ones.

Status: Complete
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

Stanley Nwoji

Harrisburg University of Science and Technology

Programs

CAREERS

Roles

researcher/educator

Stanley Nwoji, Ph.D.

Expertise

c
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Qingdou Han

Columbia University

Programs

CAREERS

Roles

student-facilitator

Placeholder headshot

Expertise

Neil McGlohon

Rensselaer Polytechnic Institute

Programs

CAREERS

Roles

mentor, steering committee, regional admin

Neil M.

Expertise

People with Interest

Fernando Garzon

University of California, San Diego

Programs

ACCESS CSSN

Roles

mentor, research software engineer

Fernando Garzon's Profile picture

Interests

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Morgan Newton

Santa Monica College

Programs

ACCESS CSSN

Roles

student-facilitator

Picture of Morgan Newton

Interests

Yating Fang

Rutgers University

Programs

Northeast

Roles

student-facilitator

Placeholder headshot

Interests

c
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