Name | Region | Skills | Interests |
---|---|---|---|
Alana Romanella | Campus Champions | ||
Michael Blackmon | Campus Champions, ACCESS CSSN | ||
Kevin Brandt | Campus Champions, Great Plains, CCMNet | ||
Bala Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Daniel Morales | Campus Champions | ||
Deborah Penchoff | Campus Champions | ||
Daniel Howard | ACCESS CSSN, Campus Champions, CCMNet, RMACC | ||
David Ryglicki | |||
Daniel Sierra-Sosa | Campus Champions | ||
Fan Chen | ACCESS CSSN | ||
Fernando Garzon | ACCESS CSSN | ||
Ibrahim Sheikh | CAREERS | ||
Jeffrey Weekley | Campus Champions | ||
Od Odbadrakh | ACCESS CSSN | ||
Lonnie Crosby | Campus Champions, ACCESS CSSN | ||
shuai liu | ACCESS CSSN | ||
Mohsen Ahmadkhani | CCMNet, ACCESS CSSN | ||
Michael Puerrer | Campus Champions, Northeast | ||
Maryam Taeb | |||
Nect Admin | Great Plains, Northeast, RMACC | ||
Jeffrey J. Nuc… | CAREERS | ||
Renos Zabounidis | Campus Champions | ||
Grant Scott | Great Plains | ||
Xiaoqin Huang | ACCESS CSSN | ||
Shaohao Chen | Northeast | ||
Simon Delattre | |||
William Lai | ACCESS CSSN | ||
Yongwook Song | Kentucky |
Title | Date |
---|---|
NSF requests research and education use cases for NAIRR | 02/22/24 |
NVIDIA GenAI/LLM Virtual Workshop Series for Higher Ed | 02/17/24 |
Open Call: Minisymposia for PASC24 | 10/05/23 |
Title | Category | Tags | Skill Level |
---|---|---|---|
ACCESS HPC Workshop Series | Learning | deep-learning, machine-learning, neural-networks, big-data, tensorflow, gpu, training, openmpi, c, c++, fortran, openmp, programming, mpi, spark | Beginner, Intermediate |
AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure | Docs | ACES, documentation, TAMU, ai, visualization, deep-learning, machine-learning, neural-networks, login, authentication, composable-systems, gpu, nvidia, slurm, bash, modules, vim, anaconda, conda, programming, python, scikit-learn | Intermediate |
Attention, Transformers, and LLMs: a hands-on introduction in Pytorch | Learning | ai, deep-learning, machine-learning, neural-networks, pytorch | Intermediate |
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.
New Jersey Institute of Technology
CAREERS
student-facilitator