Name | Region | Skills | Interests |
---|---|---|---|
Michael Blackmon | Campus Champions | ||
Benjamin Meyers | Campus Champions | ||
Cody Stevens | Campus Champions | ||
Balamurugan Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Elie Alhajjar | ACCESS CSSN | ||
Craig Gross | Campus Champions | ||
Iman Rahbari | Campus Champions, ACCESS CSSN | ||
Yu-Chieh Chi | Campus Champions | ||
Jordan Hayes | Campus Champions | ||
Jason Wells | ACCESS CSSN, Campus Champions | ||
Kenneth Bundy | CAREERS | ||
shuai liu | ACCESS CSSN | ||
Michael Puerrer | Campus Champions, Northeast | ||
Maryam Taeb | |||
Simon Delattre | |||
Soham Pal | Campus Champions, ACCESS CSSN | ||
Swabir Silayi | Campus Champions |
Title | Category | Tags | Skill Level |
---|---|---|---|
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 |
DeapSECURE – Data-Enabled Advanced Computational Training Platform for Cybersecurity Research and Education | Learning | ai, deep-learning, machine-learning, neural-networks, visualization, big-data, data-analysis, jekyll, batch-jobs, slurm, bash, ssh, training, workforce-development, python, scikit-learn, cybersecurity | Beginner |
Python Tools for Data Science | Video | ai, machine-learning, big-data, data-analysis, data-wrangling, data-science, training, workforce-development, python, scikit-learn, sql | Intermediate |
A personalized learning system that adapts to learners' interests, needs, prior knowledge, and available resources is possible with artificial intelligence (AI) that utilizes natural language processing in neural networks. These deep learning neural networks can run on high performance computers (HPC) or on quantum computers (QC). Both HPC and QC are emergent technologies. Understanding both systems well enough to select which is more effective for a deep learning AI program, and show that understanding through example, is the ultimate goal of this project. The entry to learning technologies such as HPC and QC is narrow at present because it relies on classical education methods and mentoring. The gap between the knowledge workers needed, which is in high demand, and those with the expertise to teach, which is being achieved at a much slower rate, is widening. Here, an AI cognitive agent, trained via deep learning neural networks, can help in emergent technology subjects by assisting the instructor-learner pair with adaptive wisdom. We are building the foundations for this AI cognitive agent in this project.
The role of the student facilitator will involve optimizing a deep learning neural network, comparing and contrasting with the newest technologies, such as a quantum computer (and/or a quantum computer simulator) and a high performance computer and showing the efficiency of the different computing approaches. The student facilitator will perform these tasks at the rate described in the proposal. Milestone work will be displayed and shared publicly via posting to the Jupyter Notebooks on Google Colab and linked to regular Github uploads.
Rutgers University - New Brunswick
ACCESS CSSN, CAREERS
student-facilitator
California State University-Los Angeles
ACCESS CSSN
student-facilitator
Rutgers, the State University of New Jersey
ACCESS CSSN, Campus Champions, CAREERS, Northeast
mentor, researcher/educator, research computing facilitator, cssn, Consultant
The College of New Jersey
Campus Champions, CAREERS
research computing facilitator, ci systems engineer