Name | Region | Skills | Interests |
---|---|---|---|
Alana Romanella | Campus Champions | ||
Balamurugan Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
diana Trotman | CAREERS | ||
Daniel Sierra-Sosa | Campus Champions | ||
Fernando Garzon | ACCESS CSSN | ||
Georgia Stuart | TRECIS | ||
Craig Gross | Campus Champions | ||
Iman Rahbari | Campus Champions, ACCESS CSSN | ||
Jason Yalim | Campus Champions | ||
Katia Bulekova | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Laura Christopherson | Campus Champions | ||
shuai liu | ACCESS CSSN | ||
Michael Puerrer | Campus Champions, Northeast | ||
Maryam Taeb | |||
Mahmoud Parvizi | Campus Champions | ||
Paul Rulis | Campus Champions | ||
Russell Hofmann | ACCESS CSSN | ||
Xiaoqin Huang | ACCESS CSSN | ||
Suhong Li | CAREERS, ACCESS CSSN | ||
Swabir Silayi | Campus Champions | ||
Yun Shen | CAREERS, Northeast, ACCESS CSSN |
Title | Date |
---|---|
NSF requests research and education use cases for NAIRR | 02/22/24 |
Title | Date |
---|---|
Getting Started with Jetstream2 | 4/04/24 |
Using the NSDF Services for End-to-End Analysis and Visualization of Large Scientific Data | 4/10/24 |
GlobusWorld 2024 | 5/07/24 |
Title | Category | Tags | Skill Level |
---|---|---|---|
AI Institutes Cyberinfrastructure Documents: SAIL Meeting | Learning | access-account, ai, data-analysis, machine-learning | Beginner, Intermediate, Advanced |
An Introduction to the Julia Programming Language | Learning | ai, data-analysis, machine-learning, julia | Beginner |
Applications of Machine Learning in Engineering and Parameter Tuning Tutorial | Learning | data-analysis, machine-learning, python | Beginner, Intermediate |
I am new to ACCESS. I have a little bit of past experience running code on NCSA's Blue Waters. As a self-taught programmer, it would be interesting to learn from an experienced mentor.
Here's an overview of my project:
Anxiety detection is topic that is actively studied but struggles to generalize and perform outside of controlled lab environments. I propose to critically analyze state of the art detection methods to quantitatively quantify failure modes of existing applied machine learning models and introduce methods to robustify real-world challenges. The aim is to start the study by performing sensitivity analysis of existing best-performing models, then testing existing hypothesis of real-world failure of these models. We predict that this will lead us to understand more deeply why models fail and use explainability to design better in-lab experimental protocols and machine learning models that can perform better in real-world scenarios. Findings will dictate future directions that may include improving personalized health detection, careful design of experimental protocols that empower transfer learning to expand on existing reach of anxiety detection models, use explainability techniques to inform better sensing methods and hardware, and other interesting future directions.
New Jersey Institute of Technology
CAREERS
student-facilitator
Rutgers University
Campus Champions
research computing facilitator, Affinity Group Leader