Name | Region | Skills | Interests |
---|---|---|---|
Andrew Fullard | Campus Champions | ||
Alyssa Pivirotto | ACCESS CSSN, Campus Champions | ||
Alana Romanella | Campus Champions | ||
Craig Gross | Campus Champions, CCMNet | ||
Bala Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
diana Trotman | CAREERS | ||
Deborah Penchoff | Campus Champions | ||
David Ryglicki | |||
Daniel Sierra-Sosa | Campus Champions | ||
Fernando Garzon | ACCESS CSSN | ||
Feseha Abebe-Akele | CCMNet | ||
Georgia Stuart | TRECIS | ||
Iman Rahbari | Campus Champions, ACCESS CSSN | ||
Jason Yalim | Campus Champions | ||
Katia Bulekova | ACCESS CSSN, Campus Champions, CAREERS, CCMNet, Northeast | ||
Laura Christopherson | Campus Champions, CCMNet | ||
shuai liu | ACCESS CSSN | ||
Mohsen Ahmadkhani | CCMNet, ACCESS CSSN | ||
Michael Puerrer | Campus Champions, Northeast | ||
Maryam Taeb | |||
Nannan Shan | CCMNet, ACCESS CSSN | ||
Mahmoud Parvizi | Campus Champions | ||
Paul Rulis | Campus Champions | ||
Rebecca Belshe | Campus Champions, CCMNet | ||
Russell Hofmann | ACCESS CSSN, CCMNet | ||
Xiaoqin Huang | ACCESS CSSN | ||
Swabir Silayi | ACCESS CSSN, CCMNet, Campus Champions | ||
Suhong Li | CAREERS, ACCESS CSSN | ||
Yun Shen | CAREERS, Northeast, ACCESS CSSN, CCMNet |
Title | Date |
---|---|
NSF requests research and education use cases for NAIRR | 02/22/24 |
Title | Category | Tags | Skill Level |
---|---|---|---|
Neurostars | Website | documentation, image-processing, data-sharing, psychology | Beginner, Intermediate, Advanced |
Paraview UArizona HPC links (advanced) | Docs | visualization | Intermediate, Advanced |
A guide to pip in Python | Learning | pip, software-installation | 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.
University of Southern California
Campus Champions, ACCESS CSSN
mentor, researcher/educator, research computing facilitator, research software engineer, cssn
Rice University
ACCESS CSSN
mentor, research computing facilitator, research software engineer, cssn
Austin Peay State University
Campus Champions, CCMNet
mentor, researcher/educator, research computing facilitator, CCMNet
Desert Research Institute
Campus Champions
research computing facilitator