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data-analysis

Mentors and Regional Facilitators
Name Region Skills Interests
Andrew Fullard Campus Champions
Alyssa Pivirotto ACCESS CSSN, Campus Champions
Alana Romanella Campus Champions
Craig Gross Campus Champions
Balamurugan Desinghu ACCESS CSSN, Campus Champions, CAREERS, Northeast
diana Trotman CAREERS
Deborah Penchoff Campus Champions
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, Northeast, CCMNet
Laura Christopherson Campus Champions, CCMNet
shuai liu ACCESS CSSN
Mohsen Ahmadkhani CCMNet, ACCESS CSSN
Michael Puerrer Campus Champions, Northeast
Maryam Taeb
Mahmoud Parvizi Campus Champions
Paul Rulis Campus Champions
Rebecca Belshe Campus Champions, CCMNet
Russell Hofmann ACCESS CSSN
Xiaoqin Huang ACCESS CSSN
Suhong Li CAREERS, ACCESS CSSN
Swabir Silayi Campus Champions
Yun Shen CAREERS, Northeast, ACCESS CSSN, CCMNet

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Engagements

Investigation of robustness of state of the art methods for anxiety detection in real-world conditions
University of Illinois at Urbana-Champaign

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.

Status: Complete

People with Expertise

Yuming Ding

University of Rhode Island

Programs

CAREERS

Roles

student-facilitator

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Expertise

Kyle Randall

Programs

ACCESS CSSN

Roles

student-facilitator

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Expertise

Alexandra Lamtyugina

University of Chicago

Programs

ACCESS CSSN

Roles

student-facilitator

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Expertise

People with Interest

Sue Oldenburg

Rutgers University

Programs

Campus Champions

Roles

research computing facilitator, Affinity Group Leader

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Joshua Winchell

Texas A&M University

Programs

ACCESS CSSN, CCMNet

Roles

CCMNet

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Interests

Xilin Qiao

Programs

ACCESS CSSN

Roles

student-facilitator

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Interests

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