most citedPredicting and Understanding College Student Mental Health with Interpretable Machine Learning

15 citations · 16 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2026

BRIDGE-EEG: Bridging Self-Supervised Pretraining and Efficient Deployment for Cross-Dataset EEG Classification

Meghna Roy Chowdhury, Chengwei Zhou, Haotian Yu +2

The growing use of electroencephalography (EEG) motivates automated analysis that is accurate, transferable, and deployable on constrained hardware. Recent EEG foundation models le…

cs.LG2026

From Muscle to Text with MyoText: sEMG to Text via Finger Classification and Transformer-Based Decoding

Meghna Roy Chowdhury, Shreyas Sen, Yi Ding

Surface electromyography (sEMG) provides a direct neural interface for decoding muscle activity and offers a promising foundation for keyboard-free text input in wearable and mixed…

cs.ET2025

Design-Space Exploration of Distributed Neural Networks in Low-Power Wearable Nodes

Meghna Roy Chowdhury, Ming-che Li, Archisman Ghosh +2

Wearable devices increasingly support intelligent applications such as augmented reality, continuous health monitoring, and vision-based interaction. However, the neural networks (…

cs.LG2025★ 15 cited

Predicting and Understanding College Student Mental Health with Interpretable Machine Learning

Meghna Roy Chowdhury, Wei Xuan, Shreyas Sen +2

Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental he…

cs.CY2025★ 1 cited

Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors

Wei Xuan, Meghna Roy Chowdhury, Yi Ding +1

The global mental health crisis is a pressing concern, with college students particularly vulnerable to rising mental health disorders. The widespread use of smartphones among youn…