Papers by Sung-Ju Lee
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning (2023.emnlp-main)
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| Challenge: | Existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via smartphones due to data privacy concerns. |
| Approach: | They propose a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. |
| Outcome: | The proposed system achieves 0.15 AUROC improvement and 8.21% MAE reduction in self-reported depression, stress, anxiety, and mood from 46 participants. |
ConSensus: Multi-Agent Collaboration for Multimodal Sensing (2026.findings-acl)
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| Challenge: | Large language models are increasingly grounded in sensor data to perceive and reason about human physiology and the physical world. |
| Approach: | They propose a training-free multi-agent collaboration framework that decomposes multimodal sensing tasks into specialized, modality-aware agents. |
| Outcome: | The proposed framework matches or exceeds debate methods on multimodal sensing benchmarks while achieving 12.7 times reduction in token cost. |
By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting (2024.emnlp-main)
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| Challenge: | Existing text-based prompts for large language models (LLMs) show performance degradation when handling long sensor data sequences. |
| Approach: | They propose a visual prompt that directs MLLMs to utilize visualized sensor data alongside descriptions of the target sensory task. |
| Outcome: | The proposed approach achieves 10% higher accuracy and reduces token costs by 15.8 times on nine sensory tasks involving four sensing modalities . |