Research
Research Focus
My research focuses on developing robust and generalizable machine learning systems for ubiquitous sensing and wearable technology.
Research Directions
🤖 LLM-Driven Multimodal Sensing Agents
Exploring the synergy between large language models (LLMs) and multimodal sensor data to build context-aware agents that can: - Reason about human activities - Adapt to novel scenarios - Provide personalized assistance🔄 Adaptive Continual Learning for HAR
Designing algorithms that enable models to incrementally learn new user behaviors without catastrophic forgetting: - Meta-learning approaches - Dynamic memory architectures - Long-term personalization🎯 Domain Generalization for Sensor-Based HAR
Enhancing cross-user HAR accuracy through: - Meta-feature alignment - Feature disentanglement - Seasonal style projectionProjects
Cross-Domain Generalization for Sensor-Based Human Activity Recognition
Timeline: Jun. 2025
- Developed a PyTorch-based domain generalization framework integrating meta-learning and feature disentanglement
- Validated on multiple public benchmarks, achieving state-of-the-art robustness against distribution shifts
Lightweight Large Language Model Implementation & Training
Timeline: Feb. 2026
- Built a 26M-parameter LLaMA-style LLM from scratch
- Implemented core Transformer components (Multi-Head Attention, RMSNorm, RoPE)
- Covered pre-training, SFT, LoRA fine-tuning, and vLLM deployment
Privacy-Preserving ML Inference via Garbled Circuits
Timeline: Jun. 2021
- Led the design of a privacy-preserving neural network inference framework
- Combined garbled circuits with PyTorch for secure, high-fidelity inference
Collaboration
I'm open to collaborations in:
- Human Activity Recognition
- Domain Generalization
- LLM applications