My research integrates computational linguistics and machine learning to build human-centered AI systems. These projects demonstrate the intersection of domain expertise in linguistics, cognitive science, and speech processing with modern ML/AI techniques.
Leading the development of a clinically-validated AI pipeline for managing dementia-related agitation through therapeutic conversational interactions. This project creates the first AI system that combines real-time emotion recognition with expert-designed therapeutic response generation for the 55+ million people worldwide living with dementia.
Systematic analysis of emotion recognition in conversation (ERC), moving beyond "black box" accuracy to understand how models work. Achieved state-of-the-art text-only performance on IEMOCAP using strictly causal context, surpassing prior methods that exploit future utterances.
Investigating Direct Preference Optimization (DPO) for training language models to generate empathetic responses in healthcare contexts. Developing annotation frameworks for preference data collection.
Contributed to MultiCAT, a comprehensive annotation framework for multimodal team communication, published at NAACL 2025. Developed annotation schemas for verbal and non-verbal communication patterns in collaborative settings.
Applied automated acoustic analysis to 100,000+ tokens from Korean broadcast speech, demonstrating how speakers hyperarticulate phonetic cues in lexically confusable contexts. This work bridges corpus linguistics with speech technology.
Python, R, C++, Bash
PyTorch, TensorFlow, HuggingFace Transformers, Scikit-learn
LLMs (GPT-4, Claude), Whisper, wav2vec2, BERT/RoBERTa, Praat
MoveNet, OpenCV, Pose Estimation
NVIDIA GPUs (Academic Grant), Saturn Cloud, Azure Cognitive Services
Experimental design, Statistical analysis, IRR, Clinical validation