We investigated the effectiveness of test-time adaptation for child speech recognition, with the goal of enabling continuous, unsupervised adaptation at test time.

We conducted a systematic review of AI-enabled mobile apps for pediatric speech therapy

We developed an open-source learning module for more accessible and engaging AI education

We introduced HRIBench, a visual question-answering (VQA) benchmark designed to evaluate VLMs across a diverse set of human perceptual tasks critical for HRI.

We formulate a causal tree-based method to calculate exercise difficulty based on the user's performance.

We designed an algorithm to generate trajectories for users to rank that we call Covariance Matrix Adaptation Evolution Strategies with Information Gain (CMA-ES-IG).

We aim to conduct a brief survey on the use of LLMs in SAR technologies, and discuss the potentials and risks of applying LLMs to the following three major technical challenges of SAR.

We proposed novel methods to enable fairness-aware data distillation for human-centered attribute classification

We proposed novel methods to remove privacy-related attributes information from multi-modality human-centered data

We evaluated and personalized user-perceived quality of text-to-speech voices for delivering mindfulness meditation with different physical embodiments.

This work designed and validated personalized models of arousal and valence for children with ASD using a multi-session in-home dataset of SAR interventions.

We applied supervised machine-learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children on the autism spectrum.

Personalized Socially Assistive Robots With End-to-End Speech-Language Models For Well-Being Support

We presented a user-informed design process to validate the motivation and identify major design principles for developing SAR for long-term LVR.