Health AI · Multimodal Deep Learning
My research sits at the intersection of Health AI and multimodal deep learning: particularly building models that can reason jointly over video, sensor data, and text, and solving real clinical problems, especially in Neonatology. I did my PhD at the University of Wisconsin-Madison, where I was fortunate to be advised by Prof. Yin Li.
I have also gained valuable research experience through internships at Amazon Science, Microsoft Research and EMC.
My research is driven by problems in Health AI and Biomedicine, and by the multimodal deep learning needed to solve them. I build models that learn from sequential data such as video, sensor data (time series), and text, and I am especially interested in how Vision Language Models and Large Language Models can reason over these signals.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings 2026
A lightweight RGB-IR fusion module for multimodal large language models that enables robust visual reasoning under visual degradations like blur, low-light, and fog.
International Conference on Machine Learning (ICML) 2025
A novel time series forecasting method that combines principles from nonlinear dynamical systems with deep learning to model latent temporal structure for accurate forecasts.
European Conference on Computer Vision (ECCV) 2024
Action quality assessment in videos by incorporating human designed scoring rubrics while providing calibrated uncertainty estimates.
Nature - Journal of Perinatology Nov 2024
Automatically detect the glottic opening during neonatal intubation using video laryngoscopy to improve intubation outcomes.
npj Digital Medicine June 2024
Automatically quantify care manipulation activities in neonatal intensive care units (NICUs), while integrating physiological signal data to monitor neonatal stress in NICUs.
Preprint 2022
Assess perceptual quality of videos encoded by Machine Learning-based Video Codecs.
EPIC @ CVPR Workshop 2022
Won 2nd place in the Egocentric action detection challenge
