Yuliang(Leo) Chen

I am a PhD student in the Department of Computer Science at Dartmouth College, advised by Prof. Andrew Campbell. Previously, I completed my M.S. in Data Science at the University of California, San Diego, where I worked with Prof. Jingjing Zou and Prof. Tauhidur Rahman.

I am interested in using large language models, multimodal models, and agent systems for health data analysis. My focus is on developing methods that enable these models to handle continuous and noisy wearable sensor streams to infer health outcomes.

You can find my CV here: Yuliang Chen’s Curriculum Vitae.

Email / Github / LinkedIn

Publication

Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals. Yunfei Luo, Yuliang Chen (Co-first author), Asif Salekin, Tauhidur Rahman.

MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements. Howon Ryu, Yuliang Chen (Co-first author), Yacun Wang, Andrea Z. LaCroix, Loki Natarajan, Yu Wang, Jingjing Zou.

Projects

Vivid Panels: Deep Neural Networks for Manga Colorization

Spring 2024
Explored fine-tuning pre-trained GAN-based models for manga colorization, highlighting the performance gains achieved by addressing distribution differences between task-specific inputs and pre-training data.

VitT: Vision-Topological Transformer for Medical Image Classification

Spring 2024
Introduced the VitT model, strengthening Vision Transformer (ViT) with lightweight task-specific encoder and fusion layer, resulting in better performance with minimal computational cost.

E-StyTR^2: Efficient Image Style Transfer with Transformers

Spring 2024
Investigated various fusion modules based on StyTr2 to effectively blend style and content, evaluating their efficiency and aesthetic quality using quantitative metrics.

Image-to-Image Retrieval with CLIP

Winter 2024
Developed an image-to-image retrieval system using CLIP, demonstrating its superior ability to capture robust and generalized image representations compared to traditional CNNs like ResNet.

Navigating the Landscape of Explanation Multiplicity

Fall 2023
Investigated the challenges and strategies related to explanation multiplicity in machine learning models, highlighting the implications of multiple explanations for model decisions and developing effective methods to manage them.