Hi! I’m a third-year PhD student at the University of Washington, advised by Prof. Jenq-Neng Hwang and Prof. Linda Shapiro. Previously, I obtained my master’s degree from Sun Yat-sen University, supervised by Prof. Yao Lu.

My research centers on diffusion models for human motion generation and fine-grained video understanding, building encoders that distinguish subtle motion details across large and diverse datasets. I also work on medical image analysis, video grounding, and object detection, combining machine learning research with hands-on engineering experience in scalable data pipelines, real-time computer vision systems, software development, and databases.

News

• May 2026: I will be joining Amazon as an Applied Scientist Intern in Summer 2026.

Selected Publications

1. CLEP: Contrastive Language-Pose Pretraining
   Jia, S., Wang, H., Huang, H., An, Z., Hwang, J. N., Zhang, H., & Li, L.
   Accepted by CVPR 2026

2. Privacy-Preserving Radar-Based Italian Sign Language Recognition via ConvNeXt and Ensemble Learning
   Wang, H., Tan, Z., Jian, J. X., Zou, S., Huang, S., Chung, P. C., & Hwang, J. N.
   Accepted by CVPR 2026 Workshop

3. Detector-in-the-Loop Tracking: Active Memory Rectification for Stable Glottic Opening Localization
   Wang, H., Alattar, B., & Hwang, J. N.
   Accepted by MIDL: Medical Imaging with Deep Learning 2026

4. CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction
   Soraki, R., Wang, H., Elmore, J. G., & Shapiro, L.
   Accepted by MIDL: Medical Imaging with Deep Learning 2026

5. Concepts from Neurons: Building Interpretable Medical Image Diagnostic Models by Dissecting Opaque Neural Networks
   Gong, S., Wang, H., Zhang, X., & Dou, Q. (2025).
   International Conference on Information Processing in Medical Imaging (IPMI)

6. The Uncertainty of Boundary Can Improve the Classification Accuracy of BI-RADS 4A Ultrasound Image
   Wang, H., Hu, Y., Lu, Y., Zhou, J., & Guo, Y. (2022).
   Medical Physics

Working Experience

Research Assistant, University of Washington, Since 2023

Full-time Machine Learning Engineer, MicroPort MedBot Corporation, 2022-2023

Intern at the Precision Health Institution of GE, GE Healthcare Fall, 2020

Full-time Software Engineer, Perception Vision Medical Technologies 2017-2018

Teaching Experience

• EE 215: Fundamentals of Electrical Engineering
• EE 344: Data-Driven Modeling and Machine Learning
• EE 443: Machine Learning for Signal Processing Applications
• EE 596: Advanced Topics in Signal and Image Processing