CV

You can download my resume here.

您可以从这里下载我的中文简历。

Education

• Duke University, Durham, North Carolina, USA, 2023 -
  Ph.D. Candidate, Vision and Image Processing Lab, Department of Biomedical Engineering
  • GPA: 3.84/4.0
• Shanghai Jiao Tong University (SJTU), Shanghai, China, 2019 - 2023
  Bachelor’s Degree of Engineering, major in Biomedical Engineering, minor in Computer Science and Engineering
  • GPA: 3.84/4.3 (Top 10%)
  • See more on my final transcript

Scholar experience

• Aug. 2023 – Present: VIP Lab @ Duke directed by Sina Farsiu

  A knowledge-enhanced multi-modality clinical assessment system for integrative analysis of textual and visual features

  • Developed a multi-modality clinical assessment framework that leverages structured patient metadata and unstructured clinical notes to support clinical decision-making with large language models (LLMs).
  • Built an expert-curated medical knowledge base using Retrieval-Augmented Generation (RAG) to enhance domain-specific language comprehension and reasoning.
  • Integrated latent representations from imaging and textual modalities using Multi-Modal LLMs (MMLLMs) to uncover cross-modality relationships and improve diagnostic performance.
  • Submitted an abstract to ARVO 2026 and preparing a journal manuscript.

  A multi-granularity language learning approach to boost visual understanding

  • Proposed a novel contrastive learning framework that enables simultaneous multi-label and cross-granularity alignment.
  • Provided a set of multi-label, multi-granularity learning objectives to enhance their visual understanding.
  • Designed a structured multi-granular, multi-label system and construct large-scale multi-granular retinal and X-ray image-text datasets.
  • Under review of ICLR 2026 and available at arXiv.

  An Automated Quantitative Ulcer Analysis (AQUA) algorithm to classify Microbial keratitis (MK) organism types

  • Proposed a contrastive-learning-based method to extract robust features across different data patterns.
  • Developed a triple-stage multi-modality framework to integrate features of different modalities.
  • Expected to publish a journal article in 2026

• Dec. 2022 – Jun. 2023: IMIT @ SJTU directed by Lichi Zhang

  A 2D/3D Registration Method for Full-length Images of Lower Limbs

  • Constructed the first 2D-3D registration network for X-rays and CT images of full-length lower limbs
  • Adopted the shifted-window self-attention and the cross-attention mechanism for efficient feature extraction
  • Proposed SigmoidDiceLoss, which makes the registration of discrete labels continuous and differentiable

• June. 2022 – Nov. 2022: CCVL @ JHU directed by Alan Yuille & VLAA @ UCSC directed by Yuyin Zhou & Cihang Xie

  Multi-view MAE for 3D medical image representation learning

  • Presented the first multi-view pipeline for self-supervised medical image analysis
  • Achieved a comparable performance to the current state-of-the-art method with less training cost
  • Published in MICCAI 2023

• Feb. 2022 – Jan. 2023: Advanced MRI Lab @ SJTU directed by Hongjiang Wei

  Brain Region Segmentation and Age Estimation Using QSM

  • Created a novel network to segment several key brain areas on QSM images to improve brain age prediction
  • Improved brain age estimation compared to previous studies based on T1w MRI
  • Published in ISMRM 2023 and IEEE Journal of Biomedical and Health Informatics (JBHI)

Awards

• 2023 Oral Presentation at MICCAI 2023
• 2023 Outstanding Graduate of Shanghai Jiao Tong University
• 2022 Scholarship of School of Biomedical Engineering Alumni Association
• 2022 Merit Student of Shanghai Jiao Tong University
• 2021 Shanghai Municipal Government Scholarship
• 2020 Class A Scholarship of Shanghai Jiao Tong University

Skills

• Programming Languages: Python, C, C++, MatLab
• Deep Learning Frameworks: PyTorch, TensorFlow, Keras

Service

• Conference Reviewer MICCAI 2025; MICCAI 2024;
• Journal Reviewer Image and Vision Computing; IEEE Journal of Biomedical & Health Informatics (JBHI);

Publications

Z Li$^{1}$, Y Wang$^{1}$, S Farsiu, P Kinahan. Boosting Medical Visual Understanding From Multi-Granular Language Learning. arXiv preprint arXiv:2511.15943. 2025 Nov 20.

J Ong, M Lu, C Thanitcul, M Pawar, JN Hart, E Vogt, S Farsiu, Y Wang, P Dmitriev, A Gupta, N Nallasamy & MA Woodward. Automated Deep Learning Classification of the Quality of Slit-Lamp Photographs of Microbial Keratitis. Investigative Ophthalmology & Visual Science, 66(8), 4436-4436.

Z Yang, MA Woodward, LM Niziol, M Pawar, NV Prajna, A Krishnamoorthy, Y Wang, M Lu, S Selvaraj, & S Farsiu. Self-knowledge distillation-empowered directional connectivity transformer for microbial keratitis biomarkers segmentation on slit-lamp photography. Medical Image Analysis, 102, 103533.

M Chen¹, Y Wang¹, Y Shi¹, J Feng, R Feng, X Guan, … & H Wei. Brain Age Prediction Based on Quantitative Susceptibility Mapping Using the Segmentation Transformer. IEEE Journal of Biomedical and Health Informatics.

Y Wang¹, Z Li¹, J Mei¹, Z Wei¹, L Liu, C Wang, … & Y Zhou. SwinMM: Masked Multi-view with Swin Transformers for 3d Medical Image Segmentation. 2023 International Conference on Medical Image Computing and Computer-Assisted Intervention. (pp. 486-496). Cham: Springer Nature Switzerland.

Y Wang, Y Shi, H Wei. A Brain Age Estimation Network based on QSM using the Segment Transformer. 2023 International Society for Magnetic Resonance in Medicine (ISMRM).