Smart Digital Health Lab
Our group works at the intersection of AI for Science, digital health, and computational precision medicine. My group develops Artificial Intelligence (AI) Models for proteomics, medical image analysis, multimodal biomedical modeling, and medical time-series learning, with a particular interest in reliable diagnosis, prognosis, and personalized prediction.
I am actively recruiting self-motivated PhD students, research assistants, interns, visiting students, and undergraduate researchers. Strong applicants may come from computer science, biomedical engineering, statistics, applied mathematics, or related fields.
I am particularly interested in working with students who want to build rigorous machine learning methods and apply them to important biomedical or clinical problems. Prior experience in one or more of the following is especially useful: deep learning, multimodal learning, generative models, medical imaging, sequence modeling, time-series analysis, optimization, or scientific computing.
Applicants with prior experience in AI and publications in top-tier conferences or journals will be given priority consideration.
The laboratory, in collaboration with partner institutions, has access to several dozen H100 GPUs, providing ample computational resources to support large-scale model training for students.
Send the application to PI through email directly.
Prospective PhD / RA / Intern + your name + Bachelor University Name + area of interest.
CV with GPA or Ranking information, transcript, and a research proposal describing your background, interests, and why you want to join the lab.
Awards (National Scholarship), Representative paper, thesis, code repository, competition result, or project page.
For PhD applicants, please also consult the PolyU Graduate School fellowship and scholarship schemes.
Emma, Shujun Wang
Assistant Professor
The Hong Kong Polytechnic University
Office: ST421a
Email: shu-jun.wang@polyu.edu.hk
I am happy to discuss research collaborations, student supervision, invited talks, workshops, and interdisciplinary projects at the interface of AI and healthcare.