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What is the most surprising finding in your research?

  • MedPodGPT Research: It is surprising to see that leveraging medical podcast data into LLM significantly boosts its ability to process medical information, making it an invaluable tool for education and research [1].
  • Image Quality Assessment Research: Mimicking the long-dependency perception of Human Visual System, the Non-local Modeling enhances image quality assessment [1].
  • EEG Research: By cooperating with the functional topological relationship of electrodes, the decoding performance of raw EEG signals is improved [1, 2].

Shuyue's github activity graph

Fast access to some projects

Source codes of all my works will be shared on GitHub, the trained models and datasets will be released on ๐Ÿค— Hugging Face, and some of my applied research on Vision and Language will be developed as Web or Android/iOS mobile apps.

Boston University: mostly Multimodal Foundation Models and Generative AI research

Topic 1: Large Language Models (LLMs):


Topic 2: Large Multimodal Models (LMMs/MM-LLMs): We will be deploying all our Multimodal Foundation Models on our MedPodGPT platform.


Topic 3: Medical Imaging with AI:

  • ๐Ÿ–ผ๏ธ ๐Ž๐ง๐ฅ๐ข๐ง๐ž ๐Œ๐ž๐๐ข๐œ๐š๐ฅ ๐ˆ๐ฆ๐š๐ ๐ข๐ง๐  ๐•๐ข๐ฌ๐ฎ๐š๐ฅ๐ข๐ณ๐ž๐ซ - Online Medical Imaging Visualizer (based on Papaya)

City University of Hong Kong: mostly Computer Vision research

Northeast Electric Power University: mostly Deep Learning research

Philips Research: mostly Natural Language Processing research

Others:

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