Li, Xiang (李响)
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Master and R&D Engineer, |
About me
I am an ordinary male college student. My undergraduate degree is in software engineering at HUST, and I am currently pursuing a master's degree in computer technology at the University of Electronic Science and Technology of China.
I focus on using AI to solve practical problems.
URL: https://zhuanlan.zhihu.com/p/467678730
Research
My research interests include:
Deep Learning
Computer Vision
Machine Learning System Development
Transfer Learning
Java and Web Design
Current work
Reinforcement Learning for Knee Osteoarthritis Prediction
Temporal Neural Network for Knee Osteoarthritis Prediction
Expert Neural Network for for Recommendation
Federal Learning for Recommendation
Under review
Jinke Wang*, Xiang Li. "EE-UNet: extended EfficientNet-based U-Net for joint optic disc and cup segmentation in the fundus image".
Recent publications
Jinke Wang, Xiang Li, and Changfa Shi*. "SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image", Computational and Mathematical Methods in Medicine, Jun. 2022, 195, pp. 116595. (IF = 6.954) [pdf][code]
Note: * indicates the corresponding author.
Full list of publications in Google Scholar.
Academic service
Reviewer
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Projects
Advertising Platform Development, 01.2022-Present
Provide advertising strategies and solutions for advertisers to maximize revenue
Provide automated advertising instead of manual selection
Use users' history information to build their profiles, and then select the target users
Campus Recommender System, 03.2021-12.2021
Built user profiles based on the data crawled from websites
Recommended information, such as courses from MOOC, and publications from Arxiv, to students
Recommended information from within and outside the university based on faculty research, courses taught, and interests
Online Education Explainable Recommender System, NSFC, 06.2018-12.2018
Summarized over 500,000 exercises and classified their knowledge points from all subjects
Applied matrix factorization for online learning and recommendation of exercises based on interaction of users
Added latent features learned by neural networks from exercises to online matrix factorization for better performance
Development of Memorizing Words APP, 06.2017-02.2018
Extracted the records of memorizing words of over 100,000 users from a database
Counted the pairs of error words with the co-occurrence rate to obtain a co-occurrence table
Provided words, along with situation pictures, to enhance memory and showed co-occurrence words from a table