· Personal Information
E-Mail:
Contact Information:liandefu@ustc.edu.cn
Professional Title:特任教授
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates
Discipline: Computer Science and TechnologyAcademic Honor: 2020 National outstanding youth fund winner
· Other Contact Information:
Email:
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· Personal Profile
Defu Lian is a professor from University of Science and Technology of China. His main research interest lies in data mining and deep learning. He has published more than 160 papers at prestigious conferences and journals, and received a best paper runner-up in APWeb 2016, best paper candidate in WWW 2021 and best paper award in WISE 2022. He developed a highly-modularized recommender system (Re...
· Education Experience
- 2009.9 ~ 2014.6
- University of Science and Technology of China - Computer Applications Technology - Doctoral Degree in Engineering - With Certificate of Graduation for Doctorate Study
- 2005.9 ~ 2009.6
- University of Science and Technology of China - Computer Science and Technology - Bachelor's Degree - University graduated
· Work Experience
- 2018.10-2022.12
- University of Science and Technology of China - Research Professor, Professor
- 2014.7-2018.10
- University of Electronic Science and Technology of China - Lecture, Associate Professor
· Social Affiliations
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· Research Focus
· Research Group
· Name of Research Group:Learned Index Group
Description of Research Group:Investigate Machine Learning for index building, supporting retrieval of low-dimensional and high-dimensional data.
· Name of Research Group:AI Foundation Resear
Description of Research Group:Investigate AI foundation algorithms, including continual learning, causal machine learning, robust machine learning, online deep learning.
· Name of Research Group:Recommender System G
Description of Research Group:Investigate model design, training object, optimization algorithm, efficient recommendation algorithm
· Name of Research Group:Time Series Group
Description of Research Group:Investigate detection and forecasting of time series data.