Licheng Zong(宗理成)

Ph.D. Candidate
Artificial Intelligence in Healthcare Group (AIH)
Department of Computer Science and Engineering (CSE)
The Chinese University of Hong Kong (CUHK)
Office: Rm 1026, Ho Sin-Hang Engineering Building, CUHK
Shatin, N.T., Hong Kong SAR

Email: lczong21@cse.cuhk.edu.hk

Google Scholar  /  CV  /  LinkedIn  /  GitHub

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Research Interests
I am working at the intersection between Deep Learning and Bioinformatics, including:
- Computational Methodology in Bioinformatics
- Healthcare-Related applications of Deep Learning
- Electronic Health Record Analysis
- Nanopore Sequencing Data Analysis
- GNN in Drug Discovery


Short Bio
I started to pursue my Ph.D. degree in the Department of Computer Science and Engineering, The Chinese University of Hong Kong (CUHK-CSE) from Aug 2021.
I'm a Member of AIH Group, advised by Prof. Yu Li.
I obtained my Bachelor degree in Automation Science and Technology at Xi'an Jiaotong University in July 2021.
I graduated from Hefei No.1 School, a fantastic High School in July 2014.


Education
Ph.D. in Computer Science and Engineering 2021-2025
Chinese University of Hong Kong | Hong Kong SAR
B.E. in Automation Science and Technology 2017-2021
Xi'an Jiaotong University | Xi'an, Shaanxi
Experience
Research Intern | SmartMore Corporation | Shenzhen, Guangdong Jul 2020 - May 2021
Research Assistant | Brown University | Providence, Rhode Island Mar 2020 - Mar 2021
Research Assistant | Xi'an Jiaotong University | Xi'an, Shaanxi Sep 2019 - Jun 2020
Research Assistant | Institute of Automation, CAS | Beijing Aug 2019 - Feb 2020
Research Assistant | National University of Singapore | Singapore Jul 2019 - Aug 2019
Publications
Protein-RNA interaction prediction with deep learning: Structure matters Protein-RNA interaction prediction with deep learning: Structure matters
Junkang Wei*, Siyuan Chen*, Licheng Zong*, Xin Gao#, Yu Li#
ArXiv Preprint, 2021
bibtex

We give a thorough review of Protein-RNA interactions, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features, and models.

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis Self-supervised contrastive learning for integrative single cell RNA-seq data analysis
Wenkai Han*, Yuqi Cheng*, Jiayang Chen*, Huawen Zhong, Zhihang Hu, Siyuan Chen, Licheng Zong, Irwin King, Xin Gao#, Yu Li#
bioXiv Preprint, 2021
bibtex

We present a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and the downstream analysis. CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events.

Model Adaption Object Detection System for Robot Model Adaption Object Detection System for Robot
Jingwen Fu, Licheng Zong, Yinbing Li, Ke Li, Bingqian Yang, Xibei Liu
39th Chinese Control Conference (CCC), 2020
bibtex

We propose a new vision system for robots, the model adaptation object detection system. Instead of using a single one to solve problems, we made use of different object detection neural networks to guide the robot in accordance with various situations, with the help of a meta neural network to allocate the object detection neural networks.

Honors and Awards
Execellent Graduate | Xi'an Jiaotong University 2021
Execellent Student | Xi'an Jiaotong University 2020
First Prize | National College Robot Competition (ROBOCON) 2019
Meritorious Winner | Interdisciplinary Contest In Modeling 2019
Teaching
TA - BMEG3105: Data Analytics for Personalized Genomics and Precision Medicine Fall 2021
Recent News

--- July 2021, going to be the TA of BMEG3105.

--- July 2021, put our RBP Review on ArXiv.

--- July 2021, put our CLEAR Paper on bioXiv.

--- July 2021, graduated from Xi'an Jiaotong University!

--- May 2021, decided to pursue a Ph.D. in Computer Science and Engineering at CUHK!

--- Mar 2021, won the Execellent Graduate of Xi'an Jiaotong University.



Updated Aug 2021

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