I am currently a Full Professor in the School of Computer Science at Wuhan University.
Prior to joining Wuhan University, I was a Postdoctoral Researcher in the School of Computer Science at the University of Sydney, and subsequently a Research Fellow and Research Scientist in the College of Computing and Data Science at Nanyang Technological University, where I worked under the supervision of Prof. Dacheng Tao.
I received my Ph.D. degree from the University of Sydney in 2023, also under the supervision of Prof. Dacheng Tao.
Research Interests
My current research centers on the reasoning capabilities of large language models, with a particular emphasis on their applications in healthcare:
Medical Large Language Model
Efficient Reasoning
Graph-augmented Reasoning
Most Recent Publications by My Co-Supervised Students
Yifu Ding, Jiacheng Wang, Ge Yang, Yongcheng Jing, Jinyang Guo, Xianglong Liu, Dacheng Tao.
Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression.
In ICML 2026 (Spotlight).
Huazheng Wang, Yongcheng Jing, Haifeng Sun, Jingyu Wang, Jianxin Liao, Leszek Rutkowski, Dacheng Tao.
Bridging the Tokenizer Gap: Semantics- and Distribution-Aware Knowledge Transfer for Unbiased Cross-Tokenizer Distillation.
In AAAI 2026 (Oral).
Weixiao Zhan, Yongcheng Jing, Leszek Rutkowski, Dacheng Tao.
Distillation Traps and Guards: A Calibration Knob for LLM Distillability.
In ACL 2026.
Huazheng Wang, Yongcheng Jing, Haifeng Sun, Jingyu Wang, Jianxin Liao, Dacheng Tao.
Erasing without Remembering: Safeguarding Knowledge Forgetting in Large Language Models.
In ACL 2026.
Yu Zhao, Hao Guan, Yongcheng Jing, Ying Zhang, Dacheng Tao.
MedCoG: Maximizing LLM Inference Density in Medical Reasoning via Meta-Cognitive Regulation.
In ICML 2026.
Wenbin Wang, Yongcheng Jing, Liang Ding, Yingjie Wang, Li Shen, Yong Luo, Bo Du, Dacheng Tao.
Retrieval-Augmented Perception: High-resolution Image Perception Meets Visual RAG.
In ICML 2025 (Oral).
First-Author Publications
Yongcheng Jing, et al.
Deep Graph Mating.
In NeurIPS 2024.
Yongcheng Jing, et al.
Deep Graph Reprogramming.
In CVPR 2023 (Highlight, Top 2.5% of 9155 Submissions).
Yongcheng Jing, et al.
Learning Graph Neural Networks for Image Style Transfer.
In ECCV 2022.
[Code]
Yongcheng Jing, et al.
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks.
In ICCV 2021.
Yongcheng Jing, et al.
Amalgamating Knowledge from Heterogeneous Graph Neural Networks.
In CVPR 2021 (Oral, Top 4.2% of 7015 Submissions).
[Code]
Yongcheng Jing, et al.
Turning Frequency to Resolution: Video Super-resolution via Event Cameras.
In CVPR 2021.
Yongcheng Jing, et al.
Dynamic Instance Normalization for Arbitrary Style Transfer.
In AAAI 2020 (Oral, Top 4.5% of 7737 Submissions).
[Code]
Yongcheng Jing, et al.
Neural Style Transfer: A Review.
In TVCG 2019 (Ranked as the most-cited article among all TVCG publications since 2019 on Scopus; ESI Top 1% Highly Cited; TVCG Popular Article).
[Code]
Yongcheng Jing, et al.
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields.
In ECCV 2018.
[Code]
Academic Activities
Area Chair: ACM MM (CCF A)
Journal Editorial Board: IET Signal Processing (CCF C)
Program Committee: MICCAI, CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, AAAI, IJCAI, MM, KDD, UAI, Eurographics, etc.
