Professor Yongcheng Jing joined the School of Computer Science at Wuhan University in 2026 as a Full Professor and Ph.D. Supervisor. He has been selected for a national-level high-caliber young talent program. His long-term research focuses on efficient inference methods for Large Language Models (LLMs). He is currently working with Professor Bo Du's research team on interdisciplinary research at the intersection of artificial intelligence and healthcare.

From 2020 to 2023, he pursued his Ph.D. at the University of Sydney, Australia, followed by postdoctoral research appointments at the University of Sydney and Nanyang Technological University (NTU). Throughout his doctoral and postdoctoral studies, he was supervised by Professor Dacheng Tao, a Fellow of the Australian Academy of Science.

From 2016 to 2019, he pursued his Master's degree at the College of Computer Science and Technology, Zhejiang University, under the supervision of Professors Mingli Song and Yizhou Yu. His Master's thesis received the Zhejiang Province Outstanding Master's Thesis Award, ranking first within Zhejiang University's College of Computer Science and Technology and being the only thesis from the college selected for this distinction. From 2013 to 2016, he pursued his Bachelor's degree in the English-Taught Joint Program in Computer Science at South China University of Technology, graduating one year ahead of schedule.

His most highly cited first-author paper has received over 1,000 citations, and four of his first-author papers have each accumulated more than 100 citations. His research contributions have received positive recognition from more than 60 academicians and Fellows. He has received several prestigious honors and awards, including the National Outstanding Overseas Student Award from the Ministry of Education of China (awarded to only 650 recipients annually across all disciplines worldwide), the CVPR Doctoral Consortium Award at CVPR (a CCF-A conference), and the AI 2000 Most Influential Scholar Honorable Mention by Tsinghua University's AMiner for two consecutive years.

Publications Selected for Oral / Spotlight / Hightlight：

1. 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).

2. 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).

3. 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).

4. Yongcheng Jing, Chongbin Yuan, Li Ju, Yiding Yang, Xinchao Wang, Dacheng Tao. Deep Graph Reprogramming. In CVPR 2023 (Hightlight).

5. Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, Dacheng Tao. Amalgamating Knowledge from Heterogeneous Graph Neural Networks. In CVPR 2021 (Oral).

6. Yongcheng Jing, Xiao Liu, Yukang Ding, Xinchao Wang, Errui Ding, Mingli Song, Shilei Wen. Dynamic Instance Normalization for Arbitrary Style Transfer. In AAAI 2020 (Oral).

2026 Publications in Top Venues:

1. 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).

2. 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).

3. Weixiao Zhan, Yongcheng Jing, Leszek Rutkowski, Dacheng Tao. Distillation Traps and Guards: A Calibration Knob for LLM Distillability. In ACL 2026.

4. 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.

5. 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.

6. Yibo Wang, Yongcheng Jing, Shunyu Liu, Hao Guan, Rong-Cheng Tu, Chengyu Wang, Jun Huang, Dacheng Tao. VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning. In EMNLP 2026.

7. Xiaohui Zhao, Xiangyu Meng, Jinjin Chi, Yongcheng Jing, Yiming Wang, Yibing Zhan, Hang Sun, Dacheng Tao. PeriBench: A Protocol-Aware Benchmark for Dynamic Perioperative Risk Prediction from Numerical Models to Language Interfaces. In EMNLP 2026.

8. Yifu Ding, Yongcheng Jing, Chengyu Wang, Jun Huang, Dacheng Tao. Orchestrating Hidden-Intermediate Pruning-and-Distill for MoEs Slimming. In EMNLP 2026 (Finding).

9. Yingjie Wang, Yutian Zhou, Shi Fu, Yuzhu Chen, Yongcheng Jing, Leszek Rutkowski, Dacheng Tao. Towards a Theoretical Understanding of In-context Learning: Stability and Non-I.I.D Generalisation. In ICLR 2026.

10. Wenhao Sun, Rong-Cheng Tu, Yifu Ding, Zhao Jin, Jingyi Liao, Yongcheng Jing, Dacheng Tao. Singular Proxies for Adaptive Caching in Diffusion Language Models. In ICML 2026.

11. Jinjin Chi, Taoping Liu, Mengtao Yin, Ximing Li, Yongcheng Jing, Jialie Shen, Leszek Rutkowski, Dacheng Tao. Disentangled Representation Learning via Flow Matching. In NeurIPS 2026.

12. Junjie Zhang, Guozheng Ma, Shunyu Liu, Zetian Hu, Yongcheng Jing, Ting-En Lin, Yongbin Li, Dacheng Tao. STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning. In NeurIPS 2026.

13. Mingyang Yu, Rong-Cheng Tu, Yifu Ding, Hanqing Zhao, Yongcheng Jing, Xiao Luo, Dacheng Tao. LAPrune: Logits-Aligned Scoring Proxy for KV Pruning via Vector Quantization. In NeurIPS 2026.

14. Siyuan Liang, Tianmeng Fang, Yongcheng Jing, Hanqing Zhao, Jiaxing Huang, Dacheng Tao. TAME-MA: Taming Mobile Agents via Trajectory-Level Trustworthiness Under Jailbreak Manipulations. In IJCV 2026.

15. Shengkai Hu, Jiaqi Ma, Xu Zhang, Yongcheng Jing, Lefei Zhang, Jun Wan. ClusIR: Towards Cluster-Guided All-in-One Image Restoration. In ACM MM 2026.