VLDB 2026 Research / reviewers in the wild / expert
Hongru Sun
dblp:203/2139
· DBLP profile ↗
4ranked-venue papers
2as first author
2since 2021 · last 2026
0000-0003-2605-0594ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Reinforcement learning · 56% Language models and text generation · 44% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › mathematical reasoning
mathematical problem solving |
1.0 | 1 | 2026 | FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner · ACL (1) 2026 |
Machine learning › Reinforcement learning › reinforcement learning for NLP
reinforcement learning for language models |
1.0 | 1 | 2026 | A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions · ACL (1) 2026 |
Machine learning › Reinforcement learning › sample efficiency
sample-efficient reinforcement learning |
1.0 | 1 | 2026 | A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions · ACL (1) 2026 |
Natural language and speech › Language models and text generation › model steering
language model steering |
0.3 | 1 | 2026 | FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner · ACL (1) 2026 |
Natural language and speech › Language models and text generation
large language model training |
0.3 | 1 | 2026 | A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions · ACL (1) 2026 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 2.0fuzzy logic · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted ReasonerabstractHao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Hao Wu 0094, Hongru Sun, Wanqing Li 0001, Xinguo Yu, Hao Ming 0001, Xiao Luo 0001, Wenbin Zhang 0002, Jiahong Zhao, Yi Guo 0001, Jie Yang 0009 |
ACL (1) | 2 |
| 2026 | A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and SolutionsabstractZhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo 0002, Chunchun Chen, Yunhui Liu 0002, Hongru Sun, Yatao Bian, Ming Zhang 0004, Tieke He, Jie Yang 0009, Guanjie Zheng, Zhonghai Wu, Bo Zhang 0069, Lei Bai 0020, Xiao Luo 0001 |
ACL (1) | 8 |
| 2018 | Robust Magnetic Resonant Beamforming for Secured Wireless Power TransferabstractWireless power transfer (WPT) is an emerging and promising technique for power supplies to mobile and portable devices. Among all approaches, magnetic resonant coupling (MRC) is an excellent one for midrange WPT, which provides high mobility, flexibility, and convenience due to its simplicity in hardware implementation and longer transmission distances. In this letter, we consider an MRC-WPT system with multiple power transmitters, one intended power receiver and multiple unintended power receivers. The optimal robust beamforming design of the complex transmit currents is investigated to achieve the minimal total source power with the worst-case mutual inductances measurement, whereas the unintended receiving powers are constrained by certain bounds. Numerical results demonstrate that the proposed algorithm can significantly improve the performance and the robustness of the MRC-WPT systems. Hongru Sun, Fengchao Zhu, Hai Lin 0001, Feifei Gao 0001 |
IEEE Signal Process. Lett. | 1 |
| 2017 | Magnetic Resonant Beamforming for Secured Wireless Power TransferabstractMagnetic resonance coupling (MRC) has been utilized in wireless power transfer (WPT) to achieve mid-range contactless power supply. However, unintended users might also draw power from the transmission devices. In this letter, an MRC-WPT system with multiple power transmitters, one intended power receiver, and one unintended power receiver is investigated. We formulate a power security problem by limiting the unintended receiving power and, at the same time, maximizing the power of the intended user. Such an optimization problem is in general nonconvex. Nevertheless, a global optimal solution can be efficiently achieved with the aid of semidefinite relaxation approach. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. Hongru Sun, Hai Lin 0001, Fengchao Zhu, Feifei Gao 0001 |
IEEE Signal Process. Lett. | 1 |