孔树锋 (Shufeng Kong)

central south university副教授

H指数:10

被引数:452

个人简介

"孔树锋，副教授，硕士生导师，中山大学百人计划青年学术骨干。2018年10月毕业于澳大利亚悉尼科技大学工程与信息技术学院，获博士学位。分别于美国加州大学尔湾分校 (2017.11—2018.05) 、美国康奈尔大学 (2018.10—2020.09) 和新加坡南洋理工大学 (2020.10—2022.08) 从事科研工作, 并于2022年9月加盟中山大学软件工程学院。\n本人的研究内容主要包括多智能体系统、深度学习、强化学习、自动推理与规划、约束优化、AI for Science、多标签分类、多目标回归、大语言模型等。\n本人目前已在Nature Communications、NeurIPS、ICML、AAAI、IJCAI、CP和AAMAS等国际顶尖期刊和会议上发表学术论文20余篇，并参与国内外重大科研项目多项。本人长期担任多个期刊和会议（NeurIPS、ICML、ICLR、AAAI、IJCAI和TNNLS等）的审稿人。\n本人亦长期与康奈尔大学Carla Gomes教授团队(https://www.cs.cornell.edu/gomes/) 、南洋理工大学安波教授团队(https://personal.ntu.edu.sg/boan/) 和悉尼科技大学李三江教授团队（https://profiles.uts.edu.au/Sanjiang.Li）保持良好合作关系。另外个人主页请参见 (https://sk2299.github.io)。欢迎有意向合作的单位或个人来信联系，也欢迎有意攻读我院研究生的同学或有意来中山大学从事博士后工作的同仁与我联系。\n研究团队\n中山大学区块链与智能金融研究中心\n大语言模型的高效适配、可控生成、生成内容的质量评估方法研究等\nInstitue of AI for Science, Cornell University\n大规模组合优化（应用于渔业管理优化、作业调度等）、大规模种群分布预测（应用于鱼群、鸟群分布预测等）、复杂决策过程（应用于亚马逊大坝选址，新能源材料开发等）、AI for drug design等Neuro AI\n招生简介\n欢迎有意保送或报考中山大学软件工程学院研究生的同学与我联系，来信请附上个人简历、本科阶段成绩单等材料以供参考。同时也欢迎本校学有余力、动手能力强并对科研有浓厚兴趣的本科生加入课题组。\n电子邮箱\nkongshf@mail.sysu.edu.cn\nsk2299@cornell.edu\n工作经历\n2025年12月至2026年5月 Cornell University，访问研究员 2022年9月至今中山大学软件工程学院，副教授 2020年9月-2022年8月南洋理工大学计算机科学与工程学院，研究员 2018年10月-2020年9月 Cornell University，博士后研究员\n学术论文（*即通讯作者，论文的CCF分类按投稿截止时已发布的最新CCF论文推荐列表）\nShufeng Kong, Zijie Wang, Nuan Cui, Hao Tang, Yihan Meng, Yuanyuan Wei, Feifan Chen, Yingheng Wang, Zhuo Cai, Yaonan Wang, Yulong Zhang, Yuzheng Li, Zibin Zheng, Caihua Liu, Hao Liang (2026): MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging. AAAI 2026, accepted. (CCF-A)Shufeng Kong, Tao Yu, Yuanyuan Wei, Caihua Liu, Junwen Bai, Yingheng Wang, Marc Grison, Daniel Fink, Carla Gomes (2026): STELLAR: Spatio-Temporal Environmental Learning with Latent Alignment and Refinement for Long-Tailed Species Distribution Modeling. IJCAI 2026, accepted. (CCF-A)Shufeng Kong, Fumei Deng, Xinyi Dong, Caihua Liu, Weiwei Chen, Yingheng Wang, Daniel Cao, Azahara Oliva, Antonio Fernandez-Ruiz, Carla P. Gomes (2026): Machine Learning Methods for Studying Latent Neural Activity Dynamics. IJCAI 2026, accepted. (CCF-A)Shufeng Kong, Xiaochuan Zhang, Caihua Liu (2026): Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration. ICML 2026, accepted. (CCF-A)Yingheng Wang (指导学生), Tao Yu (指导学生), Shufeng Kong*, Francesco Ricci, John M Gregoire, Carla P Gomes (2026): Deep Scientific Reasoning under Physical Constraints: Structure-Aware Spectrum Prediction for Electronic Density of States. ICML 2026, accepted. (CCF-A)Shufeng Kong, Feifan Chen, Zijie Wang and Caihua Liu (2025): Fast Deep Belief Propagation: An Efficient Learning-Based Algorithm for Solving Constraint Optimization Problems. Mathematics 2025, 13(20), 3349 (中科院4区)Shufeng Kong, Caihua Liu, and Carla Gomes (2024): IPGPT: Solving Integer Programming Problems with Sequence to Contrastive Multi-Label Learning. In the 40th Conference on Uncertainty in Artificial Intelligence (UAI 2026), accepted. (CCF-B)Yingheng Wang (指导学生), Shufeng Kong*, John Gregoire, Carla Gomes (2024): Conformal Crystal Graph Transformer with Robust Encoding of Periodic Invariance. AAAI 2024, accepted. (CCF-A)Yimeng Min, Ming-Chiang Chang, Shufeng Kong*, John M. Gregoire, R. Bruce van Dover, Michael O. Tompson, and Carla P. Gomes (2023): Physically Informed Graph-based Deep Reasoning Net for Efficient Combinatorial Phase Mapping. In: the 22nd International Conference on Machine Learning and Applications (ICMLA), accepted. (EI)Hao Cheng (指导学生), Shufeng Kong*, Yanchen Deng, Caihua Liu, Xiaohu Wu, Bo An, Chongjun Wang. Exploring leximin principle for fair core-selecting combinatorial auctions: Payment rule design and implementation. Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), accepted. (CCF A) Shufeng Kong, Francesco Ricci, Dan Guevarra, Jeffrey B. Neaton, Carla P. Gomes, and John M. Gregoire (2022): Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings. Nature Communications (Impact Factor: 14.92), 13(1), 1-12. (中科院1区)Junwen Bai, Yuanqi Du, Yingheng Wang, Shufeng Kong, John Gregoire, Carla Gomes (2022): Xtal2DoS: Attention-based crystal to sequence learning for density of states prediction. NeurIPS Workshop on AI for Science.Yanchen Deng (指导学生), Shufeng Kong*, Caihua Liu, and Bo An (2022): Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems. In: the 36th Annual Conference on Neural Information Processing Systems (NeurIPS'22), accepted. (CCF A)Junwen Bai (指导学生), Shufeng Kong*, and Carla P. Gomes (2022): Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification. In: the 39th International Conference on Machine Learning (ICML'22), accepted. (CCF A)Yanchen Deng (指导学生), Shufeng Kong*, and Bo An (2022): Pretrained cost model for distributed constraint optimization problems. In: the 36th AAAI Conference on Artificial Intelligence (AAAI'22). pp. 9331-9340. (CCF A)Shufeng Kong, Dan Guevarra, Carla Gomes, and John Gregoire (2021): Materials representation and transfer learning for multi-property prediction. Applied Physics Reviews (Impact Factor: 19.162), 8(2). (中科院1区)Wenting Zhao, Shufeng Kong, Junwen Bai, Daniel Fink, and Carla Gomes (2021): HOTVAE: Learning high-order label correlation for multi-label classification via attention-based variational autoencoders. In: the 35th AAAI Conference on Artificial Intelligence (AAAI'21), pp. 15016-15024. (CCF A)Shufeng Kong, Junwen Bai, Jae Hee Lee, Di Chen, Andrew Allyn, Michelle Stuart, Malin Pinsky, Kathy Mills and Carla Gomes (2020): Deep hurdle networks for zero-inflated multi-target regression: application to multiple species abundance estimation. In: the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), pp. 4375-4381. (CCF A)Junwen Bai, Shufeng Kong and Carla Gomes (2020): Disentangled variational autoencoder based multi-label classif"