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。

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。

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。

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。

　　

研究团队

中山大学区块链与智能金融研究中心

  • 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

Institue of AI for Science, Cornell University

  • 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

　　

招生简介

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。

 

电子邮箱

kongshf@mail.sysu.edu.cn

sk2299@cornell.edu

 

工作经历

  •  2025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

　　

学术论文（*即通讯作者）

  • Shufeng 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, 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, 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 classification with covariance-aware multivariate probit model. In: the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), pp. 4313-4321. (CCF A)
  • Shufeng Kong, Jae Hee Lee and Sanjiang Li (2018): A new distributed algorithm for efficient generalized arc-consistency propagation. Autonomous agent and multi-agent systems (Impact Factor: 1.419), 32(5):569-601. (JCR Q3)
  • Shufeng Kong, Jae Hee Lee and Sanjiang Li (2018): Multiagent simple temporal problem: the arc-consistency approach. In: the 32th AAAI Conference on Artificial Intelligence (AAAI’18), New Orleans, Louisiana, USA, February 2-7, 2018. (CCF A)
  • Shufeng Kong, Jae Hee Lee and Sanjiang Li (2017): A deterministic distributed algorithm for reasoning with connected row-convex constraints. In: the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS'17), pp. 203-211. (CCF B)
  • Shufeng Kong, Sanjiang Li and Michael Sioutis (2018): Exploring directional path-consistency for solving constraint networks. The Computer Journal (Impact Factor: 0.98), 61(9): 1138-1350. (JCR Q4)
  • Shufeng Kong, Sanjiang Li, Yongming Li and Zhiguo Long (2015): On tree-preserving constraints. In: the 21st International Conference on Principles and Practice of Constraint Programming (CP'15), pp. 244-261. (CCF B)
  • Shufeng Kong, Sanjiang Li, Yongming Li and Zhiguo Long (2017): On tree-preserving constraints. Annals of Mathematics and Artificial Intelligence (Impact Factor: 1.011), 81(3-4): 241-271. (JCR Q3)
  • Carla P. Gomes, Junwen Bai, Yexiang Xue, Johan Bjorck, Brendan Rappazzo, Sebastian Ament, Richard Bernstein, Shufeng Kong, Santosh K. Suram, R. Bruce van Dover, John M. Gregoire (2019): CRYSTAL: a multi-agent AI system for automated mapping of materials’ crystal structures. MRS Communications (Impact Factor: 1.935), 9(2):600-608. (JCR Q4)
  • Qiong Liu, Jiajun Zhuang and Shufeng Kong (2013): Detection of pedestrians for far-infrared automotive night vision systems using learning-based method and head validation. Measurement Science and Technology, 24(7):074022.
  • Qiong Liu, Jiajun Zhuang and Shufeng Kong (2012): Detection of pedestrians at night time using learning-based method and head validation. IEEE International Conference on Imaging Systems and Techniques (IST), pp. 398-402.

 

科研项目

  • 心血管病全周期管理的监测设备研发与中医智能诊疗系统构建，国家重点研发计划，担任中山大学课题组负责人（项目总排名第2），2026-2029
  • 面向中医领域的大模型构建研究，横向，主持，2024-2027
  • Accelerated Learning Lab: Capturing Deep Structure to Accelerate Materials Discovery，丰田汽车研究院，参与，2017-2021
  • Collaborative Research: CompSustNet: Expanding the Horizons of Computational Sustainability，美国自然科学基金，参与，2015-2021
  • Convergence Research to Meet Ocean Decision Challenges，美国自然科学基金，参与，2020-2022
  • 多无人机系统的多对多指派算法与协同优化，国家自然科学基金面上项目, 参与，2021-2024

 

学术服务（学术兼职）

  • IEEE TNNLS, IEEE TCDS, IEEE Cybernetics, Science Advances 等期刊审稿人
  • AAAI, IJCAI, ICLR, NeurIPS, ICML, AAMAS等国际学术会议审稿人