个人信息 |
姓名：王岢
部门：暨南大学信息科学技术学院、广东智慧教育研究院
职称：教授
学位：博士学位
邮箱：wangke@jnu.edu.cn
个人简介 |
暨南大学信息科学与技术学院教授、博士生导师，深耕人工智能与交叉学科领域二十余年，学术与产业成果双轮驱动。
学术出版：主编国家重点大学计算机专业系列教材《对等网络、网格计算与云计算：原理与安全》，译著国外经典计算机科学教材《软件设计：从程序设计到体系结构》。
学术成果：在ICCV、ICDE、AAAI、ICME、ACL 等国际顶级学术会议，及 TNNLS、TMI、TAI、TII、T-ITS、TNSM、PR 等权威期刊发表论文 60 余篇，研究方向覆盖人工智能、大模型、智能体、多模态融合等前沿领域。
学术任职：担任中国人工智能学会青工委委员，深度参与人工智能领域学术生态建设与行业标准制定。
学生培养与发展 |
始终以“学生成长优先、学术与产业并重” 为核心培养理念，构建 “顶会论文 + 产业实战 + 全球交流” 的立体化培养体系，已累计培养 50 余名优秀硕士、博士研究生：
学术深造：20% 毕业生赴境外顶尖高校（如 CMU、港中文、港科大、新国立等）继续攻读博士学位，在人工智能、生物医药交叉领域深耕前沿，成长为学术新星；
职业发展：80% 毕业生成功入职字节跳动、阿里、腾讯、华为等人工智能领域头部企业，成长为核心技术骨干、算法负责人及业务管理者，广受行业认可与重用；
培养特色：鼓励学生深度参与产业级 AI项目研发，在真实场景中锤炼算法设计、工程实现与落地能力；全程提供顶会论文指导、海外交流推荐、职业规划定制等全方位支持，让每一位学生都能找到适合自己的成长路径。
招生寄语 |
热忱欢迎 985、211 高校本科推免生（可直博）、考研生提前联系！在这里，在这里，你将站在 AI 与多领域交叉的前沿阵地，获得：
顶会顶刊论文发表的专业指导
产业级AI 智能体项目的实战机会
海内外顶尖高校的交流推荐
清晰的职业规划与成长路径
与我们一同深耕人工智能前沿，在交叉学科创新中成长为兼具学术视野与工程能力的 AI 领域领军人才！
研究方向 |
聚焦新一代人工智能技术（大模型、智能体、多模态融合等），深耕 AI 与生物医药、金融、电力、游戏等领域的交叉应用，已成功研发并落地多项具有行业影响力的 AI 项目：
✅ 成药性优化智能体：助力先导化合物成药性优化，大幅加速药物研发进程；
✅ 问法大模型：面向法律领域的智能问答与辅助决策大模型，实现法律知识高效检索与场景化应用；
✅ 电力设计智能体：赋能电力工程设计流程智能化，提升设计效率与决策科学性；
✅ 肿瘤激酶抑制剂智能体：针对肿瘤治疗靶点，辅助激酶抑制剂药物分子设计与筛选；
✅ 股票研判 AI 智能体：基于市场数据与情绪周期，构建短线、中线和长线量化分析与投资决策辅助系统；
✅ AI 游戏智能体：探索游戏场景下的智能交互与决策算法，推动游戏AI 技术创新。
近年代表性学术论文 |
1. J. Chi, K. Wang*, et.al.，Activations as Features: Probing LLMs for Generalizable Essay Scoring Representations，the 40th AAAI Conference on Artificial Intelligence (AAAI 2026).
2. J.T.Wu, et.al., K. Wang*, Breaking the Evaluation Paradox: Evaluating High-Entropy Search with Computationally Irreducible Constraints, The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026).
3. J.T.Wu, X.T. Huang, Yu Chen, S. Pang, K. Wang*, Scaling and Taming Adversarial Training with Synthetic Data,International Conference on Computer Vision (ICCV 2025).
4. J.T. Wu, Z.Y.Song, X.Y. Zhang, S.J.Xie, L.X. Lin, K. Wang*, Vision Transformers Beat WideResNets on Small Scale Datasets Adversarial Robustness, the 39th AAAI Conference on Artificial Intelligence (AAAI 2025).
5. Y. Chen, K. Wang*, et.al.，Semantic-Guided Fast Adversarial Training via Class Relationship Exploitation, 16th ACM International Conference on Multimedia Retrieval (ICMR 2026).
6. Y. Chen, K. Wang*, et.al.，BridgeARD: Bridging the Latent Gap for Dual-Teacher Adversarial Robust Distillation, International Conference on Multimedia and Expo (ICME 2026).
7. Y. Chen, K. Wang*,et.al.，Feature Vulnerability-Aware Adversarial Training, International Conference on Multimedia and Expo (ICME 2026).
8. Y.Shuai, K. Wang*,et.al.，GAformer: Low-Light Image Enhancement Based on Gradient-Aware Kernel and Frequency-Modulated Transformer, 16th ACM International Conference on Multimedia Retrieval (ICMR 2026).
9. D.H. Zhou, B.W. Wu, K. Wang*,et.al.，Intervention-Driven Correlation Reduction: A Data Generation Approach for Achieving Counterfactually Fair Predictors, International Conference on Data Engineering(ICDE 2025).
10. K. Wang, et.al.,A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability, IEEE Transactions on Neural Networks and Learning Systems, 36(2): 2118-2132, 2024.
11. K. Wang, et.al.,Score-based Counterfactual Generation for Interpretable Medical Image Classification and Lesion Localization, IEEE Transactions on Medical Imaging, 43(10): 3596-3607, 2024.
12. K. Wang, et.al., Interpreting Adversarial Examples and Robustness for Deep Learning-based Auto-Driving Systems, IEEE Transactions on Intelligent Transportation Systems (T-ITS), 23(7):9755-9764,2022.
13. K. Wang, et.al., AFFIRM: Provably Forward Privacy for Searchable Encryption in Cooperative Intelligent Transportation System, IEEE Transactions on Intelligent Transportation Systems (T-ITS), 23(11):22607-22618, 2022.
14. K. Wang, et.al.，Statistics-Physics-Based Interpretation of the Classification Reliability of Convolutional Neural Networks in Industrial Automation Domain, IEEE Transactions on Industrial Informatics (TII),19(2):2165-2172, 2023.
15. K. Wang, et.al., Voice-Transfer Attacking on Industrial Voice Control Systems in 5G-Aided IIoT Domain, IEEE Transactions on Industrial Informatics (TII), 17(10):7085-7092, 2021.
16. K. Wang, et.al., Optimizing Neural Network Training: A Markov Chain Approach for Resource Conservation, IEEE Transactions on Artificial Intelligence, doi: 10.1109/TAI.2024.3413688, 2024.
17. K. Wang,et.al., Forward Privacy Preservation in IoT enabled Healthcare Systems, IEEE Transactions on Industrial Informatics (TII), 18(3):1991-1999, 2022.
18. P.Xu, K. Wang*, et.al., Adversarial Robustness in Graph-Based Neural Architecture Search for Edge AI Transportation Systems, IEEE Transactions on Intelligent Transportation Systems (T-ITS), 24(8): 8465-8474, 2023.
19. P. Xu, K. Wang*, et.al., An Interpretive Perspective: Adversarial Trojaning Attack on Neural-Architecture-Search Enabled Edge AI Systems, IEEE Transactions on Industrial Informatics (TII), 19(1):503-510,2023.
20. K. Wang, et.al., Uncovering Hidden Vulnerabilities in Convolutional Neural Networks through Graph-based Adversarial Robustness Evaluation, Pattern Recognition,143,109745, 2023.