康奇宇
个人简介
电子工程与信息科学系特任教授，博士生导师，国家青年人才。2020年获南洋理工大学博士学位并从事博士后研究工作，2024年加入中科大。主要研究方向为机器学习的理论研究和技术开发工作，研究领域涉及微分方程与机器学习、大模型与通用人工智能、类脑智能等。担任ELSEVIER Signal Processing期刊编委，多个机器学习顶级会议及期刊程序委员、审稿人等职务，曾担任智能交通顶级会议ITSC专题研讨会主席，并受邀在多个国际会议作学术报告。在NeurIPS、ICML、ICLR、AAAI、IJCAI、CVPR、IEEE TIP、IEEE TKDE等领域高水平学术期刊及国际会议上发表论文30余篇，其中CCF A类会议和期刊长文20余篇。主持多项国家自然科学基金等科研项目。
研究方向
深度学习安全可信问题。
物理动力系统与机器学习。
大模型高效计算问题。
类脑智能。
联系方式
办公地点：高新校区一号学科楼A508
电子邮箱：qiyukang@ustc.edu.cn
个人主页：https://faculty.ustc.edu.cn/kangqiyu/
代表性论文
Q. Kang, X. Li, K. Zhao, W. Cui, Y. Zhao, W. Deng, and W. P. Tay,“Efficient training of neural fractional-order differential equation via adjoint backpropagation," Proc. AAAI Conference on Artificial Intelligence (AAAI), Philadelphia, USA, Feb. 2025.
Q. Kang, K. Zhao, Q. Ding, F. Ji, X. Li, W. Liang, Y. Song, and W. P. Tay, “Unleashing the potential of fractional calculus in graph neural networks with FROND,”Proc. International Conference on Learning Representations (ICLR), Vienna, Austria, May 2024, Spotlight.
K. Zhao, X. Li, Q. Kang†, F. Ji, Q. Ding, Y. Zhao, W. Liang, and W. P. Tay, “Distributed-order fractional graph operating network,” Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2024, Spotlight.
Q. Kang, K. Zhao, Y. Song, Y. Xie, Y. Zhao, S. Wang, R. She, and W. P. Tay, “Coupling graph neural networks with fractional order continuous dynamics: A robustness study,” Proc. AAAI Conference on Artificial Intelligence, Vancouver, Canada, Feb. 2024.
K. Zhao, Q. Kang†, Y. Song, R. She, S. Wang, and W. P. Tay, “Adversarial robustness in graph neural networks: A Hamiltonian approach,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, Dec. 2023, Spotlight.
Q. Kang, K. Zhao, Y. Song, S. Wang, and W. P. Tay, “Node embedding from neural Hamiltonian orbits in graph neural networks,” Proc. International Conference on Machine Learning (ICML), Hawaii, USA, Jul. 2023
Q. Kang, Y. Song, Q. Ding, and W. P. Tay, “Stable neural ODE with Lyapunov-stable equilibrium points for defending against adversarial attacks,” Advances in Neural Information Processing Systems (NeurIPS), virtual, Dec. 2021.
Q. Kang and W. P. Tay, "Task recommendation in crowdsourcing based on learning preferences and reliabilities," IEEE Transactions on Services Computing, vol. 15, no. 4, pp. 1785–1798, 2022.