焦阳
职称:中级
所在院系:计算机工程系
研究方向:AI+网络、机器学习、分布式优化、大模型安全性
电话:
邮箱:yang.jiao@seu.edu.cn
职务:
个人简介

焦阳,博士,东南大学计算机科学与工程学院讲师,硕士生导师。研究方向包括AI+网络、机器学习、分布式学习、多层优化等,在 ICML、NeurIPS、ICLR、AAAI、SIGIR、INFOCOM、IEEE TMC、IEEE TDSC 等 CCF-A 类国际知名期刊与会议上发表学术论文十余篇。

研究方向
教育经历
工作经历
科研项目
论文著作
  • Jiao Y, Yang K. Privacy Meets Hierarchy: Differentially Private Distributed Trilevel Learning (NeurIPS,CCF-A)

  • Jiao Y, Yang K, Song D. Federated distributionally robust optimization with non-convex objectives: Algorithm and analysis. IEEE Transactions on Mobile Computing (CCF-A,JCR 1区)

  • Jiao Y, Yang K, Song D. Distributed distributionally robust optimization with non-convex objectives (NeurIPS,CCF-A)

  • Jiao Y, Yang K, Wu T, et al. Provably convergent federated trilevel learning (AAAI,CCF-A)

  • Jiao Y, Yang K, Jian C. DTZO: Distributed trilevel zeroth order learning with provable non-asymptotic convergence (ICML,CCF-A)

  • Jiao Y, Yang K, Wu T, et al. Asynchronous distributed bilevel optimization (ICLR,CCF-A)

  • Jiao Y, Wang X, Yang K. Pr-attack: Coordinated prompt-rag attacks on retrieval-augmented generation in large language models via bilevel optimization (SIGIR,CCF-A)

  • Jiao Y, Yang K, Song D, et al. Timeautoad: Autonomous anomaly detection with self-supervised contrastive loss for multivariate time series. IEEE Transactions on Network Science and Engineering (JCR 1区)

  • Jian C, Yang K, Jiao Y. Tri-level navigator: LLM-empowered tri-level learning for time series OOD generalization (NeurIPS,CCF-A)

  • Han P, Wang S, Jiao Y, et al. Federated learning while providing model as a service: Joint training and inference optimization (INFOCOM,CCF-A)

  • Dou S, Yang K, Jiao Y, et al. Anomaly detection in event-triggered traffic time series via similarity learning. IEEE Transactions on Dependable and Secure Computing (CCF-A,JCR 1区)

  • Ma H, Yang K, Jiao Y. Cellular traffic prediction via byzantine-robust asynchronous federated learning. IEEE Transactions on Network Science and Engineering (JCR 1区)

  • Huang K, Jiao Y, Liu C, et al. Robust network structure reconstruction based on bayesian compressive sensing. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019, 29(9).

专利
获奖情况
  • 联系方式
  • 通信地址:南京市江宁区东南大学路2号东南大学九龙湖校区计算机学院
  • 邮政编码:211189
  • ​办公地点:东南大学九龙湖校区计算机楼
  • 学院微信公众号