JINGYAN
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Jingyan Jiang

I am an Assistant Professor in the college of big data and Internet at Shenzhen Technology University. Previously, I was a senior researcher at Sangfor SRI Lab, Shenzhen, where I worked on optimizing complex networked systems using optimization techniques and machine learning techniques. Also, I was a Postdoc at Shenzhen International Graduate School, Tsinghua University, and advised by Prof. Shutao Xia. Before that, I did my PhD and bachelor at school of CCST, Jilin University, where I was advised by Prof. Liang Hu I was a visiting PhD at Shenzhen International Graduate School, Tsinghua University during 2017-2020, where advised by Prof. Zhi Wang.

Email  /  Google Scholar  /  DBLP

I'm leading the SINX (SecurIty and Network + X) Group, and looking for self-motivated students to work with me at SZTU. Please feel free to drop me an email with your CV.

Email: jiangjingyan@sztu.edu.cn

News

  • 03/2025, One paper has been accepted by ICME 2025 (CCB-B)！

  • 03/2025, One paper has been accepted by NOSSDAV 2025 (CCB-B)！

  • 02/2025, Two paper have been accepted by CVPR 2025(CCB-A)！

  • 01/2025, I will serve as a Program Committee member for IJCNN.

  • 01/2025, I will serve as a Program Committee member for ICME.

  • 09/2024, I will serve as a Program Committee member for AAAI.

  • 06/2024, I will serve as a Program Committee member for ACM MM.

  • 09/2022, I will join the College of Big Data and Internet at Shenzhen Technology University as an assistant professor.

  • 01/2022, one paper has been accepted by Information Processing & Management.

  • 10/2021, one paper has been accepted by RTSS 2021.

  • 07/2021, one paper has been accepted by Expert Systems with Applications.

  • 06/2020, I got my PhD from Jilin University.

    • Recent Projects

    研究方向概览 / Research Direction Overview

    智能多媒体感知与推理实验室（Sinx-Lab: Smart Multimedia Learning and Inference Lab）实验室（与清华大学合作的实验室）招募优秀硕士研究生（研究生不限计算机技术或者大数据专业都可以联系）和高年级本科生。课题介绍可以具体参考如下链接： The Smart Multimedia Learning and Inference Lab (Sinx-Lab), a collaborative lab with Tsinghua University, is recruiting outstanding master's students (students from Computer Technology or Big Data majors are welcome) and senior undergraduate students. For more details about the research topics, please refer to the following link: 模型微调和推理小组 | Notion / Model Fine-tuning and Inference Group

    主要研究领域 / Primary Research Fields

    主要研究方向为：解决核心问题“如何让预训练模型在面临真实生产环境时，性能更好，资源消耗更低？” The primary research focuses on addressing the key question: "How can pre-trained models perform better and consume fewer resources in real-world production environments?"

    这些技术应用场景广泛，包括但不限于：自动驾驶、大模型、工业互联网、具身智能、推广搜索等。 These technologies have wide-ranging applications, including but not limited to autonomous driving, large models, industrial IoT, embodied AI, and search engines.

    实验室优势 / Laboratory Advantages

    Publications

    Conference Papers:

    1. Cosmic: Clique-oriented semantic multi-space integration for robust clip test-time adaptation.
      F. Huang*, J. Jiang*, Q. Jiang, H. Li, F. N. Khan, and Z. Wang
      CVPR 2025 | paper (CCF A, co-first author)

    2. Datta: Towards diversity adaptive test-time adaptation in dynamic wild world.
      Ye, D. Wei, Z. Liu, Y. Pang, Y. Lin, J. Liao, Q. Jiang, D. He, and J. Jiang †
      IEEE ICME 2025 | paper (CCF B, corresponding author, first author from Shenzhen Technology University)

    3. Q-dit: Accurate post-training quantization for diffusion transformers.
      L. Chen, Y. Meng, C. Tang, X. Ma, J. Jiang, X. Wang, Z. Wang, and W. Zhu
      CVPR 2025 | paper (CCF A)

    4. LLM4Band: Enhancing reinforcement learning with large language models for accurate bandwidth estimation.
      Wang, R. Lu, C. Westphal, D. He, and J. Jiang*
      NOSSDAV 2025 | paper (CCF B, corresponding author, first author from Shenzhen Technology University)

    5. Joint Model and Data Adaptation for Cloud Inference Serving.
      Jingyan Jiang, Ziyue Luo, Chenghao Hu, Zhaoliang He, Zhi Wang, Shutao Xia and Chuan Wu
      RTSS 2021 | paper（CCF A）

    6. Decentralized federated learning: A segmented gossip approach
      Chenghao Hu, Jingyan Jiang, Zhi Wang
      IJCAI Workshop 2019 | paper

    7. Jalad: Joint accuracy-and latency-aware deep structure decoupling for edge-cloud execution
      Hongshan Li, Chenghao Hu, Jingyan Jiang, Zhi Wang, Yonggang Wen, Wenwu Zhu
      ICPADS 2018 | paper （CCF C）

    Journal Papers:

    1. Fast-DRD: Fast decentralized reinforcement distillation for deadline-aware edge computing
      Shinan Song, Zhiyi Fang, Jingyan Jiang*
      Information Processing & Management 2022 |paper | * Indicate Corresponding Author. （SCI 1）

    2. Reinforcement learning approach for resource allocation in humanitarian logistics
      Lina Yu, Canrong Zhang, Jingyan Jiang, Huasheng Yang, Huayan Shang
      Expert Systems with Applications 2021 | paper

    3. Decentralised federated learning with adaptive partial gradient aggregation
      Jingyan Jiang, Liang Hu
      CAAI Transactions on Intelligence Technology 2020 | paper （JCR Q1）

    4. BACombo—Bandwidth-aware decentralized federated learning
      Jingyan Jiang, Liang Hu, Chenghao Hu, Jiate Liu, Zhi Wang
      Electronics 2020 | paper

    5. Dynamic pricing with traffic engineering for adaptive video streaming over software-defined content delivery networking
      Pingting Hao, Liang Hu, Kuo Zhao, Jingyan Jiang, Tong Li, Xilong Che
      Multimedia Tools and Applications 2019 | paper

    6. Q-FDBA: improving QoE fairness for video streaming
      Jingyan Jiang, Liang Hu, Pingting Hao, Rui Sun, Jiejun Hu, Hongtu Li
      Multimedia Tools and Applications 2018 | paper

    7. Mobile edge provision with flexible deployment
      Pingting Hao, Liang Hu, Jingyan Jiang, Jiejun Hu, Xilong Che
      IEEE Transactions on Services Computing 2016 | paper （SCI 1）

    8. Environment observation system based on semantics in the internet of things
      Liang Hu, Jingyan Jiang, Jin Zhou, Kuo Zhao, Liang Chen, Huimin Lu
      Journal of Networks 2013 | paper

    Services

  • Reviewers of ACM International Conference on Multimedia (MM), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), The ACM Multimedia Systems Conference (MMSys), The Conference on Information and Knowledge Management (CIKM), IEEE TrustCom, Northwest Cybersecurity Symposium, IEEE JSAC Series on Machine Learning for Communications and Networksetc.

    • Courses

  • IB01009(2022Fall) Information and security.

  • IB00030(2022Fall) Cloud Computing.

    • Awards

  • 2012-2017, Scholarship for Postgraduate in Jilin University

  • 2015, The First Prize of National Business Science and Technology Progress Award

  • 2012, Outstanding Graduates Award

  • 2011, The First Prize in China Undergraduate Mathematical Contest in Modeling (CUMCM)

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