Recent News

  • Two papers were accepted—IEEE/ACM ToN and ACM TACO.

  • One paper was accepted by CVPR 2025.

  • One paper was accepted by EuroSys 2025.

  • Two papers were accepted—IEEE INFOCOM 2025 and AAAI 2025.

  • One paper was accepted by IEEE TSC.

  • One paper was accepted by IEEE/ACM ToN.

  • Two papers were accepted—ACM MobiCom 2024 Demo and IEEE TSC.

  • Two papers were accepted—IEEE TMC and IEEE/ACM ToN.

  • Three papers were accepted—IEEE TMC, ICDE 2024, and ACL 2024.

  • Two papers were accepted—IEEE TMC and CVPR 2024.

  • One paper was accepted by ASPLOS 2024.

  • Two papers were accepted—NSDI 2024 and IEEE TKDE.

  • Two papers were accepted—IEEE TPDS and IEEE TDSC.

  • One paper was accepted by NeurIPS 2023.

  • Four papers were accepted—one SIGCOMM 2023 Demo, three ICCV 2023.

  • Three papers were accepted—SIGIR 2023, IJCAI 2023, and IEEE/ACM ToN.

  • One paper was accepted by IEEE TMC.

  • One paper was accepted by IEEE JSAC.

  • One paper was accepted by ACM MM 2021.

  • Two papers were accepted by SIGIR 2021.

  • One paper was accepted by ICDE 2021.

  • One paper was accepted by AAAI 2021.

  • One paper was accepted by ACL 2020.

  • One paper was accepted by AAAI 2020.

  • One paper was accepted by CVPR 2019.

Welcome to NIRC BUPT

Our lab (the Network Intelligence Research Center, NIRC) conducts research on intelligent networking and deep learning at the State Key Laboratory of Networking and Switching Technology (SKL-NST), School of Computer Science (CS), Beijing University of Posts and Telecommunications (BUPT).

We concentrate on understanding and decision-making in human-computer interaction, advancing both theoretical research and engineering practices in intelligent networking. Representative achievements include computing power networks, intelligent operations and maintenance, knowledge-defined networking, intent-based configuration generation, bare-hand interaction, large model acceleration, and multipath transmission. Please refer to our publication page for details.

Research Direction

What we are doing

Net/Cloud

泛在算力智联网

Exploring the integration of AI techniques into modern networking infrastructures, including configuration generation, intent-based networking, AI for data centers, vehicular networks, and intelligent networks.

MLSys/FM

智能模型与服务

Intelligent model technologies and service-oriented deployment methodologies, including efficient model optimization, lightweight architecture design, and edge-cloud collaborative inference acceleration.

DM/IR

多模态数据分析

Investigating LLM-assisted methodologies to advance multimodal time-series processing and log analytics, with applications in building AIOps platforms, engineering ChatOps solutions, and optimizing recommendation systems.

CV/NLP

视觉与意图理解

Focused on advancing Natural Language Processing and Computer Vision models, including intent networks, code generation, LLM retrieval, semantic large models, human pose estimation, and VAD anomaly detection.

Hand/HCI

手势与人机交互

3D hand reconstruction and pose estimation, 3D avatar reconstruction and driving, natural human-computer interaction systems and immersive interactive experience.