Senior Researcher

JD iCity, JD Technology

Biography 中文

Dr. Junbo Zhang is a Senior Researcher of JD Intelligent Cities Research. He is leading the Urban AI Product Department of JD iCity at JD Technology, as well as AI Lab of JD Intelligent Cities Research. His team is focusing on the research, development, and innovation of urban computing and spatio-temporal data minging/AI, with a broad range of applications in smart city.

Prior to that, he was a researcher at MSRA from 2015 - 2018. He has published over 50 research papers (e.g. AI Journal, IEEE TKDE, KDD, AAAI, IJCAI, WWW, ACL, UbiComp) in refereed journals and conferences. He serves as an Associate Editor of ACM Transactions on Intelligent Systems and Technology. He received B.E. and Ph.D. degrees from the Southwest Jiaotong University, China. He received the ACM Chengdu Doctoral Dissertation Award in 2016, the Chinese Association for Artificial Intelligence (CAAI) Excellent Doctoral Dissertation Nomination Award in 2016, the Si Shi Yang Hua Medal (Top 1/1000) of SWJTU in 2012, and the Outstanding Ph.D. Graduate of Sichuan Province in 2013. He is a senior member of CCF (China Computer Federation), a member of IEEE and ACM and a committee member of CCF-AI.

Hiring: The team has multiple Urban Computing/spatio-temporal data mining Researcher, Engineer, and Intern positions open in Beijing, China. Please feel free to send your CV if interested.

Interests
  • Spatio-Temporal AI (AI for ST-data)
  • Urban Computing
  • Deep Learning
  • Spatio-Temporal Data Mining
  • Federated Learning

Recent News

More News

  • Mar 2021: Two Spatio-Temporal AI(ST-ResNetST-MetaNet)papers were selected as most influential AAAI and KDD papers of PaperDigest: Most influential AAAI papers, Most influential KDD papers
  • Mar 2021: One paper was accepted by IEEE TKDE
  • Jan 2021: Two papers were accepted by TheWebConf(WWW) 2021
  • Jan 2021: One paper was accepted by AAAI 2021
  • Call for papers: ACM Transactions on Intelligent Systems and Technology Special Issue on Deep Learning for Spatio-Temporal Data, submission deadline: Nov. 30, 2020 Otc. 30, 2020
  • Sep. 2020: I was honored as CCF Senior Member
  • July 2020: I gave a tutorial on Deep Learning for Spatio-Temporal Data in Urban Computing Summer School 2020
  • July 2020: I was selected for the Beijing Nova Program (北京市科技新星计划)
  • July 2020: One paper was accepted by IEEE Intelligent Systems
  • June 2020: One paper was accepted by IEEE TKDE
  • May 2020: One paper was accepted by KDD 2020
  • May 2020: One paper was accepted by IEEE TKDE
  • Feb. 2019: I serve as an associate editor (AE) of ACM TIST

Projects

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AutoST: AutoML for Saptio-Temporal Data

Employing automated machine learning (AutoML) techniques for spatio-temporal data mining.

Deep Learning for Spatio-Temporal Data

Deep Learning for Spatio-Temporal Data

Leveraging deep learning techniques and big spatio-temporal data to empower intelligent applications in a city.

Urban Flow

Urban Flow

Using a diversity of big data and deep learning techniques to predict citywide crowd/traffic flows throughout a city.

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