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 AI 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
Education
  • PhD in Computer Science, 2009-2015

    Southwest Jiaotong University

  • Research Assitant, 2013-2015

    Chinese University of Hong Kong

  • Visiting PhD student, 2012-2013

    Georgia State University

  • BSc in Communication Engineering, 2005-2009

    Southwest Jiaotong University

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Projects

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AutoST: AutoML for Saptio-Temporal Data

AutoST: AutoML for Saptio-Temporal Data

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

Meta Learning for Spatio-Temporal Data

Meta Learning for Spatio-Temporal Data

Employing meta learning 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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