Senior Researcher
JD Intelligent Cities Research & 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 Second Prize of the Natural Science Award of the Ministry of Education in 2021, the 22nd China Patent Excellence Award in 2021, 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.
- Spatio-Temporal AI (AI for ST-data)
- Urban Computing
- Deep Learning
- Spatio-Temporal Data Mining
- Federated Learning
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
Recent News
- May 2022: One ST-AI paper was accpeted by KDD2022.
- Apr 2022: I serves a general co-chair for the 3rd ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. Paper submission deadline is May 26, 2022.
- Apr 2022: 荣获第二十三届中国专利优秀奖
- Feb 2022: I was recognized as the 2022 AI 2000 Most Influential Scholar Award Honorable Mention (2022年AI 2000人工智能全球最具影响力学者提名奖)
- Feb 2022: Three papers were accepted by IEEE TKDE.
- Feb 2022: One Spatio-Temporal AI paper (GeoMAN) was selected as Most Influential IJCAI papers by Paper Digest
- Otc 2021: 我们组织的2021中国计算机大会（CNCC 2021）技术论坛《第四届智慧城市与城市计算论坛》 因故推迟, 将于2021年12月17日13:30-17:30在深圳国际会展中心CC101C召开. 活动链接
- Otc 2021: 荣获2021年CCF科学技术奖科技进步杰出奖
- Sep 2021: 受邀在2021中国计算机大会（CNCC 2021）《时空智能与智慧城市论坛》做特邀报告
- Sep 2021: 受邀在2021中国人工智能大会（CCAI 2021）《大数据智能学术论坛》做特邀报告
- May 2021: 荣获第二十二届中国专利优秀奖
- May 2021: 恭喜Zheyi PAN入选AI华人新星榜单由百度学术, 代表性论文是时空元学习(Spatio-Temporal Meta Learning)相关
- Apr 2021: I was recognized as the 2021 AI 2000 Most Influential Scholar Award Honorable Mention (2021年AI 2000人工智能全球最具影响力学者提名奖)
- Mar 2021: Two Spatio-Temporal AI（ST-ResNet、ST-MetaNet）papers were selected as most influential AAAI and KDD papers by Paper Digest: Most influential AAAI papers, Most influential KDD papers
- 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)
- Feb. 2019: I serve as an associate editor (AE) of ACM TIST
Projects
AutoST: AutoML for Saptio-Temporal Data
Employing automated machine learning (AutoML) techniques for spatio-temporal data mining.
Meta Learning for Spatio-Temporal Data
Employing meta learning techniques for spatio-temporal data mining.
Deep Learning for Spatio-Temporal Data
Leveraging deep learning techniques and big spatio-temporal data to empower intelligent applications in a city.
Urban Flow
Using a diversity of big data and deep learning techniques to predict citywide crowd/traffic flows throughout a city.
Contact
- msjunbozhang@outlook.com
- No. 18 Kechuang 11 Street, BDA, Beijing