About Me

I am now a Researcher at Technology Innovation Institute (TII). Before that, I obtained my Ph.D. at University of Paris-Saclay in 2022, where I worked under the supervision of Prof. Karine Zeitouni and Dr. Yehia Taher. I received my B.Sc degree in Optical & Electronic Information from Huazhong University of Science & Technology, China in 2015, and my M.Sc. degree in Data Science from University of Paris-Saclay, France in 2018, as well as an engineer degree in Computer Science from Polytech Sorbonne & Sorbonne Université, France in 2017. My research interests are broadly in machine learning and complex data mining, particularly: Edge AI; Large Generative Models; Temporal or/and Spatial data analysis.

All kinds of collaborations are welcomed! If you are interested, please contact me by email jingwei.zuo@tii.ae.

• (2023.09) The PurpleAirSF dataset paper got accepted by SIGSPATIAL 2023, check the dataset here.
• (2023.08) Glad to receive the IEEE CIS “Industry Early Career” Travel Grant, related to DSAA 2023.
• (2023.07) Two papers got accepted by DSAA 2023: one on Edge AI for Incremental Human Activity Learning on the Edge, another on Spatio-temporal data analysis for Air Quality Forecasting with Expandable Graph Neural Networks.
• (2023.06) We released PurpleAirSF, a Ready-to-Use Dataset for Realistic Air Quality Forecasting, check our preprint here.
• (2023.01) One paper is accepted by EDBT 2023 on Edge AI for handling the Catastrophic Forgetting issue in Incremental Edge Learning, check our preprint here!
• (2022.12) One journal paper is published in Data Mining and Knowledge Discovery (DMKD) on Graph Neural Networks for Traffic Forecasting (open access), see more details here.
• (2022.09) Excited to join Technology Innovation Institute (TII), UAE as a Researcher, working on Edge AI, Large Generative Models, and beyond.
• (2022.08) Thrilled to receive the Plateau de Saclay Doctoral Prize 2022 (accessit) from University of Paris-Saclay, France.
• (2022.07) One journal paper is accepted by GeoInformatica on Micro-envicronment Learning from Rich Trajectories, check our preprint here, and published version here.
• (2022.05) I successully defend my PhD at University of Paris-Saclay, France. Here are the slides and my thesis. Thank you all!

Journals:

• J. Zuo, K. Zeitouni, Y. Taher, S. G. Rodriguez: “Graph Convolutional Networks for Traffic Forecasting with Missing Values”, Data Mining and Knowledge Discovery (DMKD’22), Springer
• H. El Hafyani, M. Abboud, J. Zuo, K. Zeitouni and Y. Taher: “Learning the Micro-environment from Rich Trajectories in the context of Mobile Crowd Sensing - Application to Air Quality Monitoring”, GeoInformatica, Springer (2022). preprint

International Conferences & Workshops:

• J. Zuo, W. Li, M. Baldo and H. Hacid: “Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset”, to appear in SIGSPATIAL’23, Hamburg, Germany. preprint, dataset.
• G. Arvanitakis, J. Zuo and H. Hacid: “Practical Insights on Incremental Learning of New Human Physical Activity on the Edge”, to appear in IEEE DSAA’23, Thessaloniki, Greece, 2023. preprint
• J. Zuo, W. Li, M. Baldo and H. Hacid: “Opportunistic Air Quality Monitoring and Forecasting with Expandable Graph Neural Networks”, to appear in IEEE DSAA’23, Thessaloniki, Greece, 2023. preprint
• J. Zuo, G. Arvanitakis and H. Hacid: “On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge”, 26th International Conference on Extending Database Technology (EDBT’23), Ioannina, Greece, 2023.
• J. Zuo, K. Zeitouni and Y. Taher: “SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series”, 2021 IEEE International Conference on Data Mining (ICDM’21). preprint, full version, code, slides
• H. El Hafyani, M. Abboud, J. Zuo, K. Zeitouni and Y. Taher: “Tell Me What Air You Breath, I Tell You Where You Are”, (demo) 2021 International Symposium on Spatial and Temporal Databases (SSTD’21)
• M. Abboud, H. El Hafyani, J. Zuo, K. Zeitouni and Y. Taher: “Micro-environment Recognition in the context of Environmental Crowdsensing”, in Big Mobility Data Analytics with EDBT 2021 (BMDA’21)
• J. Zuo, K. Zeitouni and Y. Taher: “Incremental and Adaptive Feature Exploration over Time Series Stream”. (regular paper) 2019 IEEE International Conference on Big Data (BigData’19), Los Angeles, CA, USA, 2019. preprint, code, slides
• J. Zuo, K. Zeitouni and Y. Taher: “ISETS: Incremental Shapelet Extraction from Time Series Stream“，(demo) 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’19), Würzburg, Germany, 2019. poster, preprint, code
• J. Zuo, K. Zeitouni and Y. Taher. “Exploring Interpretable Features for Large Time Series with SE4TeC”, (demo) 22nd International Conference on Extending Database Technology (EDBT’19), Lisbon, Portugal, 2019. poster, code

National Conference:

• J. Zuo, K. Zeitouni and Y. Taher. “Time Series meet Data Streams: Perspectives of the Interdisciplinary Collision and Applications”, PhD symposium, 35ème Conférence sur la Gestion de Données – Principes, Technologies et Applications (BDA’19), Lyon, France, 2019.

Organizing committees:

• MDM 2020 Web Chair of the 21st IEEE Mobile Data Management Conference
• JDSE 2019 Junior organization committee of the 4th Paris-Saclay Junior Conference on Data Science and Engineering

Program committees:

• CIKM 2023 ACM International Conference on Information and Knowledge Management
• ECML-PKDD 2020 The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Reviewing:

• Inf. Sci. Information Sciences
• Artif. Intell. Rev. Artificial Intelligence Review
• JBHI IEEE Journal of Biomedical and Health Informatics
• SIGSPATIAL 2022 ACM International Conference on Advances in Geographic Information Systems
• ICDE 2021 IEEE International Conference on Data Engineering
• AAAI 2021 AAAI Conference on Artificial Intelligence
• SSTD 2021 International Symposium on Spatial and Temporal Databases
• DSAA 2020 IEEE International Conference on Data Science and Advanced Analytics

PROJECT STREAMOPS - INSTITUT DATAIA

• Open Source Platform for Research and Integration of Algorithms for Massive Time Series Flow Analysis
• The goal of StreamOps is to develop methods and algorithms to consider temporal order in data streams.

• jingwei.zuo@tii.ae
• PO Box: 9639, Masdar City, Abu Dhabi, UAE