Jingwei ZUO

About Me

I am now a Machine Learning 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 include machine learning and complex data mining, particularly: Federated or/and Distributed Learning; Temporal or/and Spatial data analysis.

Journals:

J. Zuo, K. Zeitouni, Y. Taher, S. G. Rodriguez: “Graph Convolutional Networks for Traffic Forecasting with Missing Values”, accepted by Data Mining and Knowledge Discovery (DMKD), 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”, to appear in GeoInformatica, Springer (2022)

International Conferences & Workshops:

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, preprint, 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:

ECML-PKDD 2020 The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Reviewing:

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

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.