About Me

I am an Assistant Professor at the Department of Computer Science at University of Texas Rio Grande Valley. I received my Ph.D. from George Mason University in 2023. My advisor is Dr. Jessica Lin. I received my B.Sc. degree in mathematics and statistics from York University and M.Sc. degree in Computational Science from George Mason University. I also did research on farm structure and financial analysis at U.S. Department of Agriculture and machine prognostics at Intel Cooperation before.

My research interest lies in the broad area of data mining, machine learning, and deep learning, with a special focus on high-resolution time series data. I am currently interested developping robust, interpretable, and reliable data mining and machine learning tools for anomaly detection, time series chain discovery, long sequence forecasting, and classification, as well as developping practical solution for privacy-aware time series data sharing.

News

Publications

  • Kofi Nketia Ackaah-Gyasi*, Sergio Valdez*, Yifeng Gao, Li Zhang, "Exploring Spectral Bias in Time Series Long Sequence Forecasting", Undergraduate Consortium of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023. [ paper]

  • Li Zhang, Jiahao Ding, Yifeng Gao, Jessica Lin, "PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series", In Proceedings of the 2023 SIAM International Conference on Data Mining(SDM'23). Society for Industrial and Applied Mathematics, Minneapolis, USA, May 2023. [acceptance rate: 27.4%] [pdf]

  • Wenjie Xi*, Arnav Jain*, Li Zhang, Jessica Lin, "LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification." Deep Learning on Graphs: Method and Applications Workshop, AAAI 2023 (DLG-AAAI'23). [pdf] (Accepted) * equal contribution

  • Li Zhang*, Yan Zhu*, Yifeng Gao, Jessica Lin, "Robust Time Series Chain Discovery with Incremental Nearest Neighbors." In 2022 IEEE International Conference on Data Mining (ICDM'22). (Accepted) [acceptance rate: 20%] [extended version] [ support website] * equal contribution

  • Li Zhang, Nital Patal, Xiuqi Li, Jessica Lin, "Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series", In Proceedings of the 2022 SIAM International Conference on Data Mining(SDM'22). Society for Industrial and Applied Mathematics. [acceptance rate: 27.8%] [pdf] [code]

  • Li Zhang, Yifeng Gao, Jessica Lin, "Semantic Discord: Finding Unusual Local Patterns for Time Series", In Proceedings of the 2020 SIAM International Conference on Data Mining(SDM'20). Society for Industrial and Applied Mathematics, Cincinnati, USA, May 2020. [acceptance rate: 24%] [pdf] [code]

  • Li Zhang and Huzefa Rangwala, "Early Identification of At-Risk Students Using Iterative Logistic Regression." In Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED'2018). Springer, London, UK, June 2018. [acceptance rate: 23.5%] [pdf] (Best Student Full Research Paper Nominee 6/192=3.125%)

  • Sujit K Ghosh, Christopher B Burns, Daniel L Prager, Li Zhang, and Glenn Hui. "On nonparametric estimation of thelatent distribution for ordinal data." Computational Statistics and Data Analysis(CSDA), 2018. [impact factor: 1.323][pdf]

  • Samiul Haque, Laszio P. Kindrat, Li Zhang, Vikenty Mikheev, Daewa Kim, Sijing Lui, Jooyeon Chung, Mykhailo Kuian, Jordan E. Massad, and Ralph C. Smith. Uncertainty-enabled design of electromagnetic reflectors with integrated shape control. In Proceding of SPIE, 2018.

  • Christopher Burns, Sujit Ghosh, Daniel Prager, and Li Zhang. Imputation of ordinal data in the agricultural resource management survey using bayesian methods. In Joint Statistical Meetings (JSM). American Statistical Association (JSM), 2017.


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