Hi! I'm Ziqi Liu (刘子奇).
I am a second-year Ph.D. student at the University of Texas at Dallas, supervised by Prof. Yi Ding. I received my B.Eng. degree from Taiyuan University of Technology and my M.Res. degree from the University of Liverpool and Xi'an Jiaotong-Liverpool University.
My research focuses on the synergy between machine learning and edge computing, spanning innovations in systems and algorithms for ubiquitous devices and wearables, with a particular interest in new opportunities enabled by LLMs.
I am open to collaboration and welcome connections with researchers and practitioners. Please feel free to contact me if you are interested in collaborating.
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University of Texas at DallasComputer Science
Ph.D. StudentSep. 2024 - present -
University of LiverpoolMRes. in Computer ScienceSep. 2021 - Jul. 2023 -
Xi'an Jiaotong-Liverpool UniversityMRes. in Computer ScienceSep. 2021 - Jul. 2023
Honors & Awards
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None2026
News
Selected Publications (view all )
Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality
Ziqi Liu, Peng Zeng, Yi Ding
Proceedings of the ACM Web Conference (WWW) 2026 WWW
Proposes a predictability-aware framework for compressing and decompressing multichannel time series data by leveraging latent seasonality, improving efficiency while preserving essential temporal patterns.
Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality
Ziqi Liu, Peng Zeng, Yi Ding
Proceedings of the ACM Web Conference (WWW) 2026 WWW
Proposes a predictability-aware framework for compressing and decompressing multichannel time series data by leveraging latent seasonality, improving efficiency while preserving essential temporal patterns.
IFRN: Insensitive Feature Removal Network for Zero-Shot Mechanical Fault Diagnosis Across Fault Severity
Ziqi Liu, Rui Yang, Weibo Liu, Xiaohui Liu
Neurocomputing 2023
Proposes an Insensitive Feature Removal Network (IFRN) for zero-shot mechanical fault diagnosis across varying fault severities. The method leverages an attention-based module and a denoising autoencoder to hierarchically eliminate insensitive features, reducing prototype shift and improving classification accuracy.
IFRN: Insensitive Feature Removal Network for Zero-Shot Mechanical Fault Diagnosis Across Fault Severity
Ziqi Liu, Rui Yang, Weibo Liu, Xiaohui Liu
Neurocomputing 2023
Proposes an Insensitive Feature Removal Network (IFRN) for zero-shot mechanical fault diagnosis across varying fault severities. The method leverages an attention-based module and a denoising autoencoder to hierarchically eliminate insensitive features, reducing prototype shift and improving classification accuracy.