Liangwei Zheng

Research Interests

Multimodal analysis and synthesis Artificial Intelligence Data engineering and data science Machine learning

Mr Liangwei Zheng

Higher Degree by Research Candidate

School of Computer Science and Information Technology

College of Engineering and Information Technology

Liangwei is a PhD candidate specializing on Computer Science, Data Science and AI at the University of Adelaide, Australia. He has published papers on many top conferences, including ICML, KDD, WWW and CIKM, spanning the research interests in Mixture-of-Experts, Multimodal Learning, Medical AI application and Time series analysis. He has actively served as reviewer in leading conferences within the Data Mining, AI and Machine Learning community. His current and future research commitment focuses on life signal monitoring and patient representation learning by leveraging multimodal mixture-of-experts and time series analysis techniques. He is currently working as Machine Learning Engineer Intern (PhD program) in TikTok Australia until July 2026. This opportunity enables him strong experience by using LLM / VLM / Agent to address content safety problem in large amount of data stream in TikTok platform.
 
In addition to research experience, Liangwei have actively contributed to academic and student leadership at the University of Adelaide. As a research supervisor, He has mentored undergraduate and postgraduate students through project design and model development in time series data and Mixture-of-Expert, strengthening my collaborative and technical leadership skills. His students have successfully published papers on ADMA 2025 and CIKM 2026. 

My research focuses on multimodal learning, mixture-of-experts, and adaptive computation. I am interested in developing intelligent systems that can effectively integrate heterogeneous information, remain robust under missing or incomplete modalities, and dynamically select appropriate experts or computational resources for different inputs。 My current work explores multimodal representation learning, missing-modality robustness, and sparse expert routing, particularly in healthcare and other heterogeneous multimodal settings. More broadly, I am interested in understanding how routing and expert selection can improve the efficiency, robustness, and scalability of multimodal models.

My future research will explore adaptive intelligent systems across multimodal learning, reinforcement learning, mixture-of-experts, and multi-agent systems. I am particularly interested in multimodal reasoning, adaptive expert and agent selection, and decision-making under heterogeneous conditions, with the goal of developing more efficient, robust, and scalable intelligent systems.

 

I am welcoming potential collaboration and motivated students to work together for top-tier publication venues.

Date Position Institution name
2026 - 2026 Machine Learning Engineer TikTok Sydney

Language Competency
Chinese (Mandarin) Can read, write, speak, understand spoken and peer review
English Can read, write, speak, understand spoken and peer review

Year Citation
2026 Zhao, Y., Zheng, L. N., Zhang, W. E., & Chen, W. (2026). Price Equilibrium Routing: A Lightweight Framework for Expert Selection in Mixture-of-Experts. In Proceedings of the 38th Australasian Joint Conference on Artificial Intelligence Vol. 16371 (pp. 3-16). United States: Springer Nature Singapore.
DOI
2026 Zheng, L. N., Liang, W., Zhang, W. E., Xu, M., Maennel, O., & Chen, W. (2026). Lifting Manifolds to Mitigate Pseudo-Alignment in LLM4TS. In Proceedings of the ACM Web Conference 2026 (pp. 3764-3775). United States: ACM.
DOI
2025 Dong, C., Zheng, L., & Chen, W. (2025). Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis. In Proceedings, Part IV of the 20th International Conference Advanced Data Mining and Applications (ADMA 2024), as published in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 15390 LNCS (pp. 342-355). Singapore: Springer Nature.
DOI Scopus26 WoS19
2025 Zheng, L. N., Dong, C., Zhang, W. E., Yue, L., Xu, M., Maennel, O., & Chen, W. (2025). Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.2 (KDD 2025) Vol. 2 (pp. 4026-4037). New York, NY, USA: Association for Computing Machinery (ACM).
DOI Scopus6 WoS3
2025 Zheng, L. N., Zhang, W. E., Yue, L., Xu, M., Maennel, O., & Chen, W. (2025). Adaptive Spline Networks in the Kolmogorov-Arnold Framework: Knot Analysis and Stability Enhancement. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 4434-4443). United States: Association for Computing Machinery.
DOI Scopus1
2024 Dong, C. G., Zheng, L. N., Chen, W., Zhang, W. E., & Yue, L. (2024). SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series. In Proceedings of the IEEE International Conference on Knowledge Graph (ICKG 2023) (pp. 117-125). Online: IEEE.
DOI Scopus9 WoS9
2024 Zheng, L. N., Li, Z., Dong, C. G., Zhang, W. E., Yue, L., Xu, M., . . . Chen, W. (2024). Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024) (pp. 3405-3414). Boise, Idaho, USA: Association for Computing Machinery (ACM).
DOI Scopus12 WoS7
2024 Zheng, L. N., Dong, C. G., Zhang, W. E., Chen, X., Yue, L., & Chen, W. (2024). Devil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinction. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024) (pp. 3395-3404). New York, NY, USA: Association for Computing Machinery (ACM).
DOI Scopus5 WoS5
2024 George Dong, C., David Li, Z., Nathan Zheng, L., Chen, W., & Emma Zhang, W. (2024). Boosting Certificate Robustness for Time Series Classification with Efficient Self-Ensemble. In Proceedings of the International Conference on Information and Knowledge Management (pp. 477-486). ID, Boise: Association of Computing Machinery.
DOI Scopus1 WoS1

Year Citation
2025 Zheng, L. N., Zhang, W. E., Yue, L., Xu, M., Maennel, O., & Chen, W. (2025). Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots
and Advancing Stability.
2025 Zheng, L. N., Zhang, W. E., Guo, M., Xu, M., Maennel, O., & Chen, W. (2025). Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality
Inputs with Confidence-Guided Gate.
2024 Dong, C., Zheng, L., & Chen, W. (2024). Kolmogorov-Arnold Networks (KAN) for Time Series Classification and
Robust Analysis.
2024 Zheng, L. N., Dong, C. G., Zhang, W. E., Yue, L., Xu, M., Maennel, O., & Chen, W. (2024). Understanding Why Large Language Models Can Be Ineffective in Time
Series Analysis: The Impact of Modality Alignment.
2024 Zheng, L. N., Li, Z., Dong, C. G., Zhang, W. E., Yue, L., Xu, M., . . . Chen, W. (2024). Irregularity-Informed Time Series Analysis: Adaptive Modelling of
Spatial and Temporal Dynamics.
2024 Zheng, L. N., Dong, C. G., Zhang, W. E., Chen, X., Yue, L., & Chen, W. (2024). Devil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction
Prediction in Long Tail Distinction.
2023 Dong, C. G., Zheng, L. N., Chen, W., Zhang, W. E., & Yue, L. (2023). SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time
Series.

Adelaide University

ARTI 5001 Generative AI (S2 2026) -- Teaching Assistant

COMP 5800 Industrial Research Project (S2 2026) -- Teaching Assistant
 

 


University of Adelaide

COMP SCI 7210 & 7211 Foundation of Computer Science A & B (S1-2023, T2&T3-2023) -- Teaching Assistant

COMP SCI 7210 & 7211 Foundation of Computer Science A & B (T1&T2&T3-2024) -- Teaching Assistant

COMP SCI 7210 & 7211 Foundation of Computer Science A & B (T1&T2-2025) -- Teaching Assistant

COMP SCI 7306 Mining Big Data (S1 2025) -- Teaching Assistant

COMP SCI 7318 Deep Learning Fundamental (T3 2025) -- Teaching Assistant

COMP SCI 1400 AI Technology (S2 2025)  -- Teaching Assistant

COPM SCI 3344 Statistical Machine Learning (S2 2025)  -- Teaching Assistant

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