Dr Tong Liu

School of Computer Science

Lecturer in Pervasive Data Science

T.Liu@sheffield.ac.uk
+44 114 222 1903

Regent Court (DCS)

Full contact details

Dr Tong Liu
School of Computer Science
Regent Court (DCS)
211 Portobello
Sheffield
S1 4DP

Profile

Dr. Tong Liu holds the position of Lecturer in Pervasive Data Science at the Department of Computer Science, University of Sheffield, U.K. Previously, he served as a Research Associate at the Sargent Centre for Process Systems Engineering, Imperial College London (2022-2023) and University of Sheffield (2021-2022). He also contributed as an AI researcher with the Shell during his time at Imperial. Before that, he was a visiting researcher at the School of Electronics and Computer Science, University of Southampton (2018-2019). Dr. Liu obtained his B.S. degree in Automation and Ph.D. degree in Control Theory and Engineering in 2016 and 2021, respectively, from Chongqing University, China.

He conducted research at the intersection of machine learning, data science, and decision-making. His primary focus lies in advancing sustainable and lifelong AI methodologies tailored for pervasive data analytics to support intelligent decision-making. His research spans various application domains including industrial automation, precision agriculture, and healthcare informatics. He has published 40+ journal and conference papers, including IEEE TSP, IEEE TKDE, IEEE TCYB, IEEE TNNLS, IEEE TCST, IEEE TIE, AAAI, CIKM.

Research interests: • Lifelong learning, online learning, transfer learning;
• Time series and streaming data analytics;
• Adaptive modeling and monitoring of dynamic processes;
• Safe learning and optimization, learning-based control;
• Applications in precision agriculture, smart healthcare and industrial automation.

Publications

Journal articles

Zuo Z, Zhang H, Ma L, Liu T & Liang S (2024) Leak Detection for Natural Gas Gathering Pipelines Under Multiple Operating Conditions Using RP-1dConvLSTM-AE and Multimodel Decision. IEEE Transactions on Industrial Electronics, 71(6), 6263-6273.
Kong X, He Y, Song Z, Liu T & Ge Z (2024) Deep Probabilistic Principal Component Analysis for Process Monitoring. IEEE Transactions on Neural Networks and Learning Systems, 1-15.
Yu D, Liu T, Wang K, Li K, Mercangöz M, Zhao J, Lei Y & Zhao R (2024) Transformer based day-ahead cooling load forecasting of hub airport air-conditioning systems with thermal energy storage. Energy and Buildings, 308, 114008-114008.
Li Z, Liu T, Peng X, Ren J & Liang S (2024) An AIS-based deep learning model for multi-task in the marine industry. Ocean Engineering, 293, 116694-116694.
He X, Liu T, Zhang Y & Xie R (2024) Soft Sensor Modeling Based on Vector-Quantized Weighted-Wasserstein VAE for Polyester Polymerization Process. IEEE Transactions on Industrial Informatics, 1-10.
Zhao Y, Liu T, Zhu Y-P, Liu Z, Han Q & Ma H (2024) Lifelong Monitoring of Bearing-Rotor Systems Over Whole Life Cycle: An Emerging Paradigm. IEEE Transactions on Industrial Informatics, 1-10.
Liu T, Chen S, Li K, Gan S & Harris CJ (2023) Adaptive Multioutput Gradient RBF Tracker for Nonlinear and Nonstationary Regression. IEEE Transactions on Cybernetics, 53(12), 7906-7919.
Liu T, Chen S, Yang P, Zhu Y, Mercangoz M & Harris C (2023) Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement. IEEE Transactions on Control Systems Technology. View this article in WRRO
Liu T, Tian Z, Chen S, Wang K & Harris CJ (2023) Deep Cascade Gradient RBF Networks With Output-Relevant Feature Extraction and Adaptation for Nonlinear and Nonstationary Processes. IEEE Transactions on Cybernetics, 53(8), 4908-4922.
Liu T, Chen S, Liang S, Gan S & Harris CJ (2022) Multi-Output Selective Ensemble Identification of Nonlinear and Nonstationary Industrial Processes. IEEE Transactions on Neural Networks and Learning Systems, 33(5), 1867-1880.
Liu T, Chen S, Liang S, Du D & Harris CJ (2020) Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes. Journal of Process Control, 93, 53-65.
Liu T, Chen S, Liang S & Harris CJ (2020) Growing and Pruning Selective Ensemble Regression for Nonlinear and Nonstationary Systems. IEEE Access, 8, 73278-73292.
Liu T, Chen S, Liang S, Gan S & Harris CJ (2020) Fast Adaptive Gradient RBF Networks For Online Learning of Nonstationary Time Series. IEEE Transactions on Signal Processing, 68, 2015-2030.
Liu T, Liang S, Xiong Q & Wang K (2020) Data-Based Online Optimal Temperature Tracking Control in Continuous Microwave Heating System by Adaptive Dynamic Programming. Neural Processing Letters, 51(1), 167-191.
Liu T, Liang S, Xiong Q & Wang K (2020) Integrated CS optimization and OLS for recurrent neural network in modeling microwave thermal process. Neural Computing and Applications, 32(16), 12267-12280.
Liang S, Ye C, Xiong Q, Wang Z & Liu T (2019) Model-free adaptive control for microwave heating process with actuator saturation constraint. Journal of Microwave Power and Electromagnetic Energy, 53(2), 81-93.
Liu T, Liang S, Xiong Q & Wang K (2018) Adaptive Critic Based Optimal Neurocontrol of a Distributed Microwave Heating System Using Diagonal Recurrent Network. IEEE Access, 6, 68839-68849.
Liu T, Wang X, Yang P, Sheng C & Harris C () Unsupervised transfer aided lifelong regression for learning new tasks without target output. IEEE Transactions on Knowledge and Data Engineering. View this article in WRRO
Zhou M, Wang X, Liu T, Yang Y & Yang P () Integrating visualized automatic temporal relation graph into multi-task learning for Alzheimer’s disease progression prediction. IEEE Transactions on Knowledge and Data Engineering. View this article in WRRO

Research group: Pervasive Computing Group