Ming Jin Assistant Professor (Lecturer) @ Griffith University Office: N44_1.14, Griffith University, Nathan QLD 4111, Australia Email: mingjinedu AT gmail DOT com [Google Scholar] [Github] [Linkedin]

About Me

I am currently an Assistant Professor at School of Information and Communication Technology (ICT), Griffith University. Before this, I was a Research Assistant in the Faculty of Information Technology at Monash University, where I also obtained my Ph.D. degree in 2024. I specialize in time series analysis and spatio-temporal data mining, with a good track record of publishing high-impact papers in top-ranked venues, including NeurIPS, ICLR, ICML, KDD, and IEEE TPAMI, among others. One of my research was selected as one of the most influential IJCAI papers by PaperDigest (2022-05), and another received 200+ citations and 1.3K+ GitHub stars within few months. I have also served as an Associate Editor for Journal of Neurocomputing (Q1; IF=6.0) and frequently as an Area Chair/PC member for prestigious conferences and workshops, such as NeurIPS (incl. TGL), ICLR, ICML, KDD (incl. MiLeTS), IJCAI (incl. AI4TS), AAAI, and more.

I am dedicated to conducting high-impact research and open for collaborations. My research interests are broadly in (1) time series analysis, (2) graph neural networks, and (3) multimodal learning, with a special focus on temporal settings (e.g., GNNs & FMs & LLMs for time series and spatio-temporal data) in solving both fundamental and real-world problems.

- Multiple Ph.D. positions are available. I am looking for well-matched, qualified, and self-motivated candidates to work with me. If you are interested, drop me an email with your application materials. See more information here about our lab.

Research Interests

I work in the field of time series and spatio-temporal data mining, primarily investigating the development and application of advanced machine learning techniques to model and predict complex patterns in temporal data. I focus on the following research topics:

• Time Series Analysis (e.g., foundation models, generation, and classification)
• Spatio-temporal Data Mining (e.g., representation learning and AI for transportation/climate science)
• Dynamic Graph Neural Networks (e.g., discrete-time/continuous-time dynamic graph pattern recognition)
• Multimodal Learning and Alignment (e.g., time series language models)
• Trustworthy AI (e.g., transparency and robustness)

Recent News

• [10/2024] Our paper Scaling to Billion Parameters for Time Series Foundation Models with Mixture of Experts has been accepted by NeurIPS 2024 TSALM Workshop 🎊🥳
• [10/2024] I will give another talk at MLBoost about "Time Series Foundation Models" 👉 [YouTube Live]
• [09/2024] Our paper Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective has been accepted by NeurIPS 2024 (CORE A*) 🎊🥳
• [09/2024] I am honored to be invited by MLBoost to give a talk on "Graph Neural Networks for Time Series" 👉 [Video]
• [09/2024] I will serve as an Area Chair of 2025 IEEE International Conference on Acoustics, Speech and Signal Processing
• [08/2024] I am honored to deliver a lecture-style tutorial on "Time Series Foundation Models" at KDD 2024. 👉 [Slides]
• [08/2024] Our paper A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection has been accepted by IEEE TPAMI (IF 20.8) 🎊🥳
• [07/2024] Our paper HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-term Forecasting has been accepted by CIKM 2024 (CORE A) 🎊🥳
• [06/2024] I am honored to serve as an Associate Editor of Neurocomputing (Q1; IF 6.0)
• [05/2024] Our tutorial on Foundation Models for Time Series (FM4TS) has been accepted by KDD 2024 (CORE A*) 🎊🥳
• [05/2024] Our paper Position: What Can Large Language Models Tell Us about Time Series Analysis has been accepted by ICML 2024 (CORE A*) 🎊🥳
• [05/2024] I will serve as an Area Chair of TGL Workshop 2024
• [04/2024] I am honored to be invited by Talk on MLLM to give a talk on the topic of Time-LLM: Time Series Forecasting by Reprogramming Large Language Models 👉 [Slide] [Video]
• [04/2024] Our paper Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects has been accepted by IEEE TPAMI (IF 20.8) 🎊🥳
• [01/2024] Our paper Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values has been accepted by Information Fusion (IF 14.7) 🎊🥳

Selected Publications [Full List] (^Co-first author; *Corresponding author)

	Scaling to Billion Parameters for Time Series Foundation Models with Mixture of Experts Xiaoming Shi^, Shiyu Wang^, Yuqi Nie^, Dianqi Li, Zhou Ye, Qingsong Wen, Ming Jin* TSALM Workshop at the Conference on Neural Information Processing Systems (NeurIPS), 2024. [Paper] [GitHub]
	A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Ming Jin^, Huan Yee Koh^, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I Webb, Irwin King, Shirui Pan IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. [Paper] [GitHub]
	Time-LLM: Time Series Forecasting by Reprogramming Large Language Models Ming Jin^, Shiyu Wang^, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen International Conference on Learning Representations (ICLR), 2024. [Paper] [GitHub]
	Position: What Can Large Language Models Tell Us about Time Series Analysis Ming Jin^, Yifan Zhang^, Wei Chen^, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen International Conference on Machine Learning (ICML), 2024. [Paper]
	Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang Annual Conference on Neural Information Processing Systems (NeurIPS), 2024. [Paper]
	Foundation Models for Time Series Analysis: A Tutorial and Survey Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen International Conference on Knowledge Discovery and Data Mining (KDD), 2024. [Paper] [Tutorial]
	Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. [Paper] [GitHub]
	A Survey on Diffusion Models for Time Series and Spatio-Temporal Data Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Jiang Bian, Shirui Pan, Qingsong Wen arXiv preprint, 2024. [Paper] [GitHub]
	HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting Shubao Zhao^, Ming Jin^, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang International Conference on Information and Knowledge Management (CIKM), 2024. [Paper]
	Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, Shirui Pan, Vincent S Tseng, Yu Zheng, Lei Chen, Hui Xiong arXiv preprint, 2023. [Paper] [GitHub]
	Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs Ming Jin, Yuan-Fang Li, Shirui Pan Annual Conference on Neural Information Processing Systems, (NeurIPS), 2022. [Paper] [GitHub]
	Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan IEEE Transactions on Knowledge and Data Engineering, (TKDE), 2022. [Paper] [GitHub]
	Graph Self-Supervised Learning: A Survey Yixin Liu^, Ming Jin^, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, S Yu Philip IEEE Transactions on Knowledge and Data Engineering, (TKDE), 2022. [Paper]
	ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning Ming Jin, Yixin Liu, Yu Zheng, Lianhua Chi, Yuan-Fang Li, Shirui Pan International Conference on Information & Knowledge Management, (CIKM), 2021. [Paper] [GitHub]
	ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning Yu Zheng, Ming Jin*, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen IEEE Transactions on Knowledge and Data Engineering, (TKDE), 2021. [Paper] [GitHub]
	Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan International Joint Conference on Artificial Intelligence, (IJCAI), 2021. [Paper] [GitHub]

Selected Talks

• [09/2024] Invited talk of "Graph Neural Networks for Time Series" at MLBoost [Video]
• [08/2024] Keynote talk of "Time Series Foundation Models" at KDD 2024 [Slides]
• [05/2024] Invited talk of "What Can Large Language Models Tell Us About Time Series Analysis" at Salesforce [Slides]
• [04/2024] Invited talk of "Time Series Forecasting by Reprogramming Large Language Models" at "Talk on MLLM" [Slides][Video]
• [02/2024] Invited talk of "Time Series Forecasting by Reprogramming Large Language Models" at AI TIME [Slides] [Video]
• [10/2023] Invited talk of "Graph Neural Networks for Time Series" at TGL Workshop [Slides]
• [03/2023] Invited talk of "Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs" at TGL Workshop [Slides] [Video]
• [02/2023] Invited talk of "Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs" at AI TIME [Video]

Services

a) Conferences:

• Organizer, The Tutorial of Graph Self-Supervised Learning: Taxonomy, Frontiers, and Applications at The 2023 International Joint Conference on Neural Networks
• Session Chair, Deep Learning for Graphs (DL4G) at The 2023 International Joint Conference on Neural Networks
• Area Chair, IEEE International Conference on Acoustics, Speech and Signal Processing, 2025
• Area Chair, Temporal Graph Learning (TGL) Workshop
• Reviewer, The 13th International Conference on Learning Representations (ICLR, 2025)
• PC Member, The 1st Workshop on Time Series in the Age of Large Models (NeurIPS, 2024)
• PC Member, The 3rd Workshop of Artificial Intelligence for Time Series Analysis (IJCAI, 2024)
• Reviewer, The 38th Conference on Neural Information Processing Systems (NeurIPS, 2024)
• Reviewer, The 41st International Conference on Machine Learning (ICML, 2024)
• PC Member, The 33rd International Joint Conference on Artificial Intelligence (IJCAI, 2024)
• PC Member, The 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD, 2024)
• PC Member, Temporal Graph Learning (TGL) Workshop (NeurIPS, 2023)
• Reviewer, The 12th International Conference on Learning Representations (ICLR, 2024)
• PC Member, The 38th Annual AAAI Conference on Artificial Intelligence (AAAI, 2024)
• Reviewer, The 37th Conference on Neural Information Processing Systems (NeurIPS, 2023)
• PC Member, The 9th SIGKDD International Workshop on Mining and Learning From Time Series (MiLeTS) (SIGKDD, 2023)
• PC Member, The 2nd Workshop of Artificial Intelligence for Time Series Analysis (IJCAI, 2023)
• PC Member, The 32nd International Joint Conference on Artificial Intelligence (IJCAI, 2023)
• PC Member, The 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD, 2023)

b) Journals:

• Associate Editor, Journal of Neurocomputing (Q1; IF: 6.0)
• Reviewer, IEEE Transactions on Pattern Analysis and Machine Intelligence (IF: 23.6)
• Reviewer, IEEE Transactions on Cybernetics (IF: 19.118)
• Reviewer, IEEE Transactions on Knowledge and Data Engineering (IF: 9.235)
• Reviewer, IEEE Transactions on Neural Networks and Learning Systems (IF: 14.255)
• Reviewer, IEEE Transactions on Geoscience and Remote Sensing (IF: 8.125)
• Reviewer, IEEE Transactions on Dependable and Secure Computing(IF: 7.3)
• Reviewer, Technological Forecasting & Social Change (IF: 10.884)
• Reviewer, Future Generation Computer Systems (IF: 7.307)
• Reviewer, Neural Computing and Applications (IF: 5.130)

Teaching

• 2024 Trimester 3 1007ICT/7611ICT Computer Systems and Cyber Security, Griffith University
• 2024 Spring FIT5212 Data Analysis for Semi-Structured Data, Monash University
• 2023 Spring FIT5212 Data Analysis for Semi-Structured Data, Monash University