Ming Jin's Homepage

Ming Jin

Assistant Professor (Lecturer) @ Griffith University

Office: N44_2.11, 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 130+ citations and 1K+ GitHub stars within few months. I have also served as an Associate Editor for Elsevier 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. We are seeking well-matched, qualified, and self-motivated candidates. Please send your application materials via email, and see more information here.

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)
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., explainability and robustness)

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

	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]
	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]
	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 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]

Talks

[05/2024] What Can Large Language Models Tell Us About Time Series Analysis at Salesforce AI Research [Slides]
[05/2024] Time-LLM: Time Series Forecasting by Reprogramming Large Language Models at ICLR-24 [Video]
[04/2024] Time-LLM: Time Series Forecasting by Reprogramming Large Language Models at "Talk on MLLM" [Slides][Video]
[02/2024] Time-LLM: Time Series Forecasting by Reprogramming Large Language Models at AI TIME [Slides] [Video]
[10/2023] A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection at TGL Workshop [Slides]
[03/2023] Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs at TGL Workshop [Slides] [Video]
[02/2023] Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs at AI TIME [Video]
[10/2022] Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs at NeurIPS-22 [Slides] [Video]
[09/2021] ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning at CIKM-21 [Slides]
[07/2021] Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning at IJCAI-21 [Slides]

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, Temporal Graph Learning (TGL) Workshop (NeurIPS, 2024)
PC Member, The Third Workshop of Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications (IJCAI, 2024)
Reviewer, Thirty-eighth 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, Thirty-seventh 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 Second Workshop of Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications (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)
PC Member, IEEE World Congress on Computational Intelligence (WCCI, 2022)
PC Member, IEEE International Conference on Data Science and Systems (DSS, 2021)

b) Journals:

Associate Editor, Elsevier 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 Spring FIT5212 Data Analysis for Semi-Structured Data, Monash University
2023 Spring FIT5212 Data Analysis for Semi-Structured Data, Monash University