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] [University Profile]

About Me

I am currently an assistant professor at School of Information and Communication Technology (ICT), Griffith University. Prior to this, I obtained my Ph.D. degree in the Faculty of Information Technology at Monash University 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 TPAMI, among others. My research works were selected as Most Influential Papers, with some having become widely used baselines, gaining substantial recognition in the open-source community. I also server as an associate editor for Neurocomputing (JCR Q1 IF 6.0) and actively contributes as a senior program committee for prestigious international conferences and workshops.

I am dedicated to conducting high-impact research and open for collaborations. My research interests are broadly in (i) time series analysis, (ii) graph neural networks, and (iii) 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./visiting positions are available. I am looking for (i) well-matched, (ii) qualified, and (iii) 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. Due to the overwhelming volume of emails I receive daily, I kindly apologize in advance for not being able to respond to each one individually.

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 and reasoning models)
• 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 and agentic applications)
• Trustworthy AI (e.g., transparency and robustness)

Recent News

• [02/2025] Our paper Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting has been accepted by IEEE TPAMI (IF 20.8)
• [02/2025] I am honored to deliver a lecture-style tutorial on "Time Series Foundation Models" at AAAI 2025 (CORE A*)
• [01/2025] Our paper TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis has been accepted by ICLR 2025 (CORE A*) as an Oral (Top 1.8%) 🎊🥳
• [01/2025] Our paper Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts has been accepted by ICLR 2025 (CORE A*) as a Spotlight (Top 5%) 🎊🥳
• [01/2025] Our paper Towards Neural Scaling Laws for Time Series Foundation Models has been accepted by ICLR 2025 (CORE A*)
• [12/2024] We are organizing AI4TS-25 Workshop at TheWebConf 2025 (CORE A*) and now calling for fast-track submissions
• [12/2024] We are organizing WebST-25 Workshop at TheWebConf 2025 (CORE A*) and now calling for fast-track submissions
• [12/2024] Our tutorial on Time Series Analysis in the Web: Recent Advances and Future Trends has been accepted by TheWebConf 2025 (CORE A*)
• [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 (CORE A*) [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)

	Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts Xiaoming Shi^, Shiyu Wang^, Yuqi Nie^, Dianqi Li, Zhou Ye, Qingsong Wen, Ming Jin* International Conference on Learning Representations (ICLR Spotlight), 2025. [Paper] [GitHub]
	TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis Shiyu Wang^, Jiawei Li^, Xiaoming Shi, Zhou Ye, Baichuan Mo, Wenze Lin, Shengtong Ju, Zhixuan Chu*, Ming Jin* International Conference on Learning Representations (ICLR Oral), 2025. [Paper] [GitHub]
	Towards Neural Scaling Laws for Time Series Foundation Models Qingren Yao, Chao-Han Huck Yang, Renhe Jiang, Yuxuan Liang, Ming Jin*, Shirui Pan* International Conference on Learning Representations (ICLR), 2025. [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]
	Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting Ming Jin^, Guangsi Shi^, Yuan-Fang Li, Bo Xiong, Tian Zhou, Flora Salim, Liang Zhao, Lingfei Wu, Qingsong Wen, Shirui Pan IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025. [Paper]
	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] [GitHub]
	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]
	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

• [02/2025] Keynote talk of "Time Series Foundation Models" at AAAI 2025
• [10/2024] Invited talk of "Time Series Foundation Models" at MLBoost [Video]
• [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 "Learning on Continuous-Time Dynamic Graphs" at TGL Workshop [Slides] [Video]
• [02/2023] Invited talk of "Learning on Continuous-Time Dynamic Graphs" at AI TIME [Video]

Services

(a) Conferences:

• Program Chair, The Workshop of Artificial Intelligence for Web-Centric Time Series Analysis (AI4TS): Theory, Algorithms, and Applications at The Web Conference 2025
• Program Chair, The International Workshop on Spatio-Temporal Data Mining from the Web at The Web Conference 2025
• Session Chair, The Workshop of Deep Learning for Graphs (DL4G) at IJCNN 2023
• Organizer, The Tutorial of Time Series Analysis in the Web: Recent Advances and Future Trends at The Web Conference 2025
• Organizer, The Tutorial of Foundation Models for Time Series (FM4TS) at AAAI 2025
• Organizer, The Tutorial of Foundation Models for Time Series (FM4TS) at KDD 2024
• Organizer, The Tutorial of Graph Self-Supervised Learning: Taxonomy, Frontiers, and Applications at IJCNN 2023
• Area Chair, IEEE International Conference on Acoustics, Speech and Signal Processing, 2025
• Area Chair, Temporal Graph Learning (TGL) Workshop
• PC Member, The 34rd International Joint Conference on Artificial Intelligence (IJCAI, 2025)
• 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)
...

(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

• 2025 Trimester 3 1007ICT/7611ICT Computer Systems and Cyber Security, Griffith University
• 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