Ming Jin Assistant Professor (Lecturer) @ Griffith University Office: 1.14_N44, 170 Kessels Rd, Nathan QLD 4111, Australia Email: mingjinedu AT gmail DOT com [Google Scholar] [GitHub] [LinkedIn] [Griffith 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 analytics and spatio-temporal data mining, with a proven track record of publishing high-impact papers in top-ranked venues, including NeurIPS, ICLR, ICML, KDD, and TPAMI, among others. My research outputs have been selected as Most Influential (x2) & ESI Hot (top 0.1%; x5) & ESI Highly Cited (top 1%; x6) Papers, with some having become widely used baselines such as Time-LLM (2024), Time-MoE (2025), TimeMixer 2.0 (2025), and TimeOmni series (2026), gaining substantial recognition in the open-source community. I am a committee member of IEEE CIS Task Force on AI for Time Series and Spatio-Temporal Data. I also serve as an associate editor for Neurocomputing (Q1 IF 6.5) and actively contribute as an area chair or senior program committee member for prestigious AI and data mining conferences.

My research interests are in (1) time series intelligence, (2) spatio-temporal data mining, and (3) multimodal learning & agentic AI with a focus on temporal settings in solving both fundamental and real-world problems.

📌 Long-term recruitment: PhD students & research interns. I am looking for (1) well-matched (w.r.t. research interests), (2) qualified, and (3) highly self-motivated candidates to work with me. If you are interested, please fill this Google Form and I will be in touch. Scholarships may be available in 2027. I have no quota for 2026 CSC visiting PhDs but open for next year applications. 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 focusing on the development of advanced machine learning techniques to understand complex patterns in temporal data for informed decision-making in complex environments. My team works on the following research topics (but not limited to):

• Time Series Intelligence (e.g., pattern discovery, foundation/reasoning models, and next-gen time series AI systems)
• Spatio-temporal Data Mining (e.g., learning on evolving relational data and AI for transportation/environmental/energy science)
• Multimodal AI (e.g., agentic AI solutions for healthcare and other high-value applications involving temporal analytics)
• Trustworthy AI (e.g., time series AI safety including model transparency and human-in-the-loop deployment)

We are also actively working on grounding time series AI in several important subdomains/applications, including (1) AI for physiological signals, (2) AI for transport and renewable energy, and (3) AI for environmental monitoring.

* Feel free to reach out if you are interested in collaborating on these topics.

Recent News

• [04/2026] Our paper STReasoner has been accepted by ACL 2026 (CORE A*) as a main conference paper.
• [02/2026] Honored to receive the Dean's Commendation for Research Excellence (2025)
• [02/2026] Our paper TimeOmni-1 has been accepted by ICLR 2026 (CORE A*). Check our demo here.
• [01/2026] Honored to give a talk on "Time Series Foundation Models" at AAAI 2026 (CORE A*)
• [12/2025] Honored to be named among IEEE Computing's Top 30 Early Career Professionals (2025)
• [12/2025] We have secured WUN Research Development Fund (2025) on vehicle-to-grid optimization
• [12/2025] Our paper Source-free Time Series Domain Adaptation with Prior Evaluation of Model Salience has been accepted by IEEE Transactions on Neural Networks and Learning Systems
• [11/2025] Our paper FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts has been accepted by KDD 2026 (CORE A*)
• [11/2025] Our paper TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation has been accepted by KDD 2026 (CORE A*)
• [11/2025] The 2nd International Workshop on Spatio-Temporal Data Mining from the Web has been accepted by WWW 2026
• [10/2025] A Survey on Diffusion Models for Time Series and Spatio-Temporal Data has been accepted by ACM Computing Survey
• [09/2025] Our paper ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models has been accepted by NeurIPS 2025 (CORE A*)
• [09/2025] Our benchmark paper of Self-Correction in LLMs has been accepted by NeurIPS 2025 (CORE A*)
• [09/2025] Our paper Multi-Scale Fine-tuning for Encoder-based Time Series Foundation Models has been accepted by NeurIPS 2025 (CORE A*)
• [09/2025] Our paper Continuous State Space Modeling on Dynamic Graphs has been accepted by NeurIPS 2025 (CORE A*)
• [09/2025] I have secured NVIDIA Academic Grant on developing next-gen time series analytical engines
• [09/2025] I will serve as an Area Chair for ICLR 2026 and ICASSP 2026
• [09/2025] Our tutorial on Foundation Models for Time Series Analysis has been accepted by AAAI 2026 (CORE A*)
• [08/2025] Our paper Test-time GNN Model Evaluation on Dynamic Graphs has been accepted by ICDM 2025 (CORE A*)
• [08/2025] Our applied research on salinity prediction using sparse drifter data has been accepted by CIKM 2025 (CORE A)
• [07/2025] Our applied research Large lithium-ion battery model for secure shared electric bike battery in smart cities has been accepted by Nature Communications (IF 15.7)
• [07/2025] Our applied research on supply chain forecasting with time series foundation model has been accepted by ITSC 2025
• [06/2025] Honored to serve on the Program Committee of IEEE CIS Task Force on AI for Time Series and Spatio-Temporal Data
• [05/2025] Our paper Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement has been accepted by ACL 2025 (CORE A*)
• [05/2025] Our paper Flow-based Reconcile Transformer for Hierarchical Time Series has been accepted by KDD 2025 (CORE A*)
• [05/2025] Our tutorial on Foundation Models for Spatio-Temporal Data Science has been accepted by KDD 2025 (CORE A*)
• [05/2025] Our paper Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting has been accepted by ICML 2025 (CORE A*)
• [04/2025] Our paper T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models has been accepted by IJCAI 2025 (CORE A*)
• [04/2025] Honored to deliver a lecture-style tutorial on "Time Series Analysis in the Web" at WWW 2025 (CORE A*)

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

	TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models Tong Guan, Zijie Meng, Dianqi Li, Shiyu Wang, Chao-Han Huck Yang, Qingsong Wen, Zuozhu Liu, Sabato Marco Siniscalchi, Ming Jin*, Shirui Pan* International Conference on Learning Representations (ICLR), 2026. [Paper] [GitHub] [Model Weights] [Demo]
	ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models Bosong Huang, Ming Jin*, Yuxuan Liang, Johan Barthelemy, Debo Cheng, Qingsong Wen, Chenghao Liu, Shirui Pan* Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. [Paper] [GitHub]
	T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models Yunfeng Ge, Jiawei Li, Yiji Zhao, Haomin Wen, Zhao Li, Meikang Qiu, Hongyan Li, Ming Jin*, Shirui Pan* International Joint Conference on Artificial Intelligence (IJCAI), 2025. [Paper] [GitHub]
	Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement Yaxuan Kong^, Yiyuan Yang^, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin*, Qingsong Wen* Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), 2025. [Paper] [Hugging Face]
	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] [Hugging Face]
	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] [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]
	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]
	Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection 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

• [01/2026] Keynote talk of "Time Series Foundation Models" (ver. 3) at AAAI 2026
• [11/2025] Keynote talk of "A Path Towards Time Series Intelligence" (ver. 1) at ACPR 2025
• [04/2025] Keynote talk of "Time Series Analysis in the Web: History, Preliminaries, and Challenges" at WWW 2025 [Slides]
• [02/2025] Keynote talk of "Time Series Foundation Models" (ver. 2) at AAAI 2025
• [10/2024] Invited talk of "Time Series Foundation Models" (ver. 1) 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" (ver. 1) 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

Conferences:

• Program Chair, The 2nd International Workshop on Spatio-Temporal Data Mining from the Web at The Web Conference 2026
• Program Chair, The Workshop on Rethinking Financial Time Series: Foundations, Frontiers, and Future Directions at ICAIF 2025
• 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
• Program Chair, The Workshop of Deep Learning for Graphs (DL4G) at IJCNN 2023
• Organizer, The Tutorial of Foundation Models for Time Series Analysis at AAAI 2026
• Organizer, The Tutorial of Foundation Models for Spatio-Temporal Data Science at KDD 2025
• 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, ICASSP 2025, NeurIPS 2025, ICASSP 2026, ICLR 2026, NeurIPS 2026
• Handled hundreds submissions on top-tier AI/ML/DM conferences e.g., NeurIPS, ICLR, ICML, IJCAI, AAAI, etc.

Journals:

• Associate Editor (Main areas: time series & spatio-temporal data mining), Neurocomputing (Q1; IF: 6.5)
• Handled 50+ submissions on top IEEE Trans. (e.g., TPAMI, TNNLS, TKDE, TCYB, etc.) and high-impact journals (e.g., Nature Machine Intelligence & Nature Communications)

Others:

• Program Committee, IEEE Task Force on AI for Time Series and Spatio-Temporal Data

Teaching

• 2026 Trimester 1 3016ICT Secure Development Operations, Griffith University
• 2025 Trimester 2 2808ICT/7623ICT Secure Development Operations, Griffith University
• 2024 Trimester 3 1007ICT/7611ICT Computer Systems and Cyber Security, Griffith University
• 2024 Semester 1 FIT5212 Data Analysis for Semi-Structured Data, Monash University
• 2023 Semester 1 Spring FIT5212 Data Analysis for Semi-Structured Data, Monash University