Ming of Griffith University, Australia

Abstract:  Every day, trillions of timestamped records (known as “time series”) are generated across domains such as transportation, environmental monitoring, renewable energy, and healthcare. Artificial intelligence (AI) tools like GPT‑5.5‑Codex and Claude Code have delivered impressive gains on high-visibility tasks; however, the progress in advanced time series intelligence (TSI) – the key bottleneck to truly transformative business upgrades – remains limited. This talk summarizes the progress and outlines a pathway toward general-purpose TSI. We begin with time series data foundations and a trajectory of building deep time series models. We then introduce large models for time series, covering both scaling laws and time series foundation models. We also present several of our recent advancements in building general-purpose time series engines and will discuss our vision towards a next-gen TSI with agentic AI. We also have several successful applications built on time series AI spanning domains such as healthcare and water monitoring. Through these cases, this talk demonstrates the potential of time series intelligence, identifies emerging research directions, and highlights how relevant techniques can advance AI ecosystems.

Biography:

Dr Ming Jin is a machine learning researcher with primary interests in time series and spatio-temporal data mining. He is currently a Lecturer (US Assistant Professor) at the School of Information and Communication Technology (ICT), Griffith University. He obtained his Ph.D. degree from Monash University, Australia, in 2024. Dr Jin has authored over 70 publications with over 7,000 citations and an h-index of 27 since 2021. His research outputs have been selected as Most Influential (x2) & ESI Hot (top 0.1%; x1) & ESI Highly Cited (top 1%; x5) Papers. He is a member of IEEE and a committee member of IEEE CIS Task Force on AI for Time Series and Spatio-Temporal Data. He also serves as Associate Editor for Neurocomputing and has contributed as Area Chairs or senior committee members for flagship AI conferences. Dr Jin received Dean's Commendation for Research Excellence at Griffith University and has been nominated as one of the IEEE Computing’s Top 30 Early Career Professionals in 2025.