Seminar: Decoding the Invisible - Harnessing Time Series Intelligence

This is a joint hybrid event by the IEEE Computer Society ACT Chapter, the IEEE Geoscience and Remote Sensing Society ACT&NSW Joint Chapter, and the Canberra Data Scientists Meetup. After the talk, there will be free pizzas and soft drinks provided to encourage people to stay after the presentation and socialise with others. RSVP is required, please following instructions below for registration.

Title: Decoding the Invisible: Harnessing Time Series Intelligence
Speaker: Dr Ming Jin, Griffith University
Hosts:
- Yiqing Guo (IEEE GRSS)
- Warren Jin (IEEE CS)
- Yanchang Zhao (CDS)
Date: Tuesday 16 June 2026
Times:

• 3:30-5:00pm - Presentation
• 5:00-6:00pm - Food/Networking

Venue: Stringybark Room, Ground Floor of Synergy Building (B801, corner of North Science Road and Dickson way), CSIRO Black Mountain Science and Innovation Park, Acton ACT
Event Sponsor: SHURA

RSVP for In-Person Attendees: Please register here if you are attending in person: Attendance Sheet for In-Person Attendees. To assist in catering, please register by 6pm Sunday 14 June 2026. Parking information is also provided at the link.
Sign in at the Synergy Building: When you arrive at the Synergy Building foyer, please sign in using the iPad at the counter. Enter 'Yiqing Guo,' 'Warren Jin' or 'Yanchang Zhao' as the person you are visiting. Attach the printed name tag to your chest and wait to be collected. NO tailgating, please.
Sign out: Please ask one of the event organizers or a CSIRO helper to assist you with leaving the building and signing yourself off at the counter.
RSVP for Online Attendees: Please register here if you are attending online: Attendance Sheet for Online Attendees. A Teams meeting link will be sent to your email before the event.

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.

Bio: 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.