We are happy to announce the 77th (2025) Annual General Meeting (AGM), to be followed by the Lancaster Lecture given by Associate Professor Andrew Zammit Mangion. The event will be held at 5:00PM (AEDT), Thursday March 27th at the University of Sydney Location F07.03.373.Carslaw.  We hope to see you all there!

The materials for the 2025 AGM will be progressively added here.

You can find the 2024 AGM minutes here.  

Any questions, please feel free to contact: secretary.nswbranch@statsoc.org.au.

Date:  Thursday, 27th March 2025
Time:
5:00pm - 5:30pm: AGM
5:30pm - 6:30pm: Lancaster Lecture 
6:30pm onwards: Dinner (at a nearby restaurant). See RSVP section below

Venue: F07.03.373.Carslaw, University of Sydney

RSVP: Please register via SSA NSW: 2025 AGM + Lancaster Lecture if you are attending the AGM and the Lancaster Lecture in person. Attending the dinner requires a separate RSVP and please register prior to the event. 

The AGM and Lancaster Lecture will also be available by Zoom.  Please register here. The link to the Zoom meeting is here.

Lancaster Lecture

Title : Spatio-temporal modelling of environmental data: Progress, challenges, and the road ahead

Speaker: Associate Professor Andrew Zammit Mangion 

Abstract: 

Understanding and predicting environmental processes is crucial for addressing global challenges such as climate change, biodiversity loss, and resource management. Each year, millions of dollars are invested in collecting data for this purpose, which are then used for modelling, drawing inferences, and guiding decision-making. Yet, modelling environmental data is challenging: The data are large, noisy, and often only an indirect observation of the quantity of interest, which typically evolves in both space and time. Given the multiple sources of uncertainty, statistical principles are not only useful, but essential for achieving reliable probabilistic forecasts. In this talk I will draw on classical approaches to modelling spatio-temporal data that involve two dominant approaches: descriptive and dynamical. I will then discuss some of the challenges faced by these classical approaches, and how emerging hybrid methods incorporating deep learning and AI offer unprecedented opportunities in modelling and computation. The lecture showcases several environmental applications and concludes with an outline of current directions and future challenges in this evolving field.

Biography: