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AMSI Winter School 2023

AMSI Winter School 2023

Start Date

July 3, 2023

End Date

July 14, 2023

Registration

Closed

Image Credit: Australian Mathematical Sciences Institute (AMSI)

AMSI Winter School 2023

3–14 July | hosted by the Queensland University of Technology
https://ws.amsi.org.au/

AMSI Winter School is an annual two-week event designed for postgraduate students, early-career researchers and industry professionals in the mathematical sciences and related disciplines. Focusing on a specific theme, the School provides a scaffolded introduction to current research by national and international experts, presenting open problems or cutting-edge techniques.

To maximise the experience, several program extras including social events, specialist lectures, participants talks and activities will complement the academic program to encourage networking and collaboration.

AMSI Winter School 2023 on Modelling our Changing Biosphere will be hosted by the Queensland University of Technology from 3–14 July — virtual participation is also available.

Modelling Our Changing Biosphere

The focus of the AMSI Winter School 2023 is the Earth’s biosphere, which encompasses the interconnected physical and biological systems that support human life. We will explore how mathematical modelling can help to preserve and sustain our biosphere in the face of human-driven change.

We need mathematical models that can describe and predict the dynamics of these complex, high-dimensional nonlinear systems. Once we have a clear understanding of how these systems work, we need to develop decision-support tools which can help achieve more sustainable outcomes, in the face of logistical, environmental, and societal constraints.

It is expected that all participants will be proficient in calculus at an advanced (e.g., 2nd/3rd year) undergraduate level. We would prefer that participants also have some experience with statistics and/or data analysis. Applicants should address their level of knowledge and preparedness in the application.

Learn More

Participants will study:

  • Mathematical models developed to understand the biosphere. This includes ecosystem dynamics, evolutionary processes, conservation biology, and the management of renewable resources.
  • These models are generally high-dimensional, nonlinear, spatial and temporal.
  • Tools for fitting such complex models to realistic, noisy datasets.
  • Techniques to use these models to identify better decisions. “Better” might mean closer to optimal, but it might also mean more robust, more likely to be applied, or more likely to succeed.

Lecturers

  • DR NOKUTHABA SIBANDA SENIOR LECTURER, VICTORIA UNIVERSITY OF WELLINGTON
    Natural resources modelling: Focus on fisheries modelling
  • DR CARL BOETTIGER ASSOCIATE PROFESSOR, THE UNIVERSITY OF CALIFORNIA
    From Theory to Practice: Reinforcement Learning for Realistic Ecosystem Management
  • DR NADIAH KRISTENSEN RESEARCH SCIENTIST, NATIONAL UNIVERSITY OF SINGAPORE
    Evolutionary Game Theory: Mathematical Approaches to Understanding Cooperation.
  • DR MATTHEW ADAMS SENIOR LECTURER AND ARC DECRA FELLOW, QUEENSLAND UNIVERSITY OF TECHNOLOGY
    Analysing Sloppiness of Simulation Models: Examples in ecology