data-driven methods

Over the past two decades, I have developed AI tools to support decision-making in biodiversity conservation. Methods such as Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and adaptive management help us plan under uncertainty, incorporate feedback, and learn from dynamic ecological systems. These techniques have supported efforts from invasive species management to threatened species recovery, helping balance short-term actions with long-term outcomes. 

Yet some of the most important insights lie beyond the algorithms themselves. Reversing the biodiversity extinction crisis requires more than technical capability: it demands co-design with ecologists, social scientists, landholders, and policymakers. It calls for a shift from what can we build? to what does biodiversity actually need?

This is the mission of the Environmental Informatics Hub at Monash, a new initiative I lead within the Faculty of IT: to develop AI that supports the future of biodiversity, not just the future of technology. But isolated efforts won’t be enough. If we are serious about halting biodiversity loss, we need a globally coordinated approach, a clear framework, much like the Global Biodiversity Framework (GBF), that defines the AI capabilities required to meet conservation targets.

Learning Objectives:

1. Learn about how AI can help support decision-making for the conservation of biodiversity
2. Learn about the needs and challenges in developing interpretable, non-autonomous and poor data AI systems

This symposium aims to bring together researchers from the interdisciplinary fields of machine learning and data-driven methods with mechanical dynamics to foster  collaboration and promote the adoption of hybrid algorithms in engineering applications. We anticipate presentations from academic institutions and industry, reflecting the growing prominence of data-driven approaches in science-based decision-making.  Topics of interest include, but are not limited to:

• Data-driven analysis and modeling of dynamics
• Physics-constrained learning
• Learning-based system identification
• Data-enhanced physical simulations
• Industrial applications of deep learning for engineering dynamics
• Data Science
• Data-based control
• Error bounds and guarantees in data-based methods

We look forward to receiving contributions to this mini-symposium for a lively exchange in this important aspect of research!

Abstract submission is currently open until April 17, 2025. Further instructions and templates are available on the conference homepage: https://colloquia.gacm.de/organisation.

Institutions

• German Association for Computational Mechanics (GACM)

As the planet faces accelerating environmental degradation and biodiversity loss, emerging technologies have an increasingly vital role to play in enhancing conservation efforts. Artificial intelligence (AI), in particular, is emerging as a powerful tool to address complex environmental challenges, from monitoring ecosystems and predicting habitat changes to combatting illegal wildlife trafficking. 

In this webinar, Professor Tshilidzi Marwala, United Nations Under-Secretary-General and Rector of the United Nations University (UNU), will outline the transformative potential of AI for biodiversity and conservation. He will examine how AI can support the ambitious targets of the Kunming-Montreal Global Biodiversity Framework by enabling scalable, data-driven solutions that enhance our ability to understand and protect our ecosystems. 

Drawing on UNU’s global work at the intersection of AI and sustainability, Prof. Marwala will also reflect on the ethical, environmental, and equity-related challenges associated with AI, such as algorithmic bias, the digital divide, and its carbon footprint. Realising the promise of AI for biodiversity conservation will depend on our ability to govern it wisely, ensuring it serves as a force for equity, sustainability, and environmental stewardship.

Learning Objectives: 

By the end of this webinar, participants will have developed their ability to:

• Identify key facing global biodiversity and conservation efforts.
• Describe the potential applications and limitations of artificial intelligence in addressing these challenges.
• Analyse real-world case studies illustrating how AI is being used to support biodiversity monitoring and conservation.
• Evaluate the role of the United Nations University in advancing research and innovation at the intersection of AI, sustainability, and biodiversity.

Estimating the geographic range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of observations of hundreds of thousands of species in addition to multi-modal image and text data. In this talk, I will present recent work from my group on deep learning-based solutions for estimating species’ ranges from incomplete data. I will also discuss some of the open challenges that exist in this space.  

Learning Objectives:  

1. Understand the capabilities of current deep learning methods for species range estimation.
2. Recognise the limitations of these models in the context of current open challenges in this space