Machine Learning to Monitor Harmful Algal Blooms

Project Description: Many lakes in New Zealand and other parts of the world are frequently affected by harmful algal blooms, where a rapid buildup of algae mass becomes toxic to local ecosystems, aquaculture and even human health. Freshwater scientists monitor these algal blooms by collecting water samples and analysing them in a lab to estimate algal concentration, to flag when water becomes unsafe. Our project aims to use machine learning techniques on satellite image data to detect harmful...

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Modelling and Explaining Climate using Machine Learning

Project Description: Regional Climate Models (RCMs) are physics-based models that simulate the regional climate. However, RCMs are extremely computationally expensive to run. The goal is to develop a hybrid RCM emulator, driven entirely by AI but guided through physics, with run times at least 1,000 times faster than current RCMs. Additionally, it will dramatically reduce RCM computing costs and enable better uncertainty quantification of climate-related risks for New Zealand. From the...

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Digital Companion Agent for Youth Mental Health

Project Description: There are many online tools that can improve resilience and help with common conditions such as mild-to-moderate symptoms of anxiety and depression. Such digital tools, can apply evidence-based techniques, such as Cogntive-Behavioural Therapy (CBT). And, unlike a human counsellor, they can be available anytime of day or night, and can be used by young people who may be reluctant about the stigma of seeing a mental health professional. While such tools can be effective,...

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Wearable Sensing for In-the-moment Anxiety Interventions

Project Description: What if your fitness bracelet could immediately sense your anxiety, and then signal your phone with a reminder that NOW is a good time to apply a relaxation technique that you've learned earlier? We are working with the Tackling Anxiety through Innovation (TAI) initiative at the National Institute for Health Innovation (NIHI) to help with ubiquitous detection of spikes in stress/anxiety. And we want to achieve this with low-cost and comfortable devices. A key challenge is...

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Using Machine Learning to Predict Dementia

Project Description: Dementia is a progressive impairment characterised by declining cognitive functions starting with minor memory problems but leading to deteriorating memory, orientation, language, and learning processes. In Aotearoa New Zealand, the estimated prevalence of dementia in 2020 was 1.4%, but in 2050, the number of people with dementia is expected to double. Since there is very little epidemiological information in New Zealand, routinely collected data could be a source of...

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