Joseph Cincotta
Joseph joined i-DEEL in 2023 to begin his higher-degree research into using AI and machine learning methods to identify animal behaviour. His research is part of a collaboration between Taronga Zoo and BEES at UNSW.
Joseph is an experienced computer scientist who has worked in software design and data science in Australia and the United States for over 25 years, designing technical solutions, patents and code for many multi-national corporations such as Facebook, and Intel. He has also contributed to multiple technology start-ups, acting as the founding Chief Technology Officer, building initial ideas into working commercial software systems that matured and were ultimately acquired.
Joseph has been engaged by NSW Health, The University of Sydney Faculty of Medicine and Health, and Taronga Conservation Society Animal Welfare and Conservation Science lab on machine learning-related research projects since 2019; over this time, his breadth of research projects includes large-scale machine learning pipelines for textual sentiment analysis, MRI neuroimaging, cardiac MRI and CT imaging, pulmonary CT imaging, Xray fluorescence spectral analysis, computer vision for animal behaviour assessment, clinical process digitisation in sleep studies, and large scale distributed biobanking.
In 2020, Joseph was fortunate to have the opportunity to work with the Welfare Conservation and Science team at Taronga. This collaboration evolved into his current academic research at UNSW.
Current areas of focus:
- Applying machine learning research to wildlife conservation and animal welfare
- Predictive analytics in healthcare
Project Title: Autonomous identification and classification of animal behaviours in captivity using computer vision and machine learning methods
Supervisors: Shinichi Nakagawa, Arcot Sowyma, Richard Kingsford
Project Description:
There are several research teams around the world currently evaluating AI to support the monitoring and assessment of animals in zoological settings. This data-driven understanding of animal behaviour can assist in optimising enrichment programs, habitat design and feeding strategies.
My research seeks to explore animal behaviour and welfare using AI and computer vision to identify and track individual animals throughout their daily lives, dynamically generating heatmaps, ethograms and behaviour budgets; essential tools in welfare assessment. Using an ensemble of machine learning methods and low-cost imaging sensors, this work seeks to use novel methods to collapse the latent space of imaging sensors to derive the essential information about the animals under observation, and also to build a model of those animals' behaviours, including individuation, using unsupervised models.
Joseph joined i-DEEL in 2023 to begin his higher-degree research into using AI and machine learning methods to identify animal behaviour. His research is part of a collaboration between Taronga Zoo and BEES at UNSW.
Joseph is an experienced computer scientist who has worked in software design and data science in Australia and the United States for over 25 years, designing technical solutions, patents and code for many multi-national corporations such as Facebook, and Intel. He has also contributed to multiple technology start-ups, acting as the founding Chief Technology Officer, building initial ideas into working commercial software systems that matured and were ultimately acquired.
Joseph has been engaged by NSW Health, The University of Sydney Faculty of Medicine and Health, and Taronga Conservation Society Animal Welfare and Conservation Science lab on machine learning-related research projects since 2019; over this time, his breadth of research projects includes large-scale machine learning pipelines for textual sentiment analysis, MRI neuroimaging, cardiac MRI and CT imaging, pulmonary CT imaging, Xray fluorescence spectral analysis, computer vision for animal behaviour assessment, clinical process digitisation in sleep studies, and large scale distributed biobanking.
In 2020, Joseph was fortunate to have the opportunity to work with the Welfare Conservation and Science team at Taronga. This collaboration evolved into his current academic research at UNSW.
Current areas of focus:
- Applying machine learning research to wildlife conservation and animal welfare
- Predictive analytics in healthcare
Project Title: Autonomous identification and classification of animal behaviours in captivity using computer vision and machine learning methods
Supervisors: Shinichi Nakagawa, Arcot Sowyma, Richard Kingsford
Project Description:
There are several research teams around the world currently evaluating AI to support the monitoring and assessment of animals in zoological settings. This data-driven understanding of animal behaviour can assist in optimising enrichment programs, habitat design and feeding strategies.
My research seeks to explore animal behaviour and welfare using AI and computer vision to identify and track individual animals throughout their daily lives, dynamically generating heatmaps, ethograms and behaviour budgets; essential tools in welfare assessment. Using an ensemble of machine learning methods and low-cost imaging sensors, this work seeks to use novel methods to collapse the latent space of imaging sensors to derive the essential information about the animals under observation, and also to build a model of those animals' behaviours, including individuation, using unsupervised models.