The project develops methods to use acoustic data for the identification of animals in the wild and in controlled settings. It is part of a broader effort to build AI-enabled methods to support biodiversity and sustainability research. The initial objective is to use deep learning techniques to perform acoustic species identification in real-time on low-cost sensing devices coupled to cloud-based backends. Ultimately, we are aiming to move to Edge-AI, ie.
Research projects in Information Technology
Displaying 111 - 113 of 113 projects.
Computational Modelling of Collective Decision Making
Our research group tries to decipher the rules that govern decision making in social groups, from animals that forage and hunt in groups to humans that work in teams.
Supervisor: Prof Bernd Meyer
Deep learning from less human supervision
Although deep learning has produces state of the art results on many problems, it is a data hungry technology requiring a lot of human supervision in the form of annotated data. Potential PhD topic include learning to learn and meta-learning, active learning, semi-supervised learning, multi-task learning, transfer learning, and learning representations for NLP. Techniques include deep generative models (eg auto-encoders and generative adversarial networks) and reinforcement/imitation learning algorithms for Markov Decision Processes.
Supervisor: Assoc Professor Reza Haffari