Research projects in Information Technology

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.

A large part of modern life is lived indoors such as in homes, offices, shopping malls, universities, libraries and airports. However, almost all of the existing location-based services (LBS) have been designed only for outdoor space. This is mainly because the global positioning system (GPS) and other positioning technologies cannot accurately identify the locations in indoor venues.

 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.