Research projects in Information Technology

This project will explore different deep learning techniques for Android app analyses, e.g., to detect Android malware, to identify common vulnerabilities, or to pinpoint repackaged Android apps. The project will start with exercising existing deep learning models to have a better understanding of how deep learning works in practice, so as to be able to quickly set up an appropriate environment for running deep earning-based experiments. Then, this project will investigate different representations of Android apps to prepare the inputs of deep learning models.

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.

 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.