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Research projects in Information Technology

Displaying 191 - 196 of 196 projects.

Indoor Data Management

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.

Deep learning for mobile app analytics

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.

Supervisor: Dr Li Li

Location-based Social Networks

This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in three unique aspects: utilizing the geographical locations of queries and social network data to provide more relevant results; acknowledging and handling inherent uncertainties in the data; and exploiting knowledge graphs to produce intelligent search results. Expected outcomes of this project include a next-generation social network search system and enhanced international collaborations.

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.

Neural Machine Translation for Low-Resource Languages

The proposed project aims to develop new methodologies for developing NMT systems between extremely low-resource languages and English. Recent advances in neural machine translation (NMT) are a significant step forward in machine translation capabilities. However, "NMT systems have a steeper learning curve with respect to the amount of training data, resulting in worse quality in low-resource settings".

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