Honours and Minor Thesis projects

Computational modelling of sign languages aims to develop technologies that can serve as means for understanding (i.e., recognising) and producing (i.e., generating) a particular sign language. The objective of this project is to study and contribute to the state-of-the-art in sign language segmentation.

This is a research project best for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.

Project Description:

The project is focused on developing game-based learning environments where the users’ trace or interaction data could be collected. The game-based environment needs to be designed to allow the users to navigate and explore at their own pace. Using the environment, the participants can practice their technical/professional skills from various options.

Project description

Mental health challenges disproportionately affect vulnerable populations, often due to limited access to traditional healthcare services. The rise of Generative AI offers a groundbreaking opportunity to bridge this gap by providing personalized, scalable, and accessible mental health support. This project, led out of Action Lab, aims to harness the potential of Generative AI to develop innovative technologies tailored for mental health interventions.

Computational modelling of sign languages aims to develop technologies that can serve as means for understanding (i.e., recognising) and producing (i.e., generating) a particular sign language. The objective of this project is to study and contribute to the state-of-the-art in sign language recognition.

This is a research project best for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.

Computational modelling of sign languages aims to develop technologies that can serve as means for understanding (i.e., recognising) and producing (i.e., generating) a particular sign language. The objective of this project is to study and contribute to the state-of-the-art in sign language generation.

This is a research project best for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.

Recognising conversational nonverbal behaviour for speakers and listeners, such as hand gestures, facial expressions and eye-gaze, is of great importance for natural interaction with intelligent agents. The objective of this project is to study and contribute to the state-of-the-art in conversational nonverbal behaviour recognition.

This is a research project best for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.

Generating conversational nonverbal behaviour for speakers and listeners, such as hand gestures, facial expressions and eye-gaze, is of great importance for natural interaction with intelligent agents. The objective of this project is to study and contribute to the state-of-the-art in conversational nonverbal behaviour generation.

This is a research project best for students who are independent and willing to take up challenges. It is also a good practice for students who wish to pursue further study at a postgraduate level.

Our groundbreaking research explores the intricate relationship between natural language processing (NLP) and electroencephalography (EEG) brain signals [1]. By leveraging advanced machine learning techniques, we aim to decode the neural patterns associated with language comprehension and production, ultimately enabling seamless communication between humans and machines. Our innovative approach has the potential to revolutionize brain-computer interfaces,speech recognition technologies, and assistive devices for individuals with communication impairments.

Planning is the reasoning side of acting in Artificial Intelligence. Planning automates the selection and the organisation of actions to reach desired states of the world as best as possible. For many real-world planning problems however, it is difficult to obtain a transition model that governs state evolution with complex dynamics.