Honours and Masters project

It is quite challenging to access to videos for people who are blind or have low vision (BLV), particularly creating audio descriptions that describe the scenes without interfering the dialogues in a video. There is also the challenge of providing additional information using multi-modal feedback, that is using non-speech audio and haptics.

People who are blind or have low vision (BLV) access documents using screen readers such as JAWS and NVDA. These screen readers emulates a cursor moving around the screen using arrow keys or various shortcut combinations.

In this project you will work on creating a 3D printed platform used with an iPad for people who are blind or have low vision. The platform will allow people to program in the Scratch visual programming language (https://scratch.mit.edu/) using 3D printed blocks.

With the rise of software systems ranging from personal assistance to the nation's facilities, software defects become more critical concerns as they can cost millions of dollars as well as impact human lives. Yet, at the breakneck pace of rapid software development settings (like CI/CD, Agile, Rapid Releases), Software Quality Assurance (QA) practices (e.g., code review and software testing) nowadays are still time-consuming.

In this project, the goal is to develop a new method (using computer vision and machine learning techniques) for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout and navigating as an active observer in which the predictions inform actions.

Learning appropriate prior distributions from replications of experiments is a important problem in the space of hierarchical and empirical Bayes. In this problem, we exploit the fact that we have multiple repeats of similar experiments and pool these to learn an appropriate prior distribution for the unknown parameters of this set of problems. Standard solutions to this type of problem tend to be of mixed Bayesian and non-Bayesian form, and are somewhat ad-hoc in nature.

Adaptively smoothing one-dimensional signals remains an important problem, with applications in time series analysis, additive modelling and forecasting. The trend filter provides an novel class of adaptive smoothers; however, it is usually implemented in a frequentist framework using tools like the lasso and cross-validation. Bayesian implementations tend to rely on posterior sampling and as such do not provide simple, sparse point-estimates of the underlying curve.

The increasing integration of Large Language Models (LLMs) into various sectors has recently brought to light the pressing need to align these models with human preferences and implement safeguards against the generation of inappropriate content. This challenge stems from both ethical considerations and practical demands for responsible AI usage. Ethically, there is a growing recognition that the outputs of LLMs must align with laws, societal values, and norms.

The world’s energy markets are transforming, and more renewable energy is integrated into the electric energy market. The intermittent renewable supply leads to unexpected demand-supply mismatches and results in highly fluctuating energy prices. Energy arbitrage aims to strategically operate energy devices to leverage the temporal price spread to smooth out the price differences in the market, which also generates some revenue.

The need: Early detection and diagnosis of eye conditions is critically important as many diseases, including diabetic retinopathy, glaucoma, and age-related macular degeneration, often show minimal or even no symptoms. Glaucoma is called the "silent thief of sight" since it progressively damages the eyes without any noticeable signs.