Honours and Minor Thesis projects

In Ophthalmology, patients are routinely scanned with multiple retinal imaging systems that provide complementary information to the clinicians. However, unlike other specialties, the images are not analysed by a radiologist and the treating ophthalmologist or optometrist is expected to analyse this data on their own. This is extremely time consuming, and difficult to achieve in clinical settings. Thus, AI models for disease detection have been extremely popular. 

Machine learning models have significantly improved the ability of autonomous systems to solve challenging tasks, such as image recognition, speech recognition and natural language processing. The rapid deployment of such models in safety critical systems resulted in an increased interest in the development of machine learning models that are robust and interpretable. 

Planning is the reasoning side of acting in Artificial Intelligence. Planning automates the selection and the organization 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.

Social media has become a dominant means for users to share their opinions, emotions and daily experience of life. A large body of work has shown that informal exchanges such as online forums can be leveraged to supplement traditional approaches to a broad range of public health questions such as monitoring depression, domestic abuse, cancer, and epidemics.

Linguistic phenomena have emerged and evolved over the span of thousands of years leading to many variations. Through this evolution, many linguistic structures and compositions have emerged or disappeared. In this project we will deploy an information-theoretic perspective to investigate the connections between linguistic phenomena (survival), and communication efficiency and emergence.

Machine Learning (ML) models are deployed in many safety-critical systems (such as self-driving cars, cancer detection software, etc.) to improve human decision-making. Therefore, safety is central to the success of many human-in-the-loop systems that deploy such ML models.

Since the 1990s, researchers have known that commonly-used public-key cryptosystems (such as RSA and Diffie-Hellman systems) could be potentially broken using efficient algorithms running on a special type of computer based on the principles of quantum mechanics, known as a quantum computer. Due to significant recent advances in quantum computing technology, this threat may become a practical reality in the coming years. To mitigate against this threat, new `quantum-safe’ (a.k.a.

Propositional satisfiability (SAT) is a well-known example of NP-complete problems. Although NP-completeness may be perceived as a drawback, it allows one to solve all the other problems in NP by reducing them to SAT and relying on the power of modern SAT solvers. This is confirmed by a wealth of successful examples of use cases for modern SAT solving, including generalisations and extensions of SAT as well as a wide variety of practical applications in artificial intelligence (AI).

Mini-CP https://www.info.ucl.ac.be/~pschaus/minicp.html is a minimal form of constraint programming solver, designed to allow for easy experimentation and learning. 

One of the most efficient approaches to discrete optimisation solving is using lazy clause generation, which is a hybrid SAT/CP approach to solving problems.  But MiniCP does not currently support this. 

Given a knowledge base describing the existing background constraints and assumptions about what is possible in the world as well as the prior experience of an autonomous agent on the one hand and probabilistic perception of the current state of the world of the autonomous agent, on the other hand, it is essential to devise and efficiently enumerate the most consistent world models that are likely to be valid under the prior knowledge in order to refine the agent’s up-to-date perception and take the most suitable actions.