Honours and Masters project

Collecting and analysing social media content (e.g., Reddit), along with using Google Trends, presents a great opportunity to develop social media epidemic intelligence. This approach can enhance the understanding of chronic conditions such as arthritis, back pain, and knee pain, as well as track associated areas such as treatments and risk factors, including obesity, diet, physical activity, and exercise. This approach can be also used to understand the community attitudes about these conditions, and see if there are changes over time as there as new public campaigns.

Sodium ions play a central role in membrane transport and cell homeostasis. Increased sodium concentration has been observed in brain tumors as well as neurodegenerative diseases including Alzheimer’s disease, multiple sclerosis and Huntington’s disease. While 23Na MRI of the human brain was first performed over 20 years ago, the low concentration of 23Na compared to 1H and rapid T2 decay resulted in low signal to noise (SNR) and long acquisition times, limiting its diagnostic feasibility. Recent advances in MR technology including the move to higher field strengths (e.g.

The security threat by quantum computing to almost all currently used digital signatures was triggered by the discovery of Shor’s quantum algorithm, which efficiently breaks the two problems underlying the security of these schemes, namely integer factoring, and elliptic curve discrete logarithms (ECDLP). When quantum computers become widespread, all security for the current digital signatures that are widely used to secure a wide range of systems is lost.

In this project, students and me will work together to develop a new technique for software testing and debugging. 

The subject under test may be AI/LLM. The technique may involve AI/LLM as well. 

Spontaneous synchronization is a common phenomenon occurring in diverse contexts, from a group of glowing fireflies at night or chirping crickets in a field to a network of coupled neurons in the brain. The study of synchronization helps to understand how uniform behaviors emerge in populations of heterogeneous neurons. At a macroscale level, the cortex operates in two classically-defined states: “synchronized” state which is characterized by strong low-frequency fluctuations and “desynchronized” state in which low-frequency fluctuations are suppressed.

The spectral density of a time series (a series of time ordered data points -- for example, daily rainfall in the Amazon or the monthly stocks of fish in the Pacific) gives substantial information about the periodic patterns hidden in the data. Learning a good model of the spectral density is usually done through parametric methods like autoregressive moving average processes [1] because non-parametric methods struggle to deal with the interesting “non-smooth” nature of spectral densities. This project aims to apply a powerful and new non-parametric smoothing technique to this problem.

Recent technological advances in micro and nano-fabrication technology and high-yield electrophysiology techniques allowed us to record the activity of hundreds/thousands of neurons simultaneously. This has spurred renewed interest in applying multi-electrode extracellular electrophysiology approaches in the field of neuroscience. Each electrode samples the activity of one or more neurons in its vicinity. One of the major challenges is to efficiently and robustly detect the spikes that individual neurons fire from the raw recorded electrophysiological signals.

The evolutionary back and forth between hosts and mobile genetic elements drives the innovation of remarkable molecular strategies to sense or conceal foreign genetic material. The Knott Lab uses bioinformatics, biochemistry, and structural biology to understand how CRISPR-Cas and other novel immune systems specifically sense DNA or RNA. We aim to better understand the function of nucleic acid sensors to harness their activity as tools for molecular diagnostics or as innovative biomedicines.

The rapid growth of electric vehicles (EVs) is transforming the transportation systems worldwide. Both EV fleets and private EVs are emerging as a cleaner and more sustainable component of urban mobility, forming an effective way to solve environmental problems and reduce commute costs in future smart cities. Due to the complex spatiotemporal behaviors of passengers and their travel patterns, the unmanaged electric charging demand from EVs may significantly impact the existing transportation and electrical power infrastructure.

Project Description

Teamwork is a big part of university life, but not all teams work smoothly. Students often face issues such as uneven contributions, unclear communication, or members falling behind. Teaching staff receive a large amount of peer feedback. But the information is often dispersed across multiple reports and can be time-consuming to interpret—particularly in large cohorts. A system that could automatically identify which teams are struggling, and why, would allow educators to offer timely, targeted support.