Honours and Masters project

Demand forecasting is the basis for a lot of managerial decisions in companies. During the last four decades, researchers and practitioners have developed numerous quantitative and qualitative demand forecasting models including statistical, machine learning, judgmental, and simulation methods. Several endogenous and exogenous variables can influence the dynamics of demand, and hence a single statistical model that only consists of historical sales data is often insufficient to produce accurate forecasts.

This project focuses on understanding and applying control charts as a tool for monitoring and improving process quality. It involves designing some basic control charts and evaluating their performance in detecting process variations under different conditions. The evaluation will be based on key metrics such as Average Run Length (ARL), false alarm rate, and detection speed, providing insights into the effectiveness of various chart types in maintaining quality standards.

As part of this project, you will work closely with a community organisation or NGO (this can either be an organisation that you have existing links with or we will connect you with one of our partner NGOs). Working in collaboration with the org, you will find out challenges they face in giving voice to their communities/beneficiaries that can be addressed through social media (for instance, perhaps they want to run an awareness raising campaign about the difficulties faced by the community and they want the communities to be very involved in this).

This project aims to develop effective machine learning algorithms for detecting deepfake videos, which have become a significant concern for disinformation and cybersecurity. The objectives include pre-processing the data for feature extraction, and training machine learning models to accurately classify videos as either real or manipulated. The methodology involves using advanced techniques such as convolutional neural networks, recurrent neural networks or video vision transformer models to analyse visual and temporal patterns in the videos.

This project aims to develop privacy-preserving deepfake detection techniques that enable accurate and secure identification of synthetic audio and video content without exposing sensitive user data. Traditional detection methods often require access to raw audio or visual inputs, raising significant privacy concerns, especially in scenarios involving personal or biometric data.

Mis/disinformation (also known as fake news), in the era of digital communication, poses a significant challenge to society, affecting public opinion, decision-making processes, and even democratic systems. We still know little about the features of this communication, the manipulation techniques employed, and the types of people who are more susceptible to believing this information.

This project extends upon Prof Whitty's work in this field to address one of the issues above.

The DNA inside a cell is not randomly distributed but rather organized in a structure called chromatin. This non-random distribution has important implications for the functioning of cellular programs. The basic building block of this organisation system is the nucleosome. The nucleosome consists of a short piece of DNA wrapped around a protein core, with millions of nucleosomes are present in the cell’s nucleus. The orientation of nucleosomes with respect to each other and the way they pack the genomic DNA determine the architecture of chromatin.

Antimicrobial resistance poses significant medical challenge worldwide. Misuse, overuse or suboptimal dosing of antibiotics are major driving factors of antimicrobial resistance. Pharmacokinetic/pharmacodynamic (PK/PD) modelling is critical for designing optimal antimicrobial therapies to maximise the efficacy and minimise the emergence of resistance. However, conventional PK/PD modelling is generally based on viable counting on agar plates after overnight culture and employs a population approach.

Behavioural manifestations of epileptic seizures (ESs) and certain non-epileptic seizures (psychogenic non-epileptic seizures, or PNESs) have considerable overlap, and so discerning between these solely based on clinical criteria is difficult.  Video EEG (electroencephalogram) monitoring (VEM) has high resource demands and is also expensive.  We endeavour to classify seizures based on non-invasive measures.

Digital signatures are asymmetric cryptographic schemes used to validate the authenticity and integrity of digital messages or documents. The signer uses their private key to generate a signature on a message. Then, this signature can be validated by any verifier who knows the signer’s corresponding public key. Sometimes a digital message might require signatures from a group of signers. The naïve method to achieve this goal is collecting distinct signatures from all signers.