Hello! I'm 

Dr. Anna Hughes.

I'm a machine learning engineer with a research background in astrophysics.

Machine Learning & Data Science

Neural Network Compression - Large Language Models

Large language models owe their sophistication not just to the underlying algorithms and training data, but also to their large number of trainable parameters. The rise of LLMs and other large generative models comes at a extraordinary cost; the energy required during the training and fine-tuning processes.
I led a team of 6 researchers to find solutions. As a team, we developed 4 novel approaches to neural network compression in large language models. We used machine learning and optimisation to identify and remove weak neurons from the network.

Time Series Analysis

I have worked extensively with time series data in multiple projects and across industries. Examples include:

Anomaly Detection

Anomaly detection algorithms - designed to identify anomalous data or events in some dataset - are immensely useful across a range of fields. I have experience identifying anomalous data using:

Nonlinear Regression

Nonlinear regression played a pivotal role in my Ph.D. research. I used radio observations of low-mass stars to 

CO2 Emissions Forecasting

Quantum Computing

Quantum Machine Learning

Quantum machine learning integrates quantum components into machine learning problems; typically classical data is converted into quantum data, a set of computations is performed, and the outgoing data is converted back into classical data.

I have experience using both quantum annealers and gate-based quantum computers for clustering, support vector machines, and neural networks.

Quantum Inspired Optimisation

While quantum computers are still in their infancy, quantum-inspired algorithms such as quadratic unconstrained binary optimisation (QUBO) can be used to solve a wide range of optimisation problems with classical hardware.

I have extensive experience identifying applications of QUBO to a variety of problems such as neural network compression, feature selection, and representative selection.