Hello! I'm 

Dr. Anna Hughes.

I'm a machine learning and data scientist with a research background in astrophysics.

Rates and availability details are provided upon request.

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 

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. 


Leadership Style

My leadership strategy will vary depending on the needs of my team, but I almost always use a coaching leadership style. I believe each team member has a unique set of skills, talents, and potential. My role is to provide guidance, support, and constructive feedback to help each member achieve their goals. I strive to create a healthy environment of trust and growth, prioritizing communication, collaboration, and continuous learning. 

A good leader facilitates personal and professional development, helping individuals unlock their full potential and excel both individually and as part of a team.