0009-0008-3517-2108 About
I specialise in machine learning, predictive modelling, and time series analysis, with a focus on building reliable models for complex, large-scale multivariate datasets. I recently completed my PhD in Computer Science, where my research combined statistical modelling with state-of-the-art deep learning architectures, including Transformers, and focused on optimising models to improve forecasting performance on diverse real-world data.
My technical toolkit includes Python and SQL, along with Pandas, NumPy, PyTorch, PyTorch Geometric, SciPy, scikit-learn, and Statsmodels, which I use to develop scalable, end-to-end analytical solutions. Over five years of research, I have contributed to peer-reviewed publications, presented at international Big Data conferences, and mentored graduate students in advanced modelling and experimentation.
I also have a strong and active interest in Large Language Models, particularly in their application to time series forecasting and natural language processing, and I am keen to explore how these models can be leveraged for more adaptive, data-driven decision systems.
Employment
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University of Leeds Research Software Engineer2026 - Present
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University of Georgia Graduate Research Assistant2021 - 2025
Education
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University of Georgia Ph.D. in Computer Science2021 - 2025
Projects & Funding
Projects & funding information is unavailable.
Publications (5)
- How Effective are Time Series Models for Pandemic Forecasting? Save
- Statistical Transformations and Normalization Strategies in Transformer Models for Time Series Forecasting Save
- A Survey of Deep Learning and Foundation Models for Time Series Forecasting Save
- Knowledge Enhanced Deep Learning: Application to Pandemic Prediction Save
- Exploring the Predictive Power of Correlation and Mutual Information in Attention Temporal Graph Convolutional Network for COVID-19 Forecasting Save