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Research staff:
- Renewable Energy
- Applied Machine Learning
- Statistical Physics
- Nonequilibrium Dynamics
- Mathematical &
- Computational Modelling in Finance and Engineering
- Stochastic Modelling
- Complex Systems
- Climate Data Analysis
- Data Science
- Artificial Intelligence (AI)
- Complex Networks
Teaching staff:
- Position:Senior Lecturer in Data Science
- Department:FET - Computer Science and Creative Technologies
- Telephone:+441173283289
- Email:Ignacio.Deza@uwe.ac.uk
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About me
I am a Senior Lecturer in Data Science and Co-Programme Leader of the BSc in Data Science at the University of the West of England (UWE), Bristol. My research lies at the intersection of Statistical Physics, Data Science, and AI, with a focus on developing methods to uncover structure in complex, noisy, and high-dimensional datasets.
I earned my PhD in Statistical Physics from the Universitat Politècnica de Catalunya (Spain) as part of a European Horizon 2020 fellowship, following earlier studies in Argentina and Spain. I have since worked across several European research environments, including in Italy, contributing to projects that bridged fundamental science and applied data analysis.
My publications span applications in climate dynamics, renewable energy forecasting, subscription markets, aerospace, and complex networks, united by a commitment to designing bespoke statistical and machine learning methods for problems that resist conventional approaches. I also developed qNoise, an open-source software package for generating non-Gaussian coloured noise, now used internationally to support research in stochastic modelling and signal analysis.
At UWE, I lead the module Applied Statistics and Fundamentals of Machine Learning and teach across areas including predictive analytics, big data, and AI. As Co-Programme Leader for the BSc in Data Science, I contribute to the design and delivery of a curriculum that combines rigorous foundations with hands-on industry relevance. I am passionate about teaching and mentoring; and many of my projects are conducted in collaboration with industry partners and government organisations.
I bring together expertise from physics, rigorous advanced statistics, data science, and AI to tackle problems that demand genuinely interdisciplinary solutions. My goal is to combine theoretical innovation with practical impact, contributing to advances that benefit both research and society.
I welcome collaborations with colleagues in academia, industry, and government.
Area of expertise
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Data Science & AI: Development of statistical and machine learning methods for complex, high-dimensional, and noisy datasets.
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Statistical Physics & Complex Systems: Applying theoretical frameworks to real-world data challenges, from climate dynamics to neural networks.
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Climate Data Analysis & Renewable Energy: Modelling variability and forecasting in large-scale environmental systems.
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Applied Machine Learning: Experience across domains including subscription markets, aerospace, and natural language processing.
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Teaching & Leadership: Co-Programme Leader of the BSc in Data Science; module leader in Applied Statistics and Fundamentals of Machine Learning; strong record in mentoring and curriculum design.
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Collaboration & Knowledge Exchange: Proven track record of working with industry, government, and academic partners to translate research into impact.
Publications
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