Dr Vahid Seydi

• Qualifications: PhD in Artificial Intelligence (2014) Thesis: "Job's Interaction Theory to Train Hyperparameters of the Cultural Optimisation Algorithm" MSc in Artificial Intelligence (2007) Thesis: "Multi-objective Optimisation to Train Neural Networks and Neuro-fuzzy Systems" BSc in Computer Software Engineering (2005) Core competencies: RUP methodology, database management, SQL, object-oriented programming, algorithm design, data structures, Java, Visual C++
• Position:Lecturer in Data Science
• Department:FET - Computer Science and Creative Technologies
• Telephone:+441179656261

About me

I am currently a Lecturer in Data Science at the University of the West of England (UWE Bristol), a role I began in June 2025.

Previously, I worked as a Research Fellow in Data Science and Machine Learning at the School of Ocean Sciences, Bangor University (2020–2025). During this time, I also served as a module leader for Data Science courses in the School of Computer Science and Electronic Engineering.

Before Bangor, I was an Assistant Professor in the Department of Artificial Intelligence at Azad University, South Tehran Branch (2014–2020), following several years as a lecturer (2010–2014).

I hold a B.Sc. (2005) in Software Engineering, and both an M.Sc. (2007) and Ph.D. (2014) in Artificial Intelligence from the Science and Research University, Tehran, Iran.

My academic and research work has been supported by several awards and fellowships, including:

• Global Talent Endorsement by the UK Royal Society (2023)
• Research Fellowship at Bangor University (2020–2025)
• Scholarship for attending the School of AI in Rome (2019)
• Full PhD Scholarship from Azad University (2010–2014)
• KNTU ISLAB Research Fellowship (2007–2010)

Throughout my studies, I maintained strong academic performance and developed a research focus in Data Science and Machine Learning. I have around 15 years of experience in these fields, with interests spanning AI applications, machine learning methods, and data-driven research.

Area of expertise

With about 20 years of comprehensive experience in Data Science and Machine Learning, I specialise in a broad spectrum of methodologies including regression, classification, information retrieval, clustering, reinforcement learning, probabilistic graphical models, Gaussian processes, recommender systems, social network analysis, association rule mining, and optimisation techniques. My experience encompasses working with diverse data modalities, including tabular data, text, images, video, and acoustic signals.

Key specialisations:

• Deep Learning, Domain Adaptation, and Generative Models
• Explainable Machine Learning
• Reinforcement Learning
• Optimization Methods

Publications

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