Contact info

• email: v.seydi@bangor.ac.uk
• web: Homepage, Google Scholar, ResearchGate

Vahid Seydi is a Research Fellow in the School of Ocean Science at Bangor University in Data Science (DS) and Machine Learning (ML). Prior to Bangor, Vahid was an Assistant Professor at the Department of AI at Azad University South Tehran Branch (Feb 2014 - Sep 2020) and was an award-winning lecturer (Oct 2010 – Feb 2014). He received a B.Sc.(2005) in software engineering, M.Sc. (2007) and PhD(2014) in AI, from the Department of Computer Science at Science and Research University, Tehran Iran. He has been awarded Global Talen endorsement from the UK Royal Society (2023); his current research fellowship(2020); a merit-based scholarship for attending the school of AI, Rome, Italy(2019); a full scholarship Award from Azad University(2010-2014); and KNTU ISLAB Research Fellowship (2007-2010). Throughout his studies, he consistently achieved grades above 18 out of 20 in nearly all modules, and I often secured the first-ranked student. furthermore, in Zillow’s home value prediction Kaggle competition, he has been in the top 2% among 3779 teams of data scientists (2017).

He possesses 15 years of extensive experience in diverse areas of Data Science (DS) and Machine Learning (ML). His expertise spans across a wide range of topics including regression, classification, retrieval, clustering, reinforcement learning, probabilistic graphical models, Gaussian process, recommender systems, social network analysis, association rule mining, and optimization methods. Throughout his career, he has worked with various models and data types, such as tabular data, text, image, video, and acoustic signals.

He believes that it is our responsibility to strive towards creating a better world for future generations. The issue of global warming stands as one of the foremost challenges facing humanity today where we can significantly mitigate its effects by implementing renewable energy sources. Machine Learning methods have the potential to address many of the challenges associated with data collected in the field of offshore renewable energy. In alignment with Bangor University's vision, which aims to foster a "sustainable world for future generations", he currently contributes his expertise in AI and ML to the sector of marine renewable energy.

Research Interests:

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

I am available for consultation on data-driven issues, proposals, and projects. If you require expertise in ML and DS or need assistance with data-driven initiatives, I would be delighted to provide my insights and support. Please feel free to reach out to me for any collaboration opportunities or inquiries.