Dr Vahid Seydi

Cymrawd Ymchwil mewn Gwyddor Data

v.seydi@bangor.ac.uk

Dr Vahid Seydi
  • email: v.seydi@bangor.ac.uk
  • web: ,听,听

Vahid Seydi is a Research Fellow in the School of Ocean Science at 亚洲色吧 in Data Science (DS) and Machine Learning (ML). Prior to 亚洲色吧, 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 鈥� 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鈥檚 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 亚洲色吧'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.

  • PhD: Artificial Intelligence- (thesis: Job鈥檚 interaction theory to train hyper-parameters of the cultural optimization algorithm.)
    2014
  • MSc: Artificial Intelligence- (thesis: multi-objective optimization to train neural networks and neuro-fuzzy systems.)
    2007
  • BSc: Computer Software Eng. - (Concentrations: RUP methodology, database management, SQL, object-oriented programming, designing algorithm, data structure, Java, Visual C++.)
    2005

Supervision

I have supervised more than 50 undergraduate students in the field of software engineering and software development since 2010. I supervised 21 ML projects for post-graduate students at the master鈥檚 level and supervised 4 and co-supervised 2 PhD students since 2014.听

PhD thesis:

Supervisor :

  • Mahta Hassanpour (2023), Adversarial Domain Adaptation.
  • Hossein Hajibabaie (2023) Community Detection in Social Networks Using Probabilistic Graphical Models.
  • Mahid Saadati (2021) Digital Image Watermarking in the field of Shearlet Transform based on SVD and meta-heuristic Algorithms.
  • Yeganeh Madadi (2020) Multi-Source Domain Adaptation via Low-Rank and Sparse Representation.

Co-Supervisor:

  • Serveh Lotfi (2020) Rumor detection in Twitter social network based on analysis of conversation propagation graph
  • Pejman Gholamnejad (2020) Multi-Objective Optimization Evolutionary Algorithm using clustering estimation of distributions

Master thesis:

  • Ali Aminzadeh Gohari (2022), The Application of Generative Adversarial Network in Text Anomaly Detection.
  • Mina Ameripour (2022), Sentiment Analysis with Graph Convolutional Networks using Directed PMI.
  • Mona Solgi (2021), Graph CNN to analyse online shopping.
  • Ehsan Nasiri (2021), The Application of Generative Adversarial Network in Recommendation System Design.
  • Nima Mashhadizadeh (2021), Breast Cancer Histology Image Classification Using Deep Neural Networks.
  • Elnaz Baktash (2020), Designing a Deep Autoencoder for Machine Translation based on Statistical Machine Translation and Attention Mechanism.
  • Mehrdad Hosseini Naveh (2020), Improving Model-based Collaborative Filter Recommender system Usin User-Empeding, Item-Embedding and Deep Learning.
  • Saman Jamalabbasi (2020),听Sentence simplification with deep reinforcement learning.
  • Sanaz Abaszadeh (2020), The Application of CNN in Sentiment Analysis.
  • Mehrdad Jannesar (2020), Text generation using LSTM Networks based on additive framework.
  • Hamideh Shooshtari (2020), Object detection using Deep YOLO algorithm based on additive framework.
  • Ali Mollaahmadi (2019), Adversarial image caption generator network
  • Arezoo Mirmahdi (2019), Community Detection in weighted Networks using BIGCLAM Algorithm.
  • Hassan Golshani (2019), Modeling Brain Activations using Hierarchical Latent Factors.
  • Ali Salmi (2019), Designing a Recommender System for Free Education Resources based on Users Activity in Social Networks.
  • Masoumed Nafari (2018), Using precision of users reviews to improve the performance of matrix factorisation method in recommender systems.
  • Elham Rajabian (2018), The Application of Deep Learning in Traffic flow prediction with big data challenge.
  • Hossein Raoof (2018), Heart Disorders Classification by heart sound signals using 听Hidden Makoff model and Decision Tree.
  • Parvin Aghazadeh (2018), Trustworthy Recommender Systems based on the prevention of fake identities influence.
  • Aydin Abedinia (2018), Optimization of XGboost algorithm for semi-supervised learning.
  • Ehsan Hosseini (2018), The Application of Reinforcement Learning in Designing non-player Adaptive Agents in Computer Games.
  • Ehsan Hojatolahi (2018), Prunin Data to Increase the Performance of Support Vector Machine
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Teaching

I was teaching a range of undergraduate and postgraduate modules in computer science and AI such as:

(The topics I covered are provided in the link to that module. However, I updated the modules鈥� content regularly and adopted best practices from leading universities worldwide to deliver the material effectively.)

PhD and Master's degree: 2014-2020

Bachelor's degree: 2010-2020

  • Foundation of Programming(C / Python)
  • formal language and automata theory

I have done most of my teaching in the Department of CS-AI at Azad University, South Tehran Branch, with which more than 41,000 students, is currently the second-largest university in Iran, and the largest in the country and middle-east in terms of technical and engineering fields, with around 10,085 students in the Faculty of Engineering.

My research interest topics which I have been actively involved in researching for at least the past three years include ML-Based Predictive Digital Twin using Probabilistic Graphical Models, Domain Adaptation, and few-shot learning, One-class classification model based on deep generative models, Reinforcement learning, Robust recommendation system and Social Network Analysis. My fascination lies in the mathematical concepts underlying these models, and I enjoy the challenge of formulating problems in their format while exploring innovative ways to make them scalable in big data environments.听 Based on my experience in oceanography, the broad range of challenges that machine learning can address can be categorized into white-box data-driven modelling, where we have physical equations for modelling, but we need observation to calibrate and improve the model; Gray-box modelling, where we have incomplete physical equations, but we know some knowledge such as how phenomena affect each other; and black-box modelling, where modelling based on learning from data play a central role. In the field of black box modelling, one of the current challenges is the lack of transparency in powerful existing models. To address this issue, researchers are pursuing two distinct paths. The first involves the to evolve those machine learning models that are inherently explainable. The second path involves the utilization of methods that analyse and provide explanations for non-transparent models. I am particularly interested in exploring feature representation methods that shed light on the behaviour of these non-transparent models.

I am interested in collaborating as a supervisor/co-supervisor for PhD and MSc students, especially in the field of oceanography and marine renewable energy. My ongoing research focuses on these areas and includes interdisciplinary dissertations where data-driven modelling plays an important role. I believe that with my expertise and knowledge, I can make valuable contributions in this capacity. On the other hand, since learning from data has a strong mathematical background, the development of machine learning models itself is another field of interest to me. If you want to cooperate, especially in the following areas, please email me.

  • ML-Based Digital twin
  • Passive acoustic monitoring
  • Classification, Object detection, Object tracking (deep learning approaches)
  • ML topics like Generative models, domain adaptation, RL, social network analysis, recommender systems

2025

  • E-gyhoeddi cyn argraffu
    Hassan Pour Zonoozi, M., Seydi, V. & Deypir, M., 26 Maw 2025, (E-gyhoeddi cyn argraffu) Yn: Machine Learning. 114, 5, 128.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid

2024

  • Cyhoeddwyd
    Hajibabaei, H., Seydi, V. & Koochari, A., 16 Maw 2024, Yn: journal of Intelligent Information Systems. 12, 1
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Abedinia, A. & Seydi, V., 1 Hyd 2024, Yn: International Journal of Machine Learning and Cybernetics. 15, 10, t. 4493-4510 18 t.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid

2023

  • Cyhoeddwyd
    HassanPour Zonoozi, M. & Seydi, V., 1 Meh 2023, Yn: Neural Processing Letters.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Hajibabaei, H., Seydi, V. & Koochari, A., 2 Chwef 2023, Yn: journal of Intelligent Information Systems. 60, 1
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Rubbens, P., Brodie, S., Cordier, T., Destro Barcellos, D., Devos, P., A Fernandes-Salvador, J., I Fincham, J., Gomes, A., Olav Handegard, N., Howell, K. L., Jamet, C., Heldal Kartveit, K., Moustahfid, H., Parcerisas, C., Politikos, D., Sauz猫de, R., Sokolova, M., Uusitalo, L., Van den Bulcke, L. & TM van Helmond, A. & 18 eraill, T Watson, J., Welch, H., Beltran-Perez, O., Chaffron, S., S Greenberg, D., K眉hn, B., Kiko, R., Lo, M., M Lopes, R., Ove M枚ller, K., Michaels, W., Pala, A., Romagnan, J.-B., Schuchert, P., Seydi, V., Villasante, S., Malde, K. & Irisson, J.-O., 1 Medi 2023, Yn: ICES Journal of Marine Science. 80, 7
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid

2022

  • Cyhoeddwyd
    Gholamnezhad, P., Broumandnia, A. & Seydi, V., Medi 2022, Yn: Progress in Artificial Intelligence.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Falahiazar, A., Sharifi, A. & Seydi, V., 1 Awst 2022, Yn: Journal of Combinatorial Optimization. t. 794-849
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Gholamnezhad, P., Broumandnia, A. & Seydi, V., Meh 2022, Yn: Computational Intelligence. 38, 3, t. 1018-1056
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Gholamnezhad, P., Broumandnia, A. & Seydi, V., 31 Hyd 2022, Yn: Electronics and Telecommunications Research Institute. 44, 5, t. 709-874
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Muhammad Aqdus Ilyas, C., Rehm, M., Nasrollahi, K., Madadi, Y., B. Moeslund, T. & Seydi, V., Awst 2022, Yn: Pattern Analysis and Applications. 25, 3, t. 653-677 25 t., 653-677.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Solgi, M. & Seydi, V., 3 Rhag 2022, Yn: Progress in Artificial Intelligence. t. 411-423
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Madadi, Y., Seydi, V. & Hosseini, R., Gorff 2022, Yn: International Journal of Computers and Applications. 44, 7, t. 670-677 9 t.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid

2021

  • Cyhoeddwyd
    Mollaahmadi Dehaqi, A., Seydi, V. & Madadi, Y., Mai 2021, Yn: SN Computer Science. 2, 3, 14 t., 182.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Gholamnezhad, P., Broumandnia, A. & Seydi, V., 9 Awst 2021, Yn: Concurrency and Computation: Practice and Experience.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Lotfi, S., Mirzarezaee, M., Hosseinzadeh, M. & Seydi, V., 1 Gorff 2021, Yn: Applied Intelligence. 51, 7, t. 4774鈥�4787 14 t.
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Lotfi, S., Mirzarezaee, M., Hosseinzadeh, M. & Seydi, V., Rhag 2021, Yn: Information Technology and Management . 22, 4, t. 265鈥�279
    Allbwn ymchwil: Cyfraniad at gyfnodolyn 鈥� Erthygl 鈥� adolygiad gan gymheiriaid
  • Cyhoeddwyd
    Falahiazar, L., Seydi, V. & Mirzarezaee, M., 2021, Yn: Journal of AI and Data Mining. 9, 3, t. 369-381
    Allbwn ymchwil