Dr Ali Sadegh Zadeh


Digital, Tech, Innovation & Business

Dr. Sadegh-Zadeh is an Assistant Professor in Artificial Intelligence at the Staffordshire University, UK, where he is dedicated to teaching and researching various aspects of AI. His primary focus lies in advancing our understanding of brain functioning and dynamics, particularly in the context of memory impairment and dementia, through computational and mathematical modelling.

As a member of the Artificial Intelligence (AI) and Robotics Research Group at the Staffordshire University, He actively contributes to the cutting-edge research in the field. His interdisciplinary approach combines insights from artificial intelligence, computational neuroscience, and computational psychiatry to shed light on the complex workings of the brain.

Dr. Sadegh-Zadeh obtained his Ph.D. in Artificial Intelligence and Computational Neuroscience from the University of Hull, where he acquired a comprehensive understanding of the fundamental principles underlying both AI and neuroscience. Building upon this foundation, he went on to earn a master's degree in Advanced Computing, specializing in Machine Learning, Data Mining, and High-Performance Computing from the University of Bristol.

Recognizing the importance of mentorship and fostering future talent, Dr. Sadegh-Zadeh actively welcomes aspiring researchers to join his team. He is currently accepting Ph.D. students interested in pursuing studies in the areas of Artificial Intelligence, Machine Learning, Data Mining, Computational Neuroscience, Computational Psychiatry, and any field with applications in AI and ML. By collaborating with Dr. Sadegh-Zadeh, students have the opportunity to engage in groundbreaking research projects and make significant contributions to the field.

His passion for bridging the gap between AI and neuroscience fuels his commitment to advancing our understanding of the brain's complexities. Through his research, teaching, and mentorship, he strives to make lasting contributions to the fields of artificial intelligence, computational neuroscience, and their real-world applications.

Academic qualifications

  • Ph.D., Artificial Intelligence and Computational Neuroscience

  • MSc, Advanced Computing-Machine Learning, Data Mining, and High-Performance Computing

  • BEng, Computer Engineering

  • PGCHE – Postgraduate Certificate in Higher Education


He is an expert in the fields of artificial intelligence, machine learning, data mining, and computational neuroscience. His expertise lies in applying cutting-edge machine learning techniques to advance the field of neuroscience and improve the early detection of neurodegenerative diseases. One of Dr. Sadegh-Zadeh's primary areas of expertise is the early detection of neurodegenerative diseases. By developing advanced algorithms and models, he aims to enhance our understanding of neurodegenerative diseases and enable early intervention and treatment strategies. In addition to his expertise in machine learning and neuroscience, Dr. Sadegh-Zadeh has a broad interest in various fields that employ artificial intelligence and machine learning applications. He actively explores areas such as data mining, computational psychiatry, and other disciplines that leverage AI and ML to gain insights and make informed decisions.

His research and expertise contribute significantly to the development of advanced AI and ML applications in a range of fields, ultimately aiming to improve human health and well-being.

Areas of Expertise:

  • Artificial Intelligence
  • Machine Learning
  • Data Mining
  • Computational Neuroscience
  • Computational Psychiatry
  • Neurodegenerative Disease Detection
  • Early Intervention Strategies
  • Biomarker Identification
  • Predictive Modelling

Research interests

Dr. Sadegh-Zadeh conducts theoretical and applied research in various areas of AI, with a particular focus on the following:

  • Machine Learning and Data Mining: Driven by a passion for uncovering patterns and extracting knowledge from vast amounts of data, he explores cutting-edge techniques in machine learning and data mining to develop intelligent algorithms and models.
  • Pattern Recognition: Understanding and interpreting patterns in complex datasets is a crucial aspect of his research. He investigates innovative approaches to pattern recognition, enabling applications in diverse fields such as image processing, speech recognition, and bioinformatics.
  • Computational Neuroscience: Inspired by the intricacies of the human brain, he delves into the realm of computational neuroscience. By studying the brain's computational principles, he aims to develop computational models that simulate cognitive processes and advance our understanding of neural mechanisms.
  • Natural Intelligent Systems: He explores the fascinating realm of natural intelligent systems, drawing inspiration from biological systems and animal behavior. His research seeks to unravel the underlying principles and adapt them to the development of intelligent algorithms and systems.
  • Manifold Learning: Investigating high-dimensional data representations, he explores manifold learning techniques. By uncovering the intrinsic low-dimensional structure of complex datasets, he aims to enhance data visualization, dimensionality reduction, and clustering algorithms.
  • Fraud Detection: Addressing the challenges posed by fraud in various domains, Dr. Sadegh-Zadeh is dedicated to developing robust fraud detection methodologies. By integrating machine learning and data analytics, he aims to create effective solutions for detecting and preventing fraudulent activities.
  • IoT and Smart Cities: He is actively involved in research pertaining to the Internet of Things (IoT) and its applications in smart cities. His work explores intelligent systems that leverage IoT technologies to enhance urban infrastructure, optimize resource allocation, and improve quality of life for citizens.
  • AI for Energy Efficiency: Recognizing the pressing need for sustainable energy solutions, he investigates the role of AI in promoting energy efficiency. His research aims to develop intelligent algorithms and control systems that optimize energy consumption, facilitate renewable energy integration, and mitigate environmental impact.
  • AI-Driven Decision Making: He is deeply interested in the development of AI-driven decision-making frameworks. By combining machine learning, optimization techniques, and decision theory, he aims to create intelligent systems that assist in complex decision-making processes across various domains.


Through his research endeavours, Dr. Sadegh-Zadeh strives to advance the frontiers of AI, paving the way for transformative technologies and applications that positively impact society and drive innovation in diverse fields.

Current Projects:

  • Early detection of Alzheimer’s Disease using machine learning techniques
  • Manifold learning for dimensionality reduction
  • Differentiation between bipolar and borderline personality disorder using brain rhythms and artificial intelligence algorithms
  • Development of a geometric index for datasets in order to select the optimal dimension reduction algorithm


  • Artificial Intelligence and Chatbots
  • Advanced Topics in Cybersecurity
  • Business Analytics
  • E-Business
  • Operating Systems
  • Computer Architecture
  • Decision Management


  • Sadegh-Zadeh, Seyed-Ali, et al. "An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals." diagnostics 13.3 (2023): 477.
  • Sadegh-Zadeh, Seyed-Ali, et al. "Machine Learning Modelling for Compressive Strength Prediction of Superplasticizer-Based Concrete." Infrastructures 8.2 (2023): 21.
  • Sadegh-Zadeh, Seyed-Ali, et al. "Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning." Dentistry Journal 10.9 (2022): 164.
  • Sadegh-Zadeh, Seyed-Ali, et al. "Evaluation of COVID-19 pandemic on components of social and mental health using machine learning, analysing United States data in 2020." Frontiers in Psychiatry13 (2022).
  • Zarringhalam, Amir, et al. "CUDA and OpenMp Implementation of Boolean Matrix Product with Applications in Visual SLAM." Algorithms 16.2 (2023): 74.
  • A. Sadegh-Zadeh, C. Kambhampati, D.N. Davis, “Ionic imbalances and coupling in synchronisation of responses in neurons”, MDPI Journal, J-Multidisciplinary Scientific Journal, Volume 2, Issue 1, 2019, DOI 10.3390/j2010003
  • A. Sadegh-Zadeh, C. Kambhampati, “Computational investigation of amyloid peptide channels in Alzheimer’s disease”, MDPI Journal, J-Multidisciplinary Scientific Journal, Volume 2, Issue 1, 2019, DOI 10.3390/j2010001
  • A. Sadegh-Zadeh, C. Kambhampati, “A Computational Investigation of the Role of Ion Gradients in Signal Generation in Neurons”, Conference: COMPUTING CONFERENCE 2018, London, UK.
  • A. Sadegh-Zadeh, C. Kambhampati, “All-or-None Principle and Weakness of Hodgkin-Huxley Mathematical Model”, International Journal of Mathematical and Computational Sciences, Vol: 11, No: 11, 2017.
  • A. Sadegh-Zadeh, C. Kambhampati, “Analysing the impact of sodium channels in Alzheimer’s disease using a computational model”, 26th Annual Computational Neuroscience Meeting (CNS*2017), Antwerp, Belgium. 15–20 July 2017.

External profiles

in the UK for Quality Education

Sustainable Development Goal 4, Times Higher Education Impact Rankings 2023

for Career Prospects

Whatuni Student Choice Awards 2023

for Facilities

Whatuni Student Choice Awards 2023

for Social Inclusion

The Times and The Sunday Times Good University Guide 2023

of Research Impact is ‘Outstanding’ or ‘Very Considerable’

Research Excellence Framework 2021

of Research is “Internationally Excellent” or “World Leading”

Research Excellence Framework 2021

Four Star Rating

QS Star Ratings 2021