Dr Mohammad Dehshibi

Qualifications:

PhD in Systems Software Engineering / AI, 2017 MSc in Software Engineering / AI, 2010 BSc in Software Engineering, 2007

Position:

Visiting Researcher

Department:

FET - Computer Science and Creative Technologies

Telephone:

+441179656261

Social media:

About me

I am a Senior AI scientist, IEEE Senior Member, and member of the European Laboratory for Learning and Intelligent Systems (ELLIS), recognised as an established researcher in artificial intelligence, affective computing, and unconventional computing. My work focuses on developing robust, explainable, and human-centred AI systems, particularly in medical and biological signal analysis. I collaborate extensively with interdisciplinary teams across computer science, engineering, architecture, and the life sciences.

Teaching topics:

I am interested in teaching machine learning, deep learning, computer vision, natural language processing, and applied artificial intelligence. I also supervise PhD and Master’s projects in affective computing, medical data analysis, and explainable AI, supporting students in addressing real‑world, data‑driven problems and engaging in interdisciplinary collaborations.

Research topics:

(1) Deep learning and observational AI
(2) Affective computing and emotion-aware intelligent systems
(3) AI in medicine and healthcare technologies
(4) Unconventional computing and bio-inspired computation
(5) Explainable AI (XAI) and trustworthy AI
(6) Computer vision

Collaborations:

I actively collaborate with international partners across academia, industry, and healthcare. My collaborative projects include work on unconventional computing, AI for medicine, and human-computer interaction. These collaborations involve universities, research institutes, hospitals, and interdisciplinary laboratories across Europe and beyond, including participation in EU-funded projects and research networks such as FUNGAR, BODYinTRANSIT, and COgITOR.

My publications and citation metrics are available on Google Scholar, with additional profile information accessible via my ORCID and ResearcherID profiles.

Area of expertise

My expertise spans deep learning, machine learning, computer vision, and the analysis of complex medical and biological data. I have a particular interest in affective computing, observational AI, and explainable AI (XAI), with applications in healthcare, human-computer interaction (HCI), and adaptive intelligent systems. I also contribute to emerging research at the boundary of AI and unconventional computing, exploring novel substrates and architectures for computation.

Publications

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