Mobyen Uddin Ahmed, Professor


Mobyen Uddin Ahmed is a Professor in Artificial Intelligence/Computer Science at  Artificial Intelligence and Intelligent Systems group and a member of ESS-H - Embedded Sensor Systems for Health Research Profile. His current research in trustworthy AI with several ongoing project. Mobyen has 150+ scientific publication and more than 3080+ citations.

Mobyen on ResearchGate

Mobyen on IVA list twice: 2022 and 2023

He is involved in research and development since 2005 after completing his M.Sc. in Computer Engineering (Specialization in Intelligent Systems, thesis) from Dalarna University, Sweden. He received his PhD (thesis) in Artificial Intelligence/Computer Science in 2011 from Mälardalen University. He has completed his postdoctoral study between the years 2012 and 2014 in Computer Science and Engineering (Center for Applied Autonomous Sensor Systems) at School of Science and Technology, Örebro University, Sweden

Mobyen is the main-applicant and project leader for for MDU for the H2020 projects ‘SimuSafe’; Artimation; TRUSTY and FitDrive; Also, ‘BrainSafeDrive’, a bilateral project between Italy and Sweden funded by VR and Several national projects i.e., Digicogs, adapt2030, CPMXai and ‘InVIP’. He has been also involved in many other national and international projects, such as ecare@home, ESS-H, SafeDriver, PainOut, VDM, Prompt, FutureE, etc. He is one of the Principle Investigator of the research profile Embedded Sensor Systems for Health (ESS-H) at MDH.

 Ongoing conference, Advanced Artificial Intelligence & Robotics, ASPAI' 2020. Mobyen has been selected twice (i.e. HealthyIoT2016, HealthyIoT2017) to be the general chair of an international conference ‘International Conference on IoT Technologies for HealthCare’. He has organized several other international conferences namely ICCBR2018, pHealth2015, ESS-HIoT2015.

Mobyen is involved in teaching and is responsible for courses, Applied Machine Learning, Machine Learning Concepts, Applied Artificial Intelligence, Project in intelligent embedded systems, and Databases. He is also involved in the development and teaching for the course Machine Learning With Big Data (a distance course for industrial professionals), Deep learning for industrial imaging, Predictive Data Analytics, MooC course: Ground Knowledge on Machine Learning. Also, he is involved in the development of learning materials for AI and deep learning in the project Into DeeP.


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Journal Editors:

Sustainability Journal, IF:3.88, "Interpretable and Explainable AI Applications"

Sensors Journal, IF: 3.84, "Deep Learning in Biomedical Informatics and Healthcare"