Shaibal Barua is a Senior Lecturer at Mälardalen University in Computer Science and working with the Artificial Intelligence and Intelligent Systems group. He received his Ph.D. in computer science in 2019 from Mälardalen University. In 2013 he received his MSc in Computer Science and in 2015 he received licentiate degree from Mälardalen University.

Research Interest:

Artificial Intelligence & Machine Learning
Explainable AI
Statistical Learning Theory
Data Analytics
Signal Processing and Multi-sensor Data Fusion

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[Google Scholar author page]

Latest publications:

Enhancing Industrial AI Usability Through Human-AI Interaction (Jul 2026)
Marcus Hammarström , Liam Burberry Gahm , Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Emmanuel Weiten , Daniel Aurel
28th International Conference on Computer and Information Technology (ICCIT25)

An End-to-End Explainable Fault Prediction Pipeline for Embedded Test Systems (Jul 2026)
Md Motaher Hossain Bhuiyan, Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum
28th International Conference on Computer and Information Technology (ICCIT25)

Bias-Aware Generative XAI for Sustainable Air Traffic Control: A Methodological Framework with Predictive Telemetry (Jun 2026)
Mobyen Uddin Ahmed, Christophe Hurter , Shaibal Barua, Shahina Begum, Pietro Aricò , Nicola Cavagnetto
International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2026)

Explainable Quantum Machine Learning Concepts for Trajectory Optimization in Air Traffic Management (May 2026)
Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed, Henri de Boutray , Christophe Hurter
International Conference on Modern Artificial Intelligence and Data Science Systems (MAIDSS26)

Quantum Machine Learning for Optimisation: A Domain Focused Survey (May 2026)
Surya Teja Darbhamalla, Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed
International Conference on Modern Artificial Intelligence and Data Science Systems (MAIDSS26)

Challenges and Future Directions for AI-based Systems for Remote Digital Towers (May 2026)
Mir Riyanul Islam, Alexandre Veyrie , Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum, Christophe Hurter , Pietro Aricò
International Conference on Modern Artificial Intelligence and Data Science Systems (MAIDSS26)

Project Title	Status
ARTIMATION :TRANSPARENT ARTIFICIAL INTELLIGENCE AND AUTOMATION TO AIR TRAFFIC MANAGEMENT SYSTEMS	finished
AUTOMAD:AUTOnomous Decision Making in Industry 4.0 using MAchine Learning and Data Analytics	finished
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road	finished
DIGICOGS:DIGital Twins for Industrial COGnitive Systems through Industry 4.0 and Artificial Intelligence	finished
Embedded Sensor Systems for Health Plus	finished
ESS-H - Embedded Sensor Systems for Health Research Profile	finished
FitDrive: Monitoring devices for overall FITness of DRIVErs	active
Food4Health: A Personalized System for Adaptive Mealtime Situations for Elderly	finished
HIVEMIND:Human-centred collaboratIVE MultI-ageNt framework for accelerating software Development and maintenance	active
MONITOR: A Data-driven Intelligent MONITORring System to Improve Quality of Working Life	active
PREST:Predictive Strategy using Machine Learning for Smart Test Case Selection	active
PROMPT - Professional Master’s in Software Engineering (step II, phase B&C)	finished
SafeDriver: A Real Time Driver's State Monitoring and Prediction System	finished
SimuSafe : Simulator of Behavioural Aspects for Safer Transport	finished
TRUSTY: TRUSTWORTHY INTELLIGENT SYSTEM FOR REMOTE DIGITAL TOWER	active
Trust_Gen_Z:Trustworthy Generative AI for Advanced Industrial DigitaliZation	active
VDM - Vehicle Driver Monitoring	finished
Xbest: Generative AI towards Inference to the Best Explanation	active

PhD students supervised as assistant supervisor:

Md Mohsin Kabir
Tahira Salwa Rabbi Nishat

MSc theses supervised (or examined):

Thesis Title	Status
	available
A systematic review on theoretical aspects of k-nearest neighbour algorithm	available
An intelligent system for driver cognitive load detection using eye tracking data	available
Data-driven cognitive load classification system using machine-learning algorithm	available
Distributed case retrieval for big data using Spark platform and Case-Based Reasoning	selected
Applying artificial intelligence to identify drivers cognitive load based on correlation between EEG signals and driving behaviour signals	in progress
Machine Learning applied on embedded system recordings	in progress
Case representation methodology for a scalable Case-Based Reasoning	finished
Feature selection of EEG-signal data for cognitive load	finished
Stress Management for Pilots	finished

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