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:

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)

Attention-Based Fuzzy Neural Networks for Self-Supervised Data Annotation (Jan 2026)
Md Rakibul Islam , Shahina Begum, Mobyen Uddin Ahmed, Shaibal Barua
Intelligent Systems with Applications (ISWA)

Explainable Artificial Intelligence in Air TrafficControl: Effects of Expertise on Workload,Acceptance, and Usage Intentions (Jan 2026)
Giulia Cartocci , Alexandre Veyrie , Nicola Cavagnetto , Christophe Hurter , Augustin Degas , Ana Ferreira , Mobyen Uddin Ahmed, Shahina Begum, Shaibal Barua, Gianluca Di Flumeri , Fabio Babiloni , Pietro Aricò
Brain Informatics (BI)

Enhancing Trust in AI for Remote Digital Towers: From Gap Analysis to Integration (Jan 2026)
Sara Kebir , Alexandre Veyrie , Nicola Cavagnetto , Mobyen Uddin Ahmed, Shaibal Barua, Mir Riyanul Islam, Pietro Aricò , Christophe Hurter
SESAR Innovation Days (SIDs) (SID2025)

A Multimodal Approach for Enhancing Decision Support in Remote Digital Tower (Oct 2025)
Mobyen Uddin Ahmed, Shaibal Barua, Mir Riyanul Islam, Ricky Stanley D Cruze , Shahina Begum, Sara Kebir , Alexandre Veyrie , Christophe Hurter
2025 10th International Conference on Machine Learning Technologies (ICMLT 2025)

Integration of Explainable Artificial Intelligence and Multimodal Machine Learning for Drivers’ Fitness (Oct 2025)
Mobyen Uddin Ahmed, Arnab Barua, Mir Riyanul Islam, Shaibal Barua, Shahina Begum
International conference on Advanced Machine Learning and Data Science (AMLDS 25)

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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