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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Latest publications:

A Multimodal Approach for Enhancing Decision Support in Remote Digital Tower (Sep 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)

ENHANCING EXPLAINABILITY, ROBUSTNESS, AND AUTONOMY: A COMPREHENSIVE APPROACH IN TRUSTWORTHY AI (May 2025)
Mobyen Uddin Ahmed, Shahina Begum, Shaibal Barua, Abu Naser Masud, Gianluca Di Flumeri , Nicolò Navarin
IEEE Symposium on Explainable, Responsible, and Trustworthy CI (IEEE CITREx)

In-Depth Analysis of Diverse Driver Behaviors using Hybrid Multimodal Machine Learning (Mar 2025)
Mera Abudiab , Francisco Javier Pérez Núñez , Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Arnab Barua
International Conference on Computer and Information Technology (ICCIT)

Role of Multi-modal Machine Learning, Explainable AI and Human-AI Teaming in Trusted Intelligent Systems for Remote Digital Towers (Jan 2025)
Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Waleed Reafee Sbu Jmoona, Ricky Stanley D Cruze , Alexandre Veyrie , Christophe Hurter
7th Artificial Intelligence and Cloud Computing Conference (AICCC2024)

Trust_Gen_Z: Trustworthy Generative AI for Advanced Industrial DigitaliZation (Oct 2024)
Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed
7th Artificial Intelligence and Cloud Computing Conference (AICCC2024)

Research Issues and Challenges in the Computational Development of Trustworthy AI (Aug 2024)
Shahina Begum, Mobyen Uddin Ahmed, Shaibal Barua, Md Alamgir Kabir
IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET 2024)

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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