Dr Md Mahmudul Hasan

Dr Md Mahmudul Hasan is an expert in building data-driven AI products, with 14+ years of experience. He has specialised in artificial intelligence, Agentic AI, machine learning, games, XR technologies and IT Governance.

As a Fellow of the Higher Education Academy (FHEA), UK, Mahmud focuses on data-driven solutions and the application of Agentic AI. He also contributes to the ISO 42001 AI management standards community and serves as a reviewer for EPSRC and Elsevier.

[email protected]

Connect with Dr Hasan on LinkedIn

Background

Dr Hasan has completed his PhD in Artificial Intelligence from Anglia Ruskin University, funded by the EU. He is a member of the Computing, Informatics and Applications Research Group.

He successfully led several Innovate UK funded projects. One of the projects was with the Lothian NHS for multimorbidity risk stratification and with the Ministry of Cambodia for fish disease detection. He has also accomplished a healthy ageing project for older people funded by UKRI Social Ventures, where older people can connect with their friends and family through television. He has contributed to the first-ever native app engine for cross-platform applications called "Bunon". He has published 30+ top-quality publications in conferences and journals. He also invented a benchmark for dynamic multi-objective optimisation for deep reinforcement learning.

He has specialised in artificial intelligence, agentic AI, machine learning, games, XR technologies and IT Governance. He is currently leading several projects for Net Zero and decarbonisation, including blockchain technologies to establish transparency in the supply chain. His research interests are in building data products, Robotic Process Automation (RPA) for healthcare sectors, Consumable AI, Smart Data Transformation, Deep Reinforcement Learning, Cognitive Behaviour, Neuroscience, Pervasive Computing and Ambient Intelligence (e.g., Human-Agent Teamwork), Mobile Apps and Games development for different platforms.

Research interests

• Building data products for healthcare and net zero
• Robotic Process Automation (RPA)
• Smart Data Transformation
• Deep Reinforcement Learning
• Pervasive Computing and Ambient Intelligence (e.g., Human-Agent Teamwork)
• Mobile Apps and Games development for different platforms

Areas of expertise

• Artificial Intelligence
• Data Science
• Agentic AI
• GenAIOps
• AIBOM
• Health Informatics
• Net zero solutions
• Responsible AI
• IT Governance

Areas of research supervision

• Building AI and Data products for healthcare and net-zero
• Data-driven Agentic AI solutions
• Health informatics
• Consumable and Trustworthy AI
• AI-enabled smart solutions and AI Audit

PhD supervision

• Sarath Mohan Poyilil: A conceptual model for optimizing the Hybrid Urban Systems to achieve net-zero emissions using Nature-based Solutions (Ongoing).

Teaching

• Applied Data Analysis and Research Methodology
• Software Principles

Qualifications

• PhD in AI from Anglia Ruskin University funded by the EU
• MSc in Computer Science from the University of Essex
• PGCert Learning & Teaching in Higher Education

Memberships, editorial boards

• IEEE Membership
• MCDM Membership
• Fellow of Higher Education Academy, UK (FHEA)
• MISO 42001 Lead Auditor (AIMS)
• EPSRC Peer Review College membership

Research grants, consultancy, knowledge exchange

• Principal Investigator of the SBRI: Improving Multimorbidity Acute Care Using Data Analytics, phase 1 with Lothian NHS; funded by Innovate UK
• Principal Investigator of Global cooperation feasibility studies for fish disease detection, funded by Innovate UK
• Principal Investigator of SBRI – Healthy Ageing Social Ventures feasibility studies for alleviating loneliness for older people funded by Innovate UK
• Principal Investigator of Mindset extended reality (XR) for digital mental health: strand 1 for embedding music therapy for healthy mental health funded by Innovate UK
• Co-investigator for CYBER ASAP Academic Accelerator Program (2022) funded by Innovate UK
• Principal Investigator, Hasan, Md., Chakraborty, A., Cirstea, S., Islam, S. (2023): QuantoSniff: A Next Generation Cyber Defence using Quantum-Safe Cryptography’ KTN, Innovate UK 12k
• Principal Investigator: AI360Degree (www.ai360degree.co.uk) (2024), Elevating FinTech Security with Advanced AI Protection and Automated Compliance
• QR funding for Sustainable Futures Research (Green Skills) and Knowledge Exchange and policy funds
• QR Funding (Chakraborty, A., Hasan, Md, van der Linde, Ian (2023) Pump Prime, A remotely operated AI decision support system for preterm neonates combining skin colour and ventilation data.
• AI Consultant: nanoRail: Non-Intrusive Geotechnical Stability Monitoring System Using Satellite Interferometry, funded by Innovate UK (2025), project by Global Centre of Rail Excellence (GCRE).
• Co-investigator: AI Integrated Framework for Intelligent Geospatial Handling and Robust Operation in MultiGIS Applications (AI4MultiGIS), Project by Chist-Era (2024), Funded by the EU.

Selected recent publications

Yordanov, D.; Chakraborty, A.; Hasan, M.M.; Cirstea, S. A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving. Sensors 2024, 24, 8099. https://doi.org/10.3390/s24248099

Hazzaa, F, Hasan MM, Qashou, A, & Yousef, S, Trans. (2024), A New Lightweight Cryptosystem for IoT in Smart City Environments. Mesopotamian Journal of CyberSecurity, 4(3), 46-58. https://doi.org/10.58496/MJCS/2024/015

Nanwani, R, Hasan, Md, Cirstea, S (2023), Techniques used to predict climate risks: a brief literature survey, Natural Hazards, 118, 925-951, doi: https://doi.org/10.1007/s11069-023-06046-2

Imani, M., Hasan, M. M., Bittencourt, L. F., McClymont, K., & Kapelan, Z. (2021). A novel machine learning application: Water quality resilience prediction Model. Science of the Total Environment, 768, 144459. https://doi.org/10.1016/j.scitotenv.2020.144459

Hasan, M. M., Lwin, K., Imani, M., Shabut, A., Bittencourt, L. F., & Hossain, M. A. (2019). Dynamic multi-objective optimisation using deep reinforcement learning: benchmark, algorithm and an application to identify vulnerable zones based on water quality. Engineering Applications of Artificial Intelligence, 86, 107–135. https://doi.org/10.1016/j.engappai.2019.08.014

Recent presentations and conferences

M. M. Hasan, M. M. Rahman, M. M. Ali and P. Machado, "QuantoTrace: Quantum Error Correction as a Service for Robust Quantum Computing," 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), Dhaka, Bangladesh, 2024, pp. 616-621, doi: 10.1109/ICEEICT62016.2024.10534391.

Khaled, A.A., Hasan, M.M., Islam, S., Papastergiou, S., Mouratidis, H. (2024). Synthetic Data Generation and Impact Analysis of Machine Learning Models for Enhanced Credit Card Fraud Detection. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 711. Springer, Cham. https://doi.org/10.1007/978-3-031-63211-2_27.

M. M. Hasan, S. Cirstea and M. N. Shraboni, "NeuroXRFitness: Music Therapy for Mental Stress Relief Using EEG Signal," 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kuala Lumpur, Malaysia, 2023, pp. 231-236, doi: 10.1109/SKIMA59232.2023.10387336.

Hasan, M. M, Kumar, Rajesh., Cirstea, Silvia., (2023). An AI-enabled blockchain-based e-Waste management Framework using Non-Fungible Tokens (NFT) to achieve net zero and imply the circular economy”, Dubai at CryptoEx Event. IEEE conferences in Blockchain and Crypto Currencies. Link: https://icbc2023.ieee-icbc.org/workshop/cryptoex-2023

Hasan, M.M., Knight, P., Tania, M.H., Bitto, A.K., Das, A. and Punja, H. (2022). A Novel Framework for Co-designing of An Artificial Intelligence Based Television-enabled Application to Address Social Isolation and Alleviating Loneliness for Older People. [online] IEEE Xplore. doi: https://doi.org/10.1109/SKIMA57145.2022.10029400 .

Farrell, J., Hameed, N. and Hasan, M.M. (2022). Designing a Classification Algorithm for Skin Lesions. [online] IEEE Xplore. doi: https://doi.org/10.1109/SKIMA57145.2022.10029401 .

Hameed, N., Hasan, M.M. and Hossain, A. (2022). An Explainable Real-time Decision Support System for Identifying Fish Diseases and Analysing Water Quality. [online] IEEE Xplore. doi: https://doi.org/10.1109/SKIMA57145.2022.10029415 .

Jia Hau Ang, Nazia Hameed, Adam Walker, Md Mahmudul Hasan, A Hybrid Transfer Learning and Segmentation approach for the Detection of Acute Lymphoblastic Leukemia, International Conference on Trends in Electronics and Health Informatics, December 2022, Puebla, México.

IRepWeedScout: real-time autonomous blackgrass classification and mapping using dedicated hardware, Gazzard, M, Hicks, Hasan, MM and Machado, P (2024). WeedScout: real-time autonomous blackgrass classification and mapping using dedicated hardware. In: Huda, MN, Wang, M and Kalganova, T, eds., Towards autonomous robotic systems: 25th annual conference, TAROS 2024, London, UK, August 21–23, 2024, proceedings, part I. Lecture notes in computer science . Cham: Springer. ISBN 9783031720581

M. M. Hasan, M. M. Rahman, M. M. Ali and P. Machado, "QuantoTrace: Quantum Error Correction as a Service for Robust Quantum Computing," 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), Dhaka, Bangladesh, 2024, pp. 616-621, doi: 10.1109/ICEEICT62016.2024.10534391.

M. M. Hasan, S. Cirstea and M. N. Shraboni, "NeuroXRFitness: Music Therapy for Mental Stress Relief Using EEG Signal," 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kuala Lumpur, Malaysia, 2023, pp. 231-236, doi: 10.1109/SKIMA59232.2023.10387336.

M. M. Hasan, A. K. Bitto, A. Chakraborty, R. Nanwani, M. M. Rahman and N. Hameed, "Net0Chain: An AI-Enabled Climate and Environmental Risks (CER) Framework for Achieving Net-Zero," 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kuala Lumpur, Malaysia, 2023, pp. 163-168, doi: 10.1109/SKIMA59232.2023.10387335.

Merin Mathew, Ashim Chakraborty, MD Hasan, Arunava Dhar, “Predictive Risk Stratification of Premature Babies Using Machine Learning: A Clinical Decision Support System”, icfsp-2024, Paris, France.

Md Mahmudul Hasan, Khin Lwin, Antesar Shabut, Miltu Kumar Ghosh, M A Hossain, “Deep Reinforcement Learning for Dynamic Multi-objective Optimisation”, 17th International Conference on Operational Research-KOI 2018, Croatia, 2018.

M. M. Hasan, A. Mohsin, M. Imani and L. F. Bittencourt, "A novel method to predict water quality resilience using deep reinforcement learning in São Paulo, Brazil," 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), Dhaka, Bangladesh, 2019, pp. 1-5, doi: 10.1109/ICIET48527.2019.9290601.

M. M. Hasan, K. Abu-Hassan, Khin Lwin and M. A. Hossain, "Reversible decision support system: Minimising cognitive dissonance in multi-criteria based complex system using fuzzy analytic hierarchy process," 2016 8th Computer Science and Electronic Engineering (CEEC), Colchester, 2016, pp. 210-215. IEEE Xplore Digital Archive.

A. Ahmed and M. M. Hasan, "A hybrid approach for decision making to detect breast cancer using data mining and autonomous agent based on human agent teamwork," 2014 17th International Conference on Computer and Information Technology (ICCIT), 2014, pp. 320-325, doi: 10.1109/ICCITechn.2014.7073116.

M. M. Hasan and J. Z. H. Khondker, "Implementing artificially intelligent ghosts to play MS. Pac-Man game by using neural network at social media platform," 2013 2nd International Conference on Advances in Electrical Engineering (ICAEE), 2013, pp. 353-358, doi: 10.1109/ICAEE.2013.6750362.

M. M. Hasan, M. T. Mahfuz and M. R. Amin, "Optimizing throughput of k-fold multicast network with finite queue using M/M/n/n+q/N traffic model," 2012 7th International Conference on Electrical and Computer Engineering, 2012, pp. 537-541, doi: 10.1109/ICECE.2012.6471606.

 

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