Dr Md Mahmudul Hasan

Dr Md Mahmudul Hasan is an expert in building data-driven AI products, with 10+ years of experience. He has specialised in artificial intelligence, 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.

[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 20+ 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, 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
• Health Informatics
• Net zero solutions

Areas of research supervision

• Building AI and Data products for healthcare and net-zero
• Health informatics
• AI-enabled smart solutions

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

Memberships, editorial boards

• IEEE Membership
• MCDM 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 funded by Innovate UK
• QR funding for Sustainable Futures Research and Knowledge Exchange and policy funds

Selected recent publications

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

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

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.