Faculty:Faculty of Science and Engineering
School:Computing and Information Science
Dr Md Mahmudul Hasan is an expert in building data-driven AI products with more than 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.
Connect with Dr Hasan on LinkedIn
Dr Hasan has completed his PhD in Artificial Intelligence from Anglia Ruskin University, funded by the EU.
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.
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.