Associate Professor Nasimul Noman serves as Head of Discipline for Computing and Information Technology in the School of Computer and Information Sciences within the College of Engineering, Science and Environment at the University of Newcastle, Australia. He earned his Bachelor of Science and Master of Science degrees in Computer Science from the University of Dhaka, Bangladesh, followed by a PhD in Frontier Informatics from the Graduate School of Frontier Sciences at the University of Tokyo, Japan, in 2007. Noman's academic career commenced at the University of Dhaka's Department of Computer Science and Engineering, where he held positions as lecturer from 2002 to 2007, assistant professor from 2007 to 2012, and briefly as associate professor in 2012. He then pursued postdoctoral research as a JSPS fellow at the University of Tokyo's School of Engineering from 2009 to 2011, continued as a research fellow at the School of Information Science and Technology until 2013, and served as a visiting research fellow in Systems Biology at Harvard Medical School from 2012 to 2013. Since joining the University of Newcastle in November 2013 as a lecturer in the then Faculty of Engineering and Built Environment's School of Electrical Engineering and Computing, he has advanced to associate professor.
Noman specializes in evolutionary machine learning, a field where evolutionary algorithms integrate with machine learning to create optimal, robust, and resilient computational systems inspired by natural processes. His research develops meta-algorithms for optimizing deep neural networks, feature selection, classifiers, genetic network reconstruction in systems biology, combination therapies for diseases, machine scheduling, and economic load dispatch in power systems. Key publications include 'Accelerating differential evolution using an adaptive local search' (Noman and Iba, 2008; 777 citations), 'Differential evolution for economic load dispatch problems' (Noman and Iba, 2008; 626 citations), 'Enhancing differential evolution performance with local search for high dimensional function optimization' (Noman and Iba, 2005; 169 citations), 'Deep Neural Evolution: Deep Learning with Evolutionary Computation' (2020), and 'Evolutionary Computation in Gene Regulatory Network Research' (Iba and Noman, 2016). With approximately 3,649 citations on Google Scholar, his algorithms support applications in spam detection, drug design, and secure automated decision-making. Noman has earned the College of Engineering, Science and Environment Leadership Excellence Award (2024), Outstanding Contribution to Teaching Award (2023 for 2022 performance), Faculty of Engineering and Built Environment Teaching and Learning Award (2016), and JSPS Postdoctoral Fellowship. He leads funded projects such as AI for construction site safety ($10,000, 2026), CO2 emissions monitoring for mine fleets ($5,400, 2022), and evolutionary strategies for adversarial environments ($20,000, 2021-2022).