Rate My Professor Iman Ardekani

Iman Ardekani is the National Head of Discipline and Senior Lecturer in the Mathematics and Data Analytics Discipline Group at the School of Arts and Sciences, University of Notre Dame Australia. He possesses multidisciplinary expertise across mathematics, computer science, and electrical engineering, holding dual PhDs—one in Mathematics (University of Auckland, 2020) and another in Electrical and Computer Engineering (University of Auckland, 2011)—along with an MSc (2002) and BSc (Hons) (2000) in Electrical and Computer Engineering from the University of Tehran. Prior to his current appointment, Ardekani served as Associate Professor in the Department of Computing and Information Technology at Unitec Institute of Technology from 2013 and as a postdoctoral researcher at the University of Auckland from 2011 to 2013. A committed educator, he teaches a range of STEM subjects including linear algebra, calculus, computational modeling, data science and AI, and computational intelligence to undergraduate and graduate students. His research endeavors have attracted 12 funded grants, resulting in over 80 publications in international journals and conference proceedings, underscoring his influence in advancing computational methods and signal processing applications.

Ardekani's research specializations encompass Bayesian data analysis and uncertainty quantification, stochastic optimisation and control theory, computational intelligence, and deep learning. Notable publications include 'Generalized framework for liquid neural network upon sequential and non-sequential tasks' (Mathematics, 2024, with P.K. Karn and W. Abdulla), 'Tomato disease detection with lightweight recurrent and convolutional deep learning models for sustainable and smart agriculture' (Frontiers in Sustainability, 2024, with A.T. Le and M. Shakiba), 'AI-enhanced personality identification of websites' (Information, 2024, with S.A. Chishti and S. Varastehpour), 'A Bayesian approach to online estimation of airgap spatial variation in induction machines with static eccentricity' (Applied Mathematical Modelling, 2024, with R. Nicholson), 'Optimizing plant disease classification with hybrid convolutional neural network–recurrent neural network and liquid time-constant network' (Applied Sciences, 2024), 'Active noise control in three dimensions' (IEEE Transactions on Control Systems Technology, 2014), and 'Adaptive signal processing algorithms for creating spatial zones of quiet' (Digital Signal Processing, 2014). He maintains professional affiliations as a Senior Member of IEEE, Life Member of APSIPA, and member of ANZIAM and NZMS.