Rate My Professor Ganesh Krishnasamy

Ganesh Krishnasamy serves as a Senior Lecturer in the School of Information Technology at Monash University Malaysia, having joined the institution as a Lecturer in July 2019. He obtained his B.Eng. degree in electrical and electronic engineering from Universiti Kebangsaan Malaysia in 2004 and his M.Eng. degree from the same university in 2007. After accumulating more than four years of experience in the manufacturing industry, Krishnasamy pursued and completed his PhD in Electrical Engineering at the University of Malaya. Subsequently, he worked as a data scientist at Sime Darby Plantation for about one year before transitioning to academia at Monash University Malaysia. His academic career at Monash has progressed to his current role as Senior Lecturer, where he contributes to teaching units such as FIT1045 Introduction to Programming and supervises postgraduate research in information technology.

Krishnasamy's research specializations encompass computer vision, machine learning, optimization, pattern recognition, manifold learning, semi-supervised learning, and sparse optimization, with particular emphasis on commercial applications in multimedia processing and understanding. His scholarly output includes two books, one book chapter, seven articles, and eight conference papers. Key publications feature the book 'Cohort Intelligence: A Socio-Inspired Optimization Method' (2017, co-authored with Anand J. Kulkarni and Ajith Abraham), which has received 64 citations, and editorial contributions to 'Constraint Handling in Metaheuristics and Applications' (2021, with Anand J. Kulkarni, Efrén Mezura-Montes, Yong Wang, and Amir H. Gandomi). Recent works include 'ActNetFormer: Transformer-ResNet Hybrid Method for Semi-supervised Action Recognition in Videos' (2025), 'GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction' (2025), 'A Comparative Analysis of Deep Learning Models and Gradient Computation for Rally Detection in Badminton Videos' (2025), 'Hessian Unsupervised Extreme Learning Machine' (2024), and 'Histohdr-Net: Histogram Equalization for Single LDR to HDR Image Translation' (2024). His research has accumulated over 370 citations on Google Scholar, underscoring his influence in deep learning, computer vision, and related optimization techniques. Current projects involve machine learning algorithms for computer vision applications, HDR content creation, and no-reference image quality assessment using deep learning models.