Professor Brijesh Verma, PhD
Nathan Campus, Science 2 (N34), Room 1.42
170 Kessels Road, Brisbane, Queensland 4111, Australia
E-mail: firstname.lastname@example.org or email@example.com
Phone:+61 (07) 373 53761
Brijesh Verma has moved to Griffith University, his new e-mail is firstname.lastname@example.org
Brijesh Verma is a Professor at Griffith University, Brisbane, Australia. He is a General Co-Chair of International Joint Conference on Neural Networks (IJCNN 2023). He was a Professor and the Director of the Centre for Intelligent Systems (CIS) at Central Queensland University in Brisbane, Australia. He is the President of INNS (International Neural Network Society) Australia Chapter. He was the Chair of the IEEE Computational Intelligence Society's Queensland Chapter and under his leadership the Chapter won Outstanding Chapter Award. He was a member of the Australian Research Council (ARC) College of Experts (2018-2020).
His main research interests include Computational Intelligence and Pattern Recognition. He has authored/co-authored/co-edited 13 books (most recent books: Roadside Video Data Analysis: Deep Learning, Pattern Recognition Technologies and Applications: Recent Advances), 9 book chapters and over 200 papers [Download Papers via Google Scholar, Download Papers via CQU's Acquire Database] in areas such as neural networks, evolutionary algorithms, deep learning, pattern recognition, computer vision, image processing, data mining, digital mammography and web information retrieval. He has developed a number of novel techniques for segmentation and classification of images, roadside fire risk assessment, training of neural networks, creation of ensemble classifiers, optimisation using multi-objective evolutionary algorithm, segmentation of cursive handwriting, facial feature selection, detection and classification of microcalcification and web search. His publications and techniques have been widely cited (Citations in Google Scholar).
He was an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and an Editor in Chief of International Journal of Computational Intelligence and Applications (IJCIA). He was also an Associate Editor of IEEE Transaction on Biomedicine in Information Technology. He is an editorial board member of Applied Soft Computing and Neural Computing and Applications. He was a Special Session Co-Chair of International Joint Conference on Neural Networks (IJCNN 2021). He was a Co-Chair of Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition at IEEE SSCI 2019 and was the Chair of Special Session on Ensemble Models for Pattern Recognition and Data Mining at IEEE WCCI 2018. He was the Chair of Special Session on Machine Learning for Computer Vision at IEEE WCCI 2014 and IEEE WCCI 2016. He is/was a Program Committee Member of over 90 international conferences including IEEE International Joint Conference on Neural Networks, International Conference on Neural Information Processing, International Conference on Image and Vision Computing New Zealand and IEEE Congress on Evolutionary Computation.
He has received many competitive research grants including many ARC (Australian Research Council) grants and collaborative industry grants. His most recent ARC grants are ARC Discovery Project (2021-2023) A novel automatic neural network feature extractor which is investigating explainable feature extraction abilities of convolutional as well as traditional neural networks, ARC Discovery Project (2020-2022) Deep learning architecture with context adaptive features for image parsing which is developing a novel deep learning technique, ARC Linkage Project (2018-2021) which is developing an automated system for the analysis of road safety and conditions, ARC Discovery Project (2016-2019) which was focused on developing a novel framework for optimised ensemble classifiers and ARC Linkage Project (2014-2017) which was focused on developing novel tools for roadside fire risk assessment using computational intelligence and pattern recognition techniques.
His teaching interests include programming (Java, C++), data structures and algorithms, software development, operating systems, computer architecture, emerging technologies, pattern recognition, digital image processing, neural networks and neural evolutionary computing. He is also involved in supervising research students. Currently he is supervising 5 research higher degree students. Overall, 37 research students have completed a research degree under his supervision.
If you are looking for a PhD/Masters research topic, please send your brief CV to Prof. Verma by e-mail.