Dr Soheila Gheisari completed her Ph.D. in Computer Science at the University of Technology Sydney (UTS) in 2019. During her doctoral studies, she gained practical experience as a data analyst through an internship at the Australian Bureau of Statistics (ABS), where she applied various statistical methods to analyze household survey data. Following the completion of her Ph.D., she served as a research fellow at UTS from 2019 to 2020. Additionally, she has been actively involved in teaching since July 2019, primarily at Victoria University. In her teaching role, she instructs a range of courses related to Information Technology (IT), with a particular focus on subjects related to data mining and machine learning. Her research interest includes machine learning, data science, computer vision, and AI in medicine.
Experience
- 2019–2023 Lecturer in College of Engineering and Science Victoria University, Sydney Campus
- 2019–2020 Research Fellow School of Health, University of Technology Sydney
- 2021–2022 Lecturer, School of Information Technology and Engineering, Melbourne Institute Technology
Education
- Ph.D.: Computer Science, University of Technology Sydney, 2015-2019, Thesis Title: Computer-Aided Diagnosis Systems in the Classification of Neuroblastoma Histological Images
- Master: Engineering, Azad University, Tehran, Iran, 2003 – 2006
Bachelor: Engineering, K. N. Toosi, Tehran, Iran, 1999-2003
Teaching Experience
- Data Warehousing and Mining
- Data Analytics in Cybersecurity
- Cybersecurity Law, Regulations and Policy
- Mathematics for Data Analytics Mathematics for Computing
- Introduction to Data Analytics
- Advanced Data Analytics
- Data Visualization
- Contemporary Topics in IT
- Programming Fundamentals Introduction to Research
- Supervision
- Master’s Capstone Projects and Research Theses
Honors
- 2022 Teaching Excellence Award, Victoria University
- 2021 Teaching Excellence Award, Victoria University
- 2018 Australian Postgraduate Research Internship from Australian Bureau of Statistics (ABS)
Publication
- S Gheisari, S Shariflou, J Phu, PJ Kennedy, A Agar, M Kalloniatis, A combined convolutional and recurrent neural network for enhanced glaucoma detection, Scientific reports 11 (1), 2021
A Rafiei, A Rezaee, F Hajati, S Gheisari, M Golzan, SSP: Early prediction of sepsis using fully connected LSTM-CNN model, Computers in biology and medicine 128, 104110, 2021 - F Hajati, A Rezaee, S Gheisari, Genetic algorithms for scheduling examinations,
International Conference on Advanced Information Networking and Applications, 524-532, 2021 - CJP Sopo, F Hajati, S Gheisari, DeFungi: Direct mycological examination of microscopic fungi images, CJP Sopo, arXiv preprint arXiv:2109.07322, 2021
- S Gheisari, A Rezaee, F Hajati, Co-evolution Genetic Algorithm Approximation Technique for ROM-Less Digital Synthesizers, International Conference on Advanced Information Networking and Applications, 2021
- S Gheisari, A Rezaee, F Hajati, Solving Job Scheduling Problem Using Genetic Algorithm, International Conference on Advanced Information Networking and Application, 2021
- S Gheisari, M Golzan, J Phu, PJ Kennedy, A Agar, M Kalloniatis, A combined convolutional and recurrent neural network applied to fundus videos markedly enhances glaucoma detection, Investigative Ophthalmology & Visual Science 61 (9), 2020
- F Hajati, A Cheraghian, O Ameri Sianaki, B Zeinali, S Gheisari, Polar Topographic Derivatives for 3D Face Recognition: Application to Internet of Things Security, Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019)
- S Gheisari, DR Catchpoole, A Charlton, PJ Kennedy, Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images, Journal of pathology informatics 9 (1), 17, 2018
- S Gheisari, DR Catchpoole, A Charlton, Z Melegh, E Gradhand, Computer aided classification of neuroblastoma histological images using scale invariant feature transform with feature encoding, Diagnostics 8 (3), 56, 2018
- F Hajati, M Tavakolian, S Gheisari, Y Gao, AS Mian, Dynamic texture comparison using derivative sparse representation: Application to video-based face recognition,
IEEE Transactions on Human-Machine Systems 47 (6), 2017 - F Hajati, A Cheraghian, S Gheisari, Y Gao, AS Mian, Surface geodesic pattern for 3D deformable texture matching, Pattern Recognition 62, 21-32, 2017
- S Gheisari, D R. Catchpoole, A Charlton, P J. Kennedy, Patched Completed Local Binary Pattern is an Effective Method for Neuroblastoma Histological Image Classification, The 15th Australasian Data Mining Conference, 2017
- S Gheisari, A Charlton, DR Catchpoole, PJ Kennedy, Computers can classify neuroblastic tumours from histopathological images using machine learning, Pathology 49, S72-S73, 2017
- A Cheraghian, F Hajati, S Gheisari, Y Gao, 2.5 D Face Recognition Using Gabor Discrete Cosine Transform, International Journal of Computer and Information Engineering 10 (2), 308-311, 2016
- M Tavakolian, F Hajati, AS Mian, Y Gao, S Gheisari, Derivative Variation Pattern for Illumination-Invariant Image Representation, Image Processing (ICIP), 2013
- S Gheisari, S Javadi, A Kashaninya, 3D Face Recognition using Patch Geodesic Derivative Pattern, International Journal of Smart Electrical Engineering 2 (3), 127-132, 2013
- A Cheraghian, F Hajati, AS Mian, Y Gao, S Gheisari, 3D Face Recognition using Topographic High-order Derivatives, Image Processing (ICIP), 2013
- M Tavakolian, F Hajati, AS Mian, S Gheisari, 2013, Sparse Variation Pattern for Texture Classification, International Conference on Digital Image Computing: Techniques and Applications (DICTA).