Dr Ali Haidar
Post-Doc Fellow

Dr Ali Haidar

Medicine & Health
SWS Clinical School

I am a post-doctoral research fellow at the South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales (UNSW). I am also an affiliate member at the Ingham Institute for Applied Medical Research. 

I am working on the Australian Computer-Assisted Theragnostics (AusCAT), which is a framework that has been initially established across a network of Australian radiation oncology departments to enable data pooling and analyses. My responsibilities include establishing and maintaining the necessary infrastructure (software and hardware) and undertaking research investigations using this network (outcome models, decision support tools, etc.).

 

Journal Review:

  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE ACCESS
  • IEEE Transactions on Industrial Informatics
  • IEEE Journal of Biomedical and Health Informatics
  • Soft Computing

Conference Review:

  • International Conference on Neural Information Processing (ICONIP)
  • IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
E-mail
a.haidar@unsw.edu.au
Location
Ingham Institute for Applied Medical Research

Publications

  • Book Chapters | 2017
    Dr Ali Haidar
    Haidar A; Verma B, 2017, 'Monthly Rainfall Categorization Based on Optimized Features and Neural Network', in AI 2017: Advances in Artificial Intelligence 30th Australasian Joint Conference, Melbourne, VIC, Australia, August 19–20, 2017, Proceedings, Springer
  • Journal articles | 2021
    Dr Ali Haidar
    Woodbright M; Verma B; Haidar A, 2021, 'Autonomous deep feature extraction based method for epileptic EEG brain seizure classification', Neurocomputing, vol. 444, pp. 30 - 37, http://dx.doi.org/10.1016/j.neucom.2021.02.052
    Journal articles | 2021
    Dr Ali Haidar
    Haidar A; Field M; Sykes J; Carolan M; Holloway L, 2021, 'PSPSO: A package for parameters selection using particle swarm optimization', SoftwareX, vol. 15, pp. 100706 - 100706, http://dx.doi.org/10.1016/j.softx.2021.100706
    Journal articles | 2018
    Dr Ali Haidar
    Haidar A; Verma B, 2018, 'A novel approach for optimizing climate features and network parameters in rainfall forecasting', Soft Computing, vol. 22, pp. 8119 - 8130, http://dx.doi.org/10.1007/s00500-017-2756-7
    Journal articles | 2018
    Dr Ali Haidar
    Haidar A; Verma B, 2018, 'Monthly Rainfall Forecasting Using One-Dimensional Deep Convolutional Neural Network', IEEE Access, vol. 6, pp. 69053 - 69063, http://dx.doi.org/10.1109/ACCESS.2018.2880044
    Journal articles | 2017
    Dr Ali Haidar
    Haidar A; Verma B, 2017, 'Monthly rainfall forecasting using neural networks for sugarcane regions in Eastern Australia', Water Science and Technology: Water Supply, vol. 17, pp. 907 - 920, http://dx.doi.org/10.2166/ws.2016.099
  • Patents | 2021
    Dr Ali Haidar
    Haidar A, 2021, Computer system configured for issuing a personalized vehicle number plate, Patent No. 10909330
    Conference Papers | 2019
    Dr Ali Haidar
    Haidar A; Jan ZM; Verma B, 2019, 'Evolving One-Dimensional Deep Convolutional Neural Network: A Swarm based Approach', in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, IEEE, pp. 1299 - 1305, presented at 2019 IEEE Congress on Evolutionary Computation (CEC), 10 June 2019 - 13 June 2019, http://dx.doi.org/10.1109/CEC.2019.8790036
    Conference Papers | 2018
    Dr Ali Haidar
    Haidar A; Verma B; Sinha T, 2018, 'A Novel Approach for Optimizing Ensemble Components in Rainfall Prediction', in 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, IEEE, presented at 2018 IEEE Congress on Evolutionary Computation (CEC), 08 July 2018 - 13 July 2018, http://dx.doi.org/10.1109/CEC.2018.8477739
    Conference Papers | 2018
    Dr Ali Haidar
    Haidar A; Verma B, 2018, 'Learning based fusion in ensembles for weather forecasting', in ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, IEEE, pp. 72 - 78, presented at 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 29 July 2017 - 31 July 2017, http://dx.doi.org/10.1109/FSKD.2017.8393362
    Conference Papers | 2018
    Dr Ali Haidar
    Sinha T; Verma B; Haidar A, 2018, 'Optimization of convolutional neural network parameters for image classification', in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, IEEE, pp. 1 - 7, presented at 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 27 November 2017 - 01 December 2017, http://dx.doi.org/10.1109/SSCI.2017.8285338
    Conference Papers | 2018
    Dr Ali Haidar
    Sinha T; Haidar A; Verma B, 2018, 'Particle Swarm Optimization Based Approach for Finding Optimal Values of Convolutional Neural Network Parameters', in 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, IEEE, presented at 2018 IEEE Congress on Evolutionary Computation (CEC), 08 July 2018 - 13 July 2018, http://dx.doi.org/10.1109/CEC.2018.8477728
    Conference Papers | 2017
    Dr Ali Haidar
    Haidar A; Verma B, 2017, 'A genetic algorithm based feature selection approach for rainfall forecasting in sugarcane areas', in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, IEEE, presented at 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 06 December 2016 - 09 December 2016, http://dx.doi.org/10.1109/SSCI.2016.7849935
    Conference Papers | 2017
    Dr Ali Haidar
    Haidar A; Verma B, 2017, 'A hybrid genetic algorithm for climate input features and neural network parameters selection', in Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, presented at GECCO '17: Genetic and Evolutionary Computation Conference, 15 July 2017 - 19 July 2017, http://dx.doi.org/10.1145/3067695.3076038

Awards

Grants

  • Ingham Institute Data and Cancer Research Grant 2019, Ensemble Learning in Cancer Related Applications ($25,000)
  • Ingham Institute Data and Cancer Research Grant 2019, Unsupervised Machine Learning for Detecting and Fixing Variations in Cancer Patients Medical Records ($15,000)

Media