Mohammad Ali Moni

Mohammad Ali Moni

Program Lead, Program for AI and Digital Health Technology

Doctor of Philosophy - PhD, Bioinformatics, Machine learning, Data Science, Computational Modelling

Biography

Mohammad holds a PhD in Artificial Intelligence & Digital Health Data Science in 2015 from the University of Cambridge, UK followed by postdoctoral training at the University of New South Wales, University of Sydney Vice-chancellor fellowship, and Senior Data Scientist at the University of Oxford. Before joining the Institute, Dr Moni worked at the University of Queensland.

He is an Artificial Intelligence, Computer Vision & Machine learning, Digital Health Data Science, Health Informatics and Bioinformatics researcher developing interpretable and clinical applicable machine learning and deep learning models to increase the performance and transparency of AI-based automated decision-making systems.

His research interests include quantifying and extracting actionable knowledge from data to solve real-world problems and giving humans explainable AI models through feature visualisation and attribution methods. He has applied these techniques to various multi-disciplinary applications such as medical imaging including stroke MRI/fMRI imaging, real-time cancer imaging. He led and managed significant research programs in developing machine-learning, deep-learning and translational data science models, and software tools to aid the diagnosis and prediction of disease outcomes, particularly for hard-to-manage complex and chronic diseases.

Research

His research interests include:

  • Data Science,
  • machine learning,
  • deep learning algorithms,
  • models and software tools,

Dr Moni uses different types of data, especially medical images, neuroimaging, EEG, ECG, Bioinformatics, and secondary usage of routinely collected data.

Professional
  • Acquired a PhD in Artificial Intelligence & Digital Health Data Science from the University of Cambridge.
  • Expert in Artificial Intelligence, Computer Vision, Machine Learning, and Digital Health Data Science.
  • Pioneered in quantifying and deriving actionable insights from data, emphasizing explainable AI models through feature visualisation and attribution methods, notably applied to medical imaging realms like stroke MRI/fMRI and real-time cancer imaging.
  • Led substantial research initiatives in developing advanced machine-learning, deep-learning, and translational data science models, targeting the diagnosis and prognosis of complex and chronic diseases.
  • Published in top medical journals including Lancet.