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Dr Ayesha Sohail

Dr Ayesha Sohail

Senior Lecturer, Information Technology

Profile Summary

Dr Ayesha Sohail is a Senior Lecturer in Information Technology with expertise in Machine Learning, Data Science, Artificial Intelligence, Applied Mathematics, Statistics, and Computational Modelling. She is a Senior Fellow of the Higher Education Academy (SFHEA) and has extensive experience teaching, coordinating, and developing undergraduate and postgraduate courses across diverse educational settings.

At Skyline Higher Education Australia (SHEA), Dr Sohail teaches Machine Learning, Data Science, and Capstone Project units, helping students develop the technical, analytical, and professional skills required for successful careers in the rapidly evolving technology sector. She is passionate about bridging the gap between theory and practice by incorporating authentic industry-focused projects, real-world datasets, and applied problem-solving approaches into her teaching.

Her research integrates mathematical, statistical, and machine learning methodologies to address complex real-world challenges. She has worked across interdisciplinary fields including artificial intelligence, optimisation, public health analytics, computational modelling, and data-driven decision-making. Her computational algorithms have received international recognition from MathWorks for
their innovation and educational value.

Dr Sohail has successfully secured research funding, supervised Honours and PhD students, and collaborated with multidisciplinary research teams in Australia and internationally. She is committed to student-centred learning, curriculum innovation, and preparing graduates with the skills needed to thrive in a technology-driven global environment.

Areas of Interest

  • Machine Learning and Artificial Intelligence
  • Data Science and Predictive Analytics
  • Applied Statistics and Mathematical Modelling
  • Data Analytics and Visualisation
  • Numerical Analysis and Optimisation
  • Programming and Computational Methods
  • Biostatistics and Public Health Analytics
  • Learning Analytics
  • Curriculum and Assessment Design
  • Industry-Focused Technology Education
  • Capstone Project Supervision
  • Educational Technology and Digital Learning

Qualifications

  • Doctor of Philosophy (PhD) from The University of Sheffield UK
  • Senior Fellow of the Higher Education Academy (SFHEA), UK
  • Advanced Training in Mathematics, Operational Research and Statistics (MORS), Loughborough University, United Kingdom

Teaching Expertise

Dr Sohail has extensive experience teaching and coordinating units in:

 

  • Machine Learning
  • Data Science
  • Artificial Intelligence
  • Data Analytics
  • Statistics for Data Science
  • Programming
  • Applied Mathematics
  • Information Technology Capstone Projects
  • Research Methods
  • Computational Modelling

Professional Highlights

  • Senior Fellow of the Higher Education Academy (SFHEA).
  • International recognition from MathWorks for innovative machine learning and computational algorithms.
  • Extensive experience designing and delivering technology-focused curricula for higher education.
  • Supervisor and mentor of Honours and PhD students at the University of Sydney, Australia.
  • Experienced researcher in machine learning, mathematical modelling, data science, and public health analytics.
  • Dedicated to developing industry-ready graduates through practical, project-based learning experiences.

 

Research Funding & Awards

 

  • 2025 Australian Mathematical Sciences Institute (AMSI)  Research Award awarded in support of advanced research in applied mathematics and data-driven methodologies.
  • 2024 University of Sydney–Fudan University Ignition Grant, Indicator to Track Climate Change Impacts on Health Inequity, supporting international interdisciplinary research on climate-driven health outcomes.
  • 2024 University of Sydney International SDG Collaboration Program Grant Climate Change, Malnutrition, and Childhood Obesity, a collaborative project involving researchers from Sydney, Shanghai, and New Delhi.
  • 2021 TÜBİTAK Research Grant  Deep Learning for DICOM-CT Analysis of Gastric Cancer, supporting the development of artificial intelligence tools for medical imaging and cancer diagnostics.
  • 2018 National Natural Science Foundation of China (NSFC) Grant (approximately USD 100,000), Stochastic Partial Differential Equations and Low-Rank Uncertainty Quantification, including support for two PhD research students.
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