Staff Profile
Swathi Ganesan
Deputy Associate Dean, Computer Science Department
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I am the Deputy Associate Dean for the Computer Science Department and Programme Director for the MSc Data Science at York St John University, London. In these roles, I lead curriculum design, programme development, and quality assurance to ensure our academic offerings uphold rigorous standards, align with industry needs, and consistently deliver high levels of student satisfaction and academic performance.
With years of professional experience in the IT sector, I transitioned from Industry to Academia, bringing deep expertise in software design, development, testing, and quality assurance. This background informs my teaching practice and enhances my contributions to course leadership, curriculum innovation, and quality enhancement. As a member of the Senior Academic Leadership Team (SALT) and the School Quality Panel (SQP), I actively support institutional strategies aimed at elevating teaching standards, optimising programme delivery, and improving student outcomes.
In parallel with my academic leadership, I am a PhD researcher in Data Science, specialising in Artificial Intelligence, Machine Learning, and Deep Learning to address complex challenges in the medical domain. My research bridges technology and healthcare, with a focus on developing innovative, high-impact solutions. I have published over 15+ peer-reviewed conference and journal papers and contribute to the wider academic community through editorial board service and peer-review engagements.
As a Senior Fellow of the Higher Education Academy (SFHEA), I am committed to advancing inclusive, evidence-informed teaching and learning practices in higher education.
- School – London, York Business School
- Email – s.ganesan@yorksj.ac.uk
- Research - View my work in RaY
Further information
Teaching
As the Module Director, I lead two modules - Artificial Intelligence Machine Learning and Applied Research Project.
Research
My research focuses on leveraging Artificial Intelligence (AI), Machine Learning (ML), Deep Learning- Generative Adversarial Networks (GANs) and Generative AI to address critical challenges in the medical domain. Specifically, I am exploring innovative approaches to enhance data quality, feature engineering, and model optimisation to improve the accuracy and robustness of predictive models in healthcare. My goal is to bridge the gap between technology and healthcare by developing scalable, effective, and impactful solutions that can address pressing medical challenges and support clinical decision-making. Through my work, I aim to drive meaningful advancements in medical AI, promote innovation, and inspire future research that leverages the potential of technology to solve real-world problems.
