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 the Program Director for the MSc Data Science program at York St John University, London. In these roles, I oversee curriculum design, program development, and quality assurance, ensuring our programs maintain academic rigour, meet industry demands, and consistently deliver high satisfaction and stronger performance from students.
With over 7 years of experience in the IT sector, I transitioned from industry to academia, specialising in software design, development, testing, and quality assurance. This professional expertise informs my teaching and has enriched my contributions to course leadership, curriculum development, and quality enhancement. As part of the Senior Academic Leadership Team (SALT) and the School Quality Panel (SQP), I have supported initiatives that elevate teaching standards, optimise program delivery, and enhance student outcomes.
Alongside my academic responsibilities, I am a PhD researcher in Data Science, focusing on Artificial Intelligence, Machine Learning, and Deep Learning to tackle critical challenges in the medical domain. My research bridges technology and healthcare, driving innovative solutions with global impact. I have published over 15 peer-reviewed conferences and journals, and actively contribute to the academic community through editorial and peer-review activities.
- School – London
- Email – s.ganesan@yorksj.ac.uk
- Research - View my work in RaY
Further information
Teaching
At York St John University London Campus, I am Module Director for MSc Data Science. 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 optimization 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.
