Dr Jarutas Andritsch

Biography

Dr Jarutas Andritsch received a PhD in Computer Science from the University of Southampton, UK, in 2014. She joined Solent University in January 2021 and has since contributed to teaching, research, and academic development in areas related to computing, data science, artificial intelligence, and technology-enhanced learning.

Her research spans both the educational and computing domains, with a particular interest in artificial intelligence, machine learning, data analytics, and the application of digital technologies to address real-world challenges. She has worked on a variety of interdisciplinary projects involving AI-assisted medical image analysis, disease prediction and risk assessment, explainable artificial intelligence for healthcare decision support, predictive analytics for patient outcomes, mobile health and tele-rehabilitation systems, smart-device-based health monitoring, and machine learning approaches for analysing social media and population data. Her work has also explored computer vision techniques for personalised health and wellbeing applications, as well as the use of natural language processing and sentiment analysis to support mental health monitoring and digital mental health interventions.

In the educational field, her interests focus on learning analytics, educational data mining, artificial intelligence in education, and technology-enhanced learning. She has conducted research on innovative approaches to computing education, including inclusive programming pedagogy, supporting learners from diverse academic backgrounds, content co-creation in programming education, and the development of data literacy and digital skills through collaborative and inquiry-based learning approaches. Her work seeks to improve student engagement, confidence, belonging, and success in computing and data-related disciplines.

Her overall research vision is centred on leveraging data-driven technologies, artificial intelligence, and machine learning to support both healthcare systems and educational environments. The aim of this work is to improve quality of life, increase accessibility to advanced healthcare technologies, support evidence-based clinical and educational decision-making, generate actionable insights from complex data, and enhance teaching and learning practices through responsible and innovative use of technology.

Pure profile

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