DCU researcher Asma Slaimi provides an overview of her research in AI and machine learning applications for water management systems. Asma Slaimi is presently pursuing a PhD at Dublin City University’s School of Engineering and the Insight SFI Research Centre for Data Analytics. During her master’s studies, she collaborated with Dr Brian Davis at the Insight centre on a project involving the creation of a supervised machine learning model for an e-recruitment platform as part of the Data Live initiative. This project utilized ontology-based named entity recognition (NER) to train the model to identify known terms and concepts in unstructured or semi-structured texts.
Equipped with a solid background in AI and machine learning, Slaimi is focusing her PhD research on developing advanced AI models for water system management. Collaborating with Arup, her project emphasizes the application of artificial intelligence and machine learning to enhance water management practices in response to the growing demand for data-driven solutions in this field. Working alongside a distinguished supervisory team including Dr Michael Scriney, Dr Susan Hegarty, Prof Fiona Regan, and Prof Noel E O’Connor, Slaimi’s research explores diverse aspects of AI and machine learning technologies to derive actionable insights for improving water management efficiency.
The significance of Slaimi’s research lies in its potential to transform water management systems, rendering them more efficient, sustainable, and cost-effective. With global water resources being impacted by climate change, the research outcomes hold immense promise. By leveraging AI and machine learning technologies, the research aims to forecast water usage patterns, monitor water quality, and promptly identify issues, thereby enabling optimal utilization of this critical resource.
Driven by a passion for making a meaningful societal impact through scientific research, Slaimi draws inspiration from her internship experience under Dr Brian Davis at the Insight Centre for Data Analytics. She acknowledges the challenges of communicating the complexities and benefits of AI and machine learning to non-technical stakeholders, emphasizing the nuanced nature of implementing such solutions in sensitive areas like water management that involve multifaceted variables and ethical considerations.
In light of the increased public interest in science and its problem-solving potential accentuated by the Covid-19 pandemic, Slaimi underscores the importance of engaging with the public. To foster engagement, she actively participates in academic conferences, simplifies research findings in accessible language for broader audiences, and utilizes social media platforms to disseminate updates and insights. Making science relatable and comprehensible to the public is key to nurturing a deeper understanding and appreciation for scientific endeavors.
