Lero’s Kevin McDonnell explains how actionable data is the important thing to conducting research that may result in positive change.
Kevin McDonnell, with an MSc in Computer Science and Software Engineering from Maynooth University and an MSc in Artificial Intelligence (AI) from the University of Limerick, was well placed to undertake a PhD in Artificial Intelligence and Machine Learning at Lero Research Center for Software and the University of Limerick. Limerick.
One of his areas of research is the safety of electric vehicles. In recent research articleMcDonnell and his colleagues assessed the assorted risks related to using electric and carbon-based vehicles to generate insights and improve safety measures for automakers and policymakers.
“I always wanted to do something that would benefit society,” McDonnell says of his decision to have interaction in scientific research. Here he tells us somewhat more about his work.
Tell us about your current research.
After completing my MSc in Artificial Intelligence, Lero and the University of Limerick provided me with a wonderful opportunity to research and analyze machine learning models and their applications in vehicle risk prediction. It was the peak of the Covid-19 pandemic and I used to be working as a software engineer. I used to be hesitant at first, but taking this position was among the finest decisions I even have ever made.
Since 2020, my research has focused on the regulation and use of vehicle telematics data, novel machine learning methods for claims prediction, the risks of electric vehicles, and the explainability of machine learning. I even have published three articles and am working on a fourth article and my Viva.
Why do you’re thinking that your research is essential?
My research covers many necessary topics. The most vital finding from my current research is that electric vehicles usually tend to be involved in fault-related collisions than traditional vehicles. This research should help regulators and manufacturers reduce road accidents and deaths, which is something I might like to see.
Additionally, I’m involved within the interpretation of models in machine learning, promoting ethical standards and transparency of decision-making systems, areas where I hope that my research could have further positive effects.
What inspired you to change into a researcher?
No single memory really stands out. I at all times desired to do something that will profit society, and research or teaching gave the look of one of the best technique to achieve that. For one reason or one other, I stayed within the industry for much longer than I expected. However, I’m very grateful that we’re currently in a privileged position doing research.
What are the largest challenges or mistakes you face as a researcher in your field?
Acquiring data is essentially the most difficult a part of research. Even working with a personal industry partner with access to thousands and thousands of data points proved to be a challenge.
Gaining access to good and useful data is the premise for impactful research. Without this, it’s difficult to prove our claims, and using synthetic options is at all times subject to bias.
Do you’re thinking that public engagement with science has modified lately, especially for the reason that Covid-19 pandemic?
I began research during Covid-19, so I’m unsure what it was like before. However, I feel that research engagement could also be non-existent or all encompassing with no scope in between. I even have benefited from working with Lero to assist me find one of the best ways to have interaction socially. Without them, it’s actually rather more difficult to achieve a wider audience.
