Lecturer/Assistant Professor - Maastricht University & Open Universiteit
I am a Lecturer/Assistant Professor at DACS at Maastricht University & Open Universiteit. I have also recently worked as a Visiting Scholar at the University of California, Berkeley and as an expert working group member at the World Economic Forum.
I’m passionate about exploring the challenges and opportunities of emerging technologies such as blockchain, machine learning and artificial intelligence. I have a diverse academic and professional background that spans across several countries and domains. I enjoy collaborating with industry partners and academic peers on cutting-edge research projects that have real-world impact. I also enjoy teaching and mentoring students who share my enthusiasm for learning new skills and solving complex problems.
Background
I have a Ph.D. in Computer Science from Lero, Science Foundation Ireland Research Centre for Software, where I developed a taxonomy of centralization in distributed ledgers under the supervision of Dr. Jim Buckley and Dr. Andrew Le Gear.
I have received several awards for my research excellence and contributions to the field of software engineering and blockchain technology. Some notable ones include the IEEE Technology and Engineering Management Society’s Outstanding Ph.D. Dissertation Award and Early-Career Award in 2021. I was also selected as a member of the Expert Code Review Committee for the Irish Covid Tracker Application by the Irish government.
Research
My current research focuses on improving the understanding of environmental impact of distributed information systems, such as blockchain networks that consume large amounts of energy for securing transactions and maintaining consensus among nodes. This research is partially funded by Protocol Labs (the creators of IPFS).
In addition to this project, I am also a Co-PI of the RADeLE project (funded by Huawei) on developing a reference software architecture for machine learning frameworks that can support various types of applications such as computer vision, natural language processing and recommender systems. This project aims to provide guidelines and best practices for designing scalable, robust and efficient machine learning systems that can leverage cloud computing resources effectively.
