Dino Arla

I am an Electrical Engineer and applied Machine Learning researcher currently based in Indonesia. I work at PLN, where I have over eight years of industry-embedded experience developing and deploying analytical systems for national power distribution networks. I am currently completing a Master’s degree in Technology Management with a specialization in Business Analytics at ITS, which strengthens the analytical and methodological foundation of my research-oriented work.

My research focuses on applying machine learning to smart grid analytics, particularly electricity theft detection and non-technical loss mitigation in large-scale energy systems. I am interested in anomaly detection, cost-sensitive learning, and ranking-based decision models that support operational decision-making under uncertainty, limited resources, and explicit economic trade-offs. Rather than treating prediction as an isolated task, my work frames machine learning as a decision-support tool for improving the reliability, efficiency, and resilience of critical energy infrastructure. This research direction has resulted in peer-reviewed publications in data science and energy-related venues, as well as the deployment of operational analytics systems with measurable real-world impact.

Beyond research, I am actively involved in mentoring and collaborative knowledge sharing, including supervising undergraduate research projects and delivering guest lectures on applied machine learning and data analytics. I am broadly interested in collaborations at the intersection of machine learning, energy systems, and large-scale infrastructure analytics. Further details on my work can be found in my CV and publications listed below.