I am Anthony Faustine, a Lead data scientist at the Centre of Intelligence Power (CIP) Eaton, Dublin. I have experience applying data science and machine learning techniques to business problems, with a track record in delivering and managing AI projects in academia and industries. Previously I was a Senior Industrial Analytics Researcher at Irish Manufacturing (IMR), Dublin, Ireland, where I developed data analytics solutions and tools to help industries and manufacturing design more effective solutions.
Between 2012 and 2017, I worked as an assistant lecturer at the University of Dodoma, Tanzania, where I was involved in several research projects within ICT for development (ICT4D). In 2017, I joined IDLab, imec research group of the University of Ghent in Belgium as a Machine learning researcher. From 1st February 2020 to 30th March 2021, I worked as a Data Scientist and Machine learning researcher at CeADAR (UCD), Dublin, Ireland, where I devised and implemented data analytics and AI technical solutions for business problems.
I am also PhD research Fellow at Further Energy and Environment Research Laboratory (FEELab), at ITI/LARSyS, Técnico Lisboa in Portugal under Prof Nuno Jardim Nunes and Dr. Lucas Pereira supervision. I am currently doing a Masters progam; MSc In Leadership, Innovation And Technology offered by TU Dublin (Grangegorman Campus) in partnership with Technology Ireland ICT.
My research focuses on developing robust machine-learning algorithms for real-world industrial applications, focusing on the following key areas.
- Machine learning and signal processing for time series analysis focusing on Energy-disaggregation, Power demand forecasting, Anomaly detection and predictive maintenance.
- Computer vision techniques for machine vision focusing on vision anomaly detection, shape matching and visual inspection.
- Artificial Intelligence and Earth Observations(EO) for sustainability focusing on sustainable energy, agriculture and natural resources management.
- Data management focuses on specification, strategies, best practices and guidelines for AI dataset development.
I also maintain a repository with resources on learning and applying AI for practical problems.
- FPSeq2QFPSeq2Q: Fully Parameterized Sequence to Quantile Regression for Net-Load Forecasting with Uncertainty EstimatesIEEE Transactions on Smart Grid 2022
- AWRGNILMAdaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load MonitoringIEEE Transactions on Smart Grid 2021