I am Anthony Faustine a Senior Industrial Analytics Researcher, at Irish Manufacturing (IMR), Dublin, Ireland where I develop computational models, methods, and tools to help industries and manufacturing design more effective solutions. I have experience in applying data science and machine learning techniques to business problems with a track record in the delivery and management of AI projects in academia and industries. 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 Artificial Intelligence technical solutions for business problems.
I am also a PhD research 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. My research focus on developing robust machine learning algorithms for industrial applications with a specific focus on future energy systems.
My research focus on time series analysis and computer vision for real-world application with specific focus on Future Energy Systems, Industrial 4.0, Sustainable development.
- Machine learning and signal processing for time series analysis with a focus on energy management, forecasting and predictive mainatnce.
- Computer vision techniques for machine vision focusing on vison anomaly detection, shape matching and visual inspection.
- Artificial Intelligence and Earth Observations(EO) for suistainability focusing on sustainable energy, agriculture and natural resources management.
- Data management with a focus on specification, strategies, best practises and guideline for machine learning training dataset development.
- 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