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.

Research area

  1. Machine learning and signal processing for time series analysis with a focus on energy management, forecasting and predictive mainatnce.
  2. Computer vision techniques for machine vision focusing on vison anomaly detection, shape matching and visual inspection.
  3. Artificial Intelligence and Earth Observations(EO) for suistainability focusing on sustainable energy, agriculture and natural resources management.
  4. Data management with a focus on specification, strategies, best practises and guideline for machine learning training dataset development.

selected publications

  1. FPSeq2Q
    FPSeq2Q: Fully Parameterized Sequence to Quantile Regression for Net-Load Forecasting with Uncertainty Estimates
    Faustine, Anthony, and Pereira, Lucas
    IEEE Transactions on Smart Grid 2022
    Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring
    Faustine, Anthony, Pereira, Lucas, and Klemenjak, Christoph
    IEEE Transactions on Smart Grid 2021