Sambaiga

Hello 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.

Although I am a person who takes the initiative, I enjoy working as part of a team. I possess a strong team-work spirit with experience of working in a highly international environment. I have good critical and creative thinking, problem-solving, leadership, and project management skills with excellent scientific writing and presentation skills.

Research area

Machine learning and signal processing for smart-energy systems with a focus on load monitoring and management, Community Energy and Smart Grid.
Robust deep learning for industrial applications with a focus on a learning algorithms that allows uncertainty quantification, is robust to low data setting and allows self-supervised or semi-supervised learning.
Artificial Intelligence for Industrial 4.0 with focus on preventive maintenance, machine vision and streaming analytics
Artificial Intelligence for Earth Observations(EO) data with a focus 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.

selected publications

AWRGNILM

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

Abs PDF Code

To this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load Monitoring (NILM) research, as researchers and practitioners are forced to invest a considerable amount of time in this task. This paper proposes adaptive weighted recurrence graph blocks (AWRG) for appliance feature representation in event-based NILM. An AWRG block can be combined with traditional deep neural network architectures such as Convolutional Neural Networks for appliance recognition. Our approach transforms one cycle per activation current into an weighted recurrence graph and treats the associated hyper-parameters as learn-able parameters. We evaluate our technique on two energy datasets, the industrial dataset LILACD and the residential PLAID dataset. The outcome of our experiments shows that transforming current waveforms into weighted recurrence graphs provides a better feature representation and thus, improved classification results. It is concluded that our approach can guarantee uniqueness of appliance features, leading to enhanced generalisation abilities when compared to the widely researched V-I image features. Furthermore, we show that the initialisation parameters of the AWRG’s have a significant impact on the performance and training convergence.