Vivek Mahato is a PhD researcher in I-Form, working in the area of Machine Learning. He obtained his undergraduate degree in Computer Applications from WBUT, India and then went on to complete a master’s in Computer Science with a specialisation in Data Science at UCD. His main research interests are in the areas of data analytics, Machine Learning, and time series analysis, and their practical solutions for the manufacturing sector.
Research Interests (Lay Summary)
The manufacturing industry is experiencing an unfathomable rise of accessible data, and we are witnessing a paradigm shift from basic statistical models to pattern recognition. In the age of Industry 4.0 (the fourth industrial revolution, with a growing trend of utilizing cyber-physical systems in manufacturing), industries are competing against each other by capitalising on the opportunity to use advanced Machine Learning algorithms to make their manufacturing processes smarter, minimizing energy and material usage and maximizing environmental sustainability, health and safety and economic competitiveness.
Vivek’s research focuses on how to bring Machine Learning techniques into the manufacturing domain. He considers a particular category of time-series regression, which is typical in a production environment, but the current literature presents a limitation of readily available tools to address this problem. He intends to utilize data generated during the process of manufacturing to extract possible patterns and anomalies for exploratory analysis. The current state-of-the-art is using agglomerative statistical metrics to summarize the data for the study, but he is motivated to use the raw time-series data directly, for he believes that such summaries discard valuable insights from the data that is required for a cogent analysis or monitoring of the process.