The Role of Machine Learning in Decarbonization

The recent progress in digitalization, artificial intelligence, and machine learning has resulted in their adoption in various fields of discipline. These techniques have harnessed information to increase productivity, advance technology, and address various sustainability issues. In this talk, I will discuss how machine learning models can be used to support decision-making to achieve sustainable development goals. In particular, the examples discussed are meant to progress the adoption of clean energy systems and carbon dioxide removal technologies which are essential for SDG 13 (Climate Action).

Biography

Kathleen B. Aviso is a University Fellow and a Professor of the Department of Chemical Engineering at De La Salle University, Manila, Philippines. Her main research interest is the development of decision support tools for environmental decision-making. She earned her Ph.D. degree in Industrial Engineering from De La Salle University. She is the author of more than 250 Scopus-indexed publications with an h-index of 34. She is currently an executive editor for the Journal of Cleaner Production (Elsevier), an associate editor for Digital Chemical Engineering (Elsevier/IChemE) and the South African Journal of Chemical Engineering (Elsevier/IChemE), and part of the editorial board of several other international journals published by Elsevier and Springer Nature. She is the author of the book Input-Output Models for Sustainable Industrial Systems. For her scientific work, Prof. Aviso has received multiple scientific awards from government and professional organizations in the Philippines.